# **Advances in Molecular Genetics of Brain Tumors**

### Edited by

## Nives Pećina-Šlaus and Ivana Jovčevska

Printed Edition of the Special Issue Published in *International Journal of Molecular Sciences*

www.mdpi.com/journal/ijms

## **Advances in Molecular Genetics of Brain Tumors**

## **Advances in Molecular Genetics of Brain Tumors**

Editors

**Nives Pe´cina-Slaus ˇ Ivana Jovˇcevska**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Nives Pecina- ´ Slaus ˇ Department of Biology and Croatian Institute for Brain Research, School of Medicine University of Zagreb Croatia

Ivana Jovcevska ˇ Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana Slovenia

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *International Journal of Molecular Sciences* (ISSN 1422-0067) (available at: https://www.mdpi.com/ journal/ijms/special issues/Molecular Genetics Brain Tumors).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-6644-3 (Hbk) ISBN 978-3-0365-6645-0 (PDF)**

Cover image courtesy of Ivana Jovcevska ˇ glioblastoma stem cells

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.

## **Contents**

A Novel Role of BIRC3 in Stemness Reprogramming of Glioblastoma Reprinted from: *Int. J. Mol. Sci.* **2021**, *23*, 297, doi:10.3390/ijms23010297 ............... **127**

## **About the Editors**

#### **Nives Pe´cina-Slaus ˇ**

Nives Pecina- ´ Slaus is a tenured professor at the Department of biology and Head of the ˇ Laboratory of Neuro-oncology Croatian Institute for Brain Research at the School of Medicine of the University of Zagreb. She graduated in 1990. and obtained M.S. in 1992. from the Faculty of Sciences, and her Ph.D. in the field of molecular oncology in 1998 from the School of Medicine at the University of Zagreb. She was trained at Cold Spring Harbor Laboratory, New York, and at Georgetown University, Washington DC, USA. She was granted as principal investigator for six scientific projects and is/was a collaborator on three international projects funded by EU. Her research has led to the publication of more than 100 publications—75 scientific papers, a book, abstracts, 7 book chapters and many professional papers.  She was an invited lecturer in 29 scientific meetings. She organized several scientific workshops and often participated in the population of science. She was cited 2251 in Google Scholar (H index 24), 1130 times in Web of Science (H index 17), and 1358 in Scopus (H index 19). She acts as a reviewer for many scientific projects and indexed journals and is an editorial board member of Cancer Cell International, Frontiers in Bioscience, Acta Clinica Croatica, the Croatian Medical Journal in 2011. In 2021, she edited the Special Issue "Advances in Molecular Genetics of Brain Tumors" in *IJMS* (ISSN 1422-0067). She teaches medical biology and many doctoral courses for medical and dentistry students and was a mentor on numerous theses (29 defended theses) including seven papers rewarded by Rector's award. She is a member of professional associations and councils (European Society for Human Genetics, European Association for Cancer Research, Croatian Society for Neuroscience, Council for neuroscience and brain disease CASA (HAZU), Council of School of Medicine at the University of Zagreb). She was awarded four scientific awards by the Croatian Medical Association and the Academy of Medical Sciences in 2011. she received National Science Award for outstanding scientific research from the Ministry of Science, Education and Sports and the award for publication activity from the Medical School University of Zagreb.

#### **Ivana Jovˇcevska**

Ivana Jovcevska is a researcher at the Faculty of Medicine, University of Ljubljana in Ljubljana, ˇ Slovenia. She was born and raised in Skopje, R. N. Macedonia, where at the Ss Ss Cyril and Methodius University she finished her Bachelor's studies in analytical biochemistry in 2011. Since October 2011 she lives in Ljubljana, Slovenia, where she completed her PhD studies in biochemistry and molecular biology, and was awarded a doctoral degree in 2015. She was trained at the Vrije Universiteit Brussel in Brussels, Belgium and Max Delbruck Center for Molecular Medicine (MDC) in Berlin, Germany. ¨ Her main fields of research are molecular changes in glioblastoma, proteomics, nanobodies, and gliomagenesis. So far, during her research career, she has been granted one international CELSA grant, as well as one post-doctoral grant and two smaller bilateral projects from SRA. She was also awarded several scholarships in the course of her Bachelor's and PhD studies, and her post-doctoral training. She has also cooperated on two Interreg projects. Her research led to the publication of 24 papers, a book chapter and a review in Slovenian. She was an invited lecturer at two scientific meetings, organized the one-day conference with international participation "Brain tumors: From bench to clinic" in 2019, and is constantly working on the popularization of science. Her research has been cited over 300 times, her H index is 11. She acts as a reviewer for many peer-reviewed journals. In 2021, she edited the Special Issue "Advances in Molecular Genetics of Brain Tumors" for the *International Journal of Molecular Sciences* (ISSN 1422-0067). She has one elective course in the PhD programme Biomedicine at the UL Faculty of Medicine and is currently mentoring two PhD students and one MSc student. She is a member of FEBS and the Slovenian Biochemical Society, the latter awarded her the Lapanje recognition for extraordinary achievements in the field of biochemical sciences in 2022. The award is given to young members for significant contributions to the development of biochemical sciences in the Slovenian and international research society.

## **Preface to "Advances in Molecular Genetics of Brain Tumors"**

It is our great pleasure and joy to print the Special Issue "Advances in Molecular Genetics of Brain Tumors" in the form of a book. When planning this Special Issue, our aim was to provide readers with studies dealing with different aspects of brain tumors. Since the Special Issue contains manuscripts covering various features of brain tumors, we believe we have shown how they fit together and complement each other to accelerate the design and development of more promising diagnostic and treatment methods. The result, we hope, is a solid set of manuscripts that contribute to filling in the gap of knowledge in the biology of brain tumors.

The work that our authors, the *IJMS* editorial office and ourselves conducted during the past two years will hopefully provide valuable information for the scientific community on the molecular characteristics of brain tumors and their prospective treatment options. This unique editing experience during the work on the interesting papers that we received, provided us with valuable knowledge and we learned a great deal about novel approaches and ideas behind the formidable studies. We would like to thank and acknowledge the contributing authors for their great research studies and literature reviews. Our heartfelt thanks to Mrs Clytie Yin and the *IJMS* editorial office for encouraging us to edit the Special Issue and providing great assistance in the making of this project.

As guest editors, we are very excited about the book coming to fruition and we sincerely hope that the readers are going to find the book useful and interesting.

> **Nives Pe´cina-Slaus and Ivana Jovˇ ˇ cevska** *Editors*

## *Editorial* **Different Approaches to Study Molecular Blueprint and Biological Behavior of Brain Tumors: Editorial to the Special Issue "Advances in Molecular Genetics of Brain Tumors"**

**Nives Pe´cina-Šlaus 1,2,\* and Ivana Jovˇcevska 3**

Cancer remains one of the leading causes of mortality worldwide. In this context, brain tumors are also characterized by poor prognosis and short survival [1,2]. Brain tumors demonstrate the great heterogeneity and inherent molecular variability of tumor cells even within a specific subtype. Therefore, for precise diagnosis and successful treatment it is important to understand their biology, which, at this point, remains largely unexplained. Understanding, preventing, and treating brain tumors are among the most important ongoing challenges of neuro-oncology and a crucial public health issue.

The aim of the Special Issue "Advances in Molecular Genetics of Brain Tumors" was to present a collection of selected relevant papers in the rapidly expanding field of brain tumor genetics. The topics covered molecular genetics, signaling pathways governing the mechanisms of tumor formation and invasion, DNA methylation, predictive biomarkers, diagnostic improvement, potential therapeutic targets, metastasis and recurrence, as well as the difference in the therapeutic response to specific clinical subtypes. In this issue, we published a total of 14 papers, particularly 10 novel innovative studies on the genetics of brain tumors, as well as 1 communication and 3 reviews providing perspectives that can shed light on novel research directions. The contributing authors report a range of different and versatile molecular studies, but also offer insight into the state-of-the-art progress in several critical reviews.

The original work on the genetics of brain tumors consists of diverse studies ranging from profiling of glial cancers pathway-related genes [3] on one end, to investigating the migratory properties of glioblastoma cells [4] and proteins involved in the process [5], and identification of proteins crucial for stemness and self-renewal of glioblastoma cells [6] on another end. In more detail, in the comparative study by Majercikova et al. [3] the authors performed transcriptomic profiling of cancer pathway-related genes by combining the PCA method and multi-criteria decision making in the analysis of gene expression. They observed changes in the expression of 26 genes compared to the average expression value of three different controls belonging to various pathways including cellular senescence, metabolism, angiogenesis, apoptosis, DNA damage and repair, epithelial to mesenchymal transition, and telomeres and telomerase. Migratory glioma cell properties were examined in two studies: one by Odrzywolski et al. [4] analyzed the correlation of doublecortin (DCX) expression and glioblastoma cell migration using immunohistochemistry and singlecell RNA-seq. Along with DCX, the analysis also included the expression patterns of Nestin (NES) and Oligodendrocyte lineage transcription factor 2 (OLIG2). The findings indicated that there was a set proportion of cells expressing DCX/NES/OLIG2, regardless of treatment, guided by tumor plasticity.

**Citation:** Pe´cina-Šlaus, N.; Jovˇcevska, I. Different Approaches to Study Molecular Blueprint and Biological Behavior of Brain Tumors: Editorial to the Special Issue "Advances in Molecular Genetics of Brain Tumors". *Int. J. Mol. Sci.* **2023**, *24*, 948. https:// doi.org/10.3390/ijms24020948

Received: 18 November 2022 Revised: 18 December 2022 Accepted: 23 December 2022 Published: 4 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The second study on migratory properties by Toedebusch and colleagues [5] is about the involvement of microglia-derived olfactomedin-like 3 (Olfml3) in glioma progression. Toedebusch et al. performed CRISPR-Cas9-mediated Olfml3 gene editing in N9 cells and clearly demonstrated that this extracellular matrix protein promoted glioma cell migration and invasion. Other essential mechanisms examined were stemness and self-renewal, as reported by Wu and coauthors [6]. In their study, the authors reported that the expression of BIRC3, a member of the IAP family of proteins that inhibit apoptosis, promotes glioblastoma stemness and tumorigenicity of glioma stem cells (GSCs) through inactivation of BMP4 signaling pathway. Elucidating the mechanisms of GBM stemness reprogramming and adaptation, which is believed to be a primary cause of therapeutic failures, is very important for advancing our understanding of glioblastoma resistance to therapy (Figure 1).

**Figure 1.** BIRC3 influences tumor initiation and progression in GBM orthotopic xenograft model. From paper by Wu et al., [6]. (**A**). Horizontal axial MRI scan of mouse brain tumors 4 weeks after implantation. Two of BIRC3-OE mice were already dead at 4 weeks. (**B**). Tumor size calculation from MRI scan. *n* = 5. (**C**). Kaplan-Meier survival curve of U251 control BIRC3-OE and BIRC3-KO intracranial injection mice. *n* = 5 mice/group. (**D**). Mice were sacrificed at different timepoints and brain tissues of U251 control and BIRC3-OE groups were fixed in 10% neutral formalin. H&E staining and BIRC3 immunohistochemistry was performed as described in the Material and Methods Section 4. Five mice were included in this histological study and similar results were observed in each animal. (**E**). When mice were sacrificed, part of tumor tissues were isolated. mRNA from tumor tissues were extracted. BMP4, CD133 and ABCG2 mRNA expression analyzed by real-time PCR in extracted tumor tissues. *n* = 3, \* *p* < 0.05.

Moreover, studies also reported tumorigenesis mechanisms controlled by the circadian gene cry [7]. Circadian rhythm regulation [8] has only recently been introduced in cancer research. However, the deregulation of circadian genes in tumors has been recognized as important. Using an in-house *Drosophila* model, Jarabo et al. reported a novel role of the light-regulated protein Cry that acts as a core component of the circadian clock. The model uses the system of co-activation of EGFR and PI3K signaling pathways in Drosophila glial cells.

The mechanisms affected by temozolomide in pediatric glioblastoma was investigated by Damanskiene et al. [ ˙ 9]. The authors tested the differences in efficacy of temozolomide doses between PBT24 and SF8628 cell lines of high-grade pediatric glioblastoma xenografts in a chicken chorioallantoic membrane (CAM) model. The study stressed the importance of personalized therapy for glioblastoma, emphasizing that it should be specifically tailored to the pediatric population.

The importance of metabolic dysregulation has long been recognized as a driving force of cancer, particularly glioblastoma. In their contribution, Franceschi et al. addressed a very important aspect of glioblastoma progression: the involvement of a metabolic pathway parallel to glycolysis, specifically the pentose phosphate pathway (PPP), which plays a critical role in sustaining cancer-cell survival and growth [10]. The authors evaluated the role of sedoheptulose kinase (SHPK), an enzyme involved in the nonoxidative arm of the PPP, by conducting a functional enrichment analysis using microarray data on SHPK expression in glioblastoma patients. Further, they evaluated the effects of SHPK overexpression in three different glioblastoma cell lines. Their results showed that the increased SHPK expression was significantly correlated with a worse glioblastoma prognosis.

Moreover, two studies tackled RNA interference in glioma. Namely, the need to identify miRNAs as specific non-invasive biomarkers for the prognosis of glioma is highlighted in the work of Levallet et al. [11]. The authors reported that the expression level of a panel of seven pro-angiogenic and/or pro-hypoxic miRNAs (has-miR-200b-3p, -200c-3p, -210-3p, -126-5p, -221-3p, -424-5p, and -451-5p) was affected in patients with glioma and related to the glioma histology grade. The results suggest that pro-angiogenic and/or pro-hypoxic miRNAs can be used as tools for monitoring patients, specifically with IDH-mutated lowgrade tumors, since they are easily measurable in plasma. In a different study, Clausing et al. [12] examined the effect of IDH1R132H mutation on the redox system in a CRISPR/Cas edited glioblastoma model and compared them with IDH1 wild-type (IDH1wt) cells. This model is suitable for portrait IDH1R132H-dependent alterations in tumor cell metabolism. In the study, they also showed an increase in NAD+ in IDH1R132H glioblastoma cells compared to IDH1wt. Their findings underline the therapeutic potential of targeting the NAD+ synthesis pathway, but authors recommend caution for small-molecule inhibitors.

In the Special Issue, we also included one study about the importance of FET PET/CT scans [13]. With the purpose of treatment improvement, Skoblar Vidmar et al. tested the performance of O-(2-[18F] fluoroethyl-)-L-tyrosine (18F-FET) PET for the differentiation between glioma patients based on IDH mutational status. The enzyme isocitrate dehidrogenase (IDH) is a biomarker that improves diagnostic accuracy, but also influences the response and the course of treatment, and thus, overall survival [14]. This is the first reported study that assessed the diagnostic performance of different 18F-FET PET segmentation approaches for differentiation between treatment-related changes (TRC) and true progression (TP). The neuro-oncological therapy can lead to the development of TRC that mimic TP and distinguishing TRCs from TP in treated patients remains a challenge in glioma cases, since both share similar clinical symptoms and imaging characteristics [14]. In their contribution, Skoblar Vidmar and co-authors stressed the importance of molecular biomarkers that have clinicopathologic utilities.

One study focused on a different group of most common primary brain tumors i.e., meningiomas [15]. Bukovac and coauthors investigated the role of DVL1 that is the central mediator of Wnt signaling pathway. The results revealed that the central PDZ region of DVL1 gene harbored frequent mutations. The study further showed that the samples containing mutations in the PDZ domain expressed significantly less DVL1 protein and that the nuclear expression of DVL1 was significantly correlated with a higher expression of active β-catenin (*p* = 0.029) and a higher meningioma grades (*p* = 0.030). Their genomic instability, sequencing and immunohistochemistry results indicate that Wnt signaling is activated in meningioma and that DVL1 could potentially represent a good biomarker for meningioma progression.

To obtain a broader view of the field, we published three review papers that provided comprehensive and thoroughly updated critical standpoints on several topics such as targeted therapies for vestibular schwannomas [16], as well as molecular biomarkers [17] and precision oncology for complex diseases such as glioblastoma [18]. Tamua and Toda prepared an in-depth update of the currently available knowledge on the molecular biology of vestibular schwannomas and its relevance to treatment. The importance of the tumor microenvironment, inflammation, and stress reaction in the development and progression of vestibular schwannomas is also critically surveyed. The review discusses a range of therapeutic approaches, from surgery and radiation therapy to gene therapy. Finally, the authors indicate that tumor-microenvironment-targeted therapy may also be supportive and recommend multimodal therapy for patients with refractory vestibular schwannomas. Next, Sareen et al. [17] conducted a systematic review and performed meta-analysis of key molecular biomarkers that have been investigated for their predictive value in recent glioblastoma clinical trials. Analysis of the prognostic significance of IDH1 mutation showed significantly better overall survival in patients with IDH1 mutation. Meta-analysis including 575 glioblastoma patients presenting with either amplification or high expression of EGFR gene did not reveal prognostic significance, which the authors contributed to limited patient numbers; they recommended more homogeneous studies on larger patient cohorts. Lastly, Panovska and De Smet [18] surveyed the current knowledge on therapy approaches for complex and heterogeneous diseases such as glioblastoma that display formidable inter- and intra-tumoral heterogeneity. The authors appeal on personalized and distinct therapeutic approaches in order to achieve clinical benefits since the clinical trials of the past 20 years have failed to improve the outcome for the vast majority of glioblastoma patients. The review highlights the need for procedures that can precisely select the appropriate patients who could benefit from the given therapy, but also to the drug sensitivity of specific tumor cells of a particular patient. The authors discuss the current state of the art of transforming technologies, tools and challenges for functional precision oncology, and conclude that that the personalization of cancer medicine is the way to tackle this disease.

In conclusion, in order to better understand the biology of brain tumors and ultimately improve diagnostic and therapeutic approaches, this Special Issue intends to elucidate the diversity of problems behind brain tumors and their potential solutions. This is also nicely illustrated with the diversity of methodologies used in the papers published in our Special Issue. As brain tumor incidence increases with age, with the growing numbers of elderly population, the number of patients is expected to rise. Many cases of brain tumors are characterized with discouraging prognosis. The treatment resistance and the possibility of recurrence is the cause of poor survival, especially for the glioma branch [19].

To improve patient outcome, numerous novel ideas and alternative approaches are constantly being explored by the research community. Molecular testing in modern cancer diagnostics, combined with the development of personalized therapies, is the main avenue for the successful outcomes and progress. However, there is still room for improvement, which demands that we search for novel crucial molecular players and development of new concepts about the initiation and progression of brain tumors. As reported in this Special Issue, brain tumors are investigated on various cellular levels with the ultimate goal of greater patient benefit. We hope that the themes and fields covered in this Special Issue will attract readers from the broader scientific community, contribute to expanding our knowledge about the biology of brain tumors, and inspire further studies that will improve the diagnosis and clinical management of brain tumors.

Lastly, as Guest Editors, we would like to gratefully acknowledge the input of all the authors of both original research articles and review papers, and their contribution to this relevant and valuable research topic.

**Author Contributions:** Conceptualization, writing—original draft preparation, writing—review and editing N.P.-Š. and I.J. All authors have read and agreed to the published version of the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Article* **Different Approaches for the Profiling of Cancer Pathway-Related Genes in Glioblastoma Cells**

**Zuzana Majercikova 1, Katarina Dibdiakova 1, Michal Gala 2, Denis Horvath 3, Radovan Murin 1, Gabriel Zoldak 3 and Jozef Hatok 1,\***

**Abstract:** Deregulation of signalling pathways that regulate cell growth, survival, metabolism, and migration can frequently lead to the progression of cancer.  The present study aimed to characterise the expression of cancer pathway-related genes (*n* = 84) in glial tumour cell lines (A172, SW1088, and T98G). The transcriptomic data obtained by the qRT-PCR method were compared to different control groups, and the most appropriate control for subsequent interpretation of the obtained results was chosen. We analysed three widely used control groups (non-glioma cells) in glioblastoma research: Human Dermal Fibroblasts (HDFa), Normal Human Astrocytes (NHA), and commercially available mRNAs extracted from healthy human brain tissues (hRNA). The gene expression profiles of individual glioblastoma cell lines may vary due to the selection of a different control group to correlate with. Moreover, we present the original multicriterial decision making (MCDM) for the possible characterization of gene expression profiles. We observed deregulation of 75 genes out of 78 tested in the A172 cell line, while T98G and SW1088 cells exhibited changes in 72 genes. By comparing the delta cycle threshold value of the tumour groups to the mean value of the three controls, only changes in the expression of 26 genes belonging to the following pathways were identified: angiogenesis *FGF2*; apoptosis *APAF1*, *CFLAR*, *XIAP*; cellular senescence *BM1*, *ETS2*, *IGFBP5*, *IGFBP7*, *SOD1*, *TBX2*; DNA damage and repair *ERCC5*, *PPP1R15A*; epithelial to mesenchymal transition *SNAI3*, *SOX10*; hypoxia *ADM*, *ARNT*, *LDHA*; metabolism *ATP5A1*, *COX5A*, *CPT2*, *PFKL*, *UQCRFS1*; telomeres and telomerase *PINX1*, *TINF2*, *TNKS*, and *TNKS2*. We identified a human astrocyte cell line and normal human brain tissue as the appropriate control group for an in vitro model, despite the small sample size. A different method of assessing gene expression levels produced the same disparities, highlighting the need for caution when interpreting the accuracy of tumorigenesis markers.

**Keywords:** glioblastoma; cancer pathway; mRNA; multicriterial analysis

#### **1. Introduction**

Glioblastoma (GBM) is one of the most prevalent primary malignant brain tumours in adults, as classified by the World Health Organization (WHO). According to histopathological and molecular characteristics, glioblastoma is classified into four grades (I-IV) [1]. Despite aggressive multimodal therapy consisting of surgical resection, radiation, and chemotherapy with the alkylating agent temozolomide, the prognosis remains dismal, with a median overall survival of 12–15 months after diagnosis [2,3]. The intensive study of malignant gliomas over the past three decades has determined several molecular hallmarks that have enhanced classification and therapeutical strategies. As with all other types of

**Citation:** Majercikova, Z.; Dibdiakova, K.; Gala, M.; Horvath, D.; Murin, R.; Zoldak, G.; Hatok, J. Different Approaches for the Profiling of Cancer Pathway-Related Genes in Glioblastoma Cells. *Int. J. Mol. Sci.* **2022**, *23*, 10883. https:// doi.org/10.3390/ijms231810883

Academic Editors: Ivana Jovˇcevska and Nives Pe´cina-Šlaus

Received: 5 August 2022 Accepted: 13 September 2022 Published: 17 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

brain tumours, glioblastomas have a heterogeneous character. The result was the introduction of the most recent WHO classification of central nervous system tumours in 2021 [4,5]. In addition to the histological approach and somatic mutations, gene expression signatures contribute significantly to the overall classification of glioblastoma subtypes [6]. Currently, successful treatment response and outcome predictions for patients with GBM are made using next generation sequencing analyses that account for intratumoral heterogeneity [7]. The determination of *O*6-*methylguanine-DNA methyltransferase* (MGMT), *isocitrate dehydrogenase* (IDH), *tumour protein p53* (TP53), *platelet-derived growth factor receptor alpha* (PDGFRA), or *epidermal growth factor receptor* (EGFR) gene expression levels, as well as the presence of their mutations and methylation status, are important prognostic factors [8–11]. One of the most commonly used methods in the aforementioned molecular analysis is real-time PCR, indicating a clear application with minimal errors. The all real-time PCR techniques (classical method based on dyes, TaqMan probes assay, microarray, RNA sequencing analysis, etc.) are still highly quantitative and sensitive methods for the detection of gene expression levels but are generally best for examining a relatively small number of transcripts in a large set of samples. One of the few disadvantages is the necessity of involving a biostatistics expert in the evaluation process. However, with proper optimisation, we may encounter erroneous analysis and, subsequently, variable interpretations of results. Properly chosen biostatistical methods also clearly contribute to successful analysis. Cell models have also been used to characterise the mechanisms underlying glioblastoma formation [12].

In the present study, we examined the transcriptomic profiles of glial cancer cell lines: A172, T98G, and SW1088. We focused on the detection of representative cancer genes (Human Cancer PathwayFinderTM PCR Array), which were divided into nine pathways: angiogenesis, apoptosis, cell cycle, cellular senescence, DNA damage and repair, epithelialto-mesenchymal transition, hypoxia signalling, metabolism, and telomeres and telomerase (Table 1). This study's primary objective is based on a different approach for selecting the control group and, consequently, for interpreting the results. We analysed three different control groups (non-glioma cells) widely used in glioblastoma research: Human Dermal Fibroblasts (HDFa), Normal Human Astrocytes (NHA), and commercially available mRNAs acquired from healthy human brain tissues (hRNA). Using real-time PCR analysis, the gene expression profiles of glioblastoma cell lines differ when compared to a control group based on correlational differences. Our comprehensive approach, which incorporates numerous statistical analyses, contributes to the most accurate interpretation of the results.

**Table 1.** List of analysed genes and their assignment to cellular pathways.

#### **Table 1.** *Cont*.

#### **2. Results**

We achieved the results on human RNA isolated from cell lines (tumour and nontumour) and normal brain tissues. The expression of cancer pathway-related genes at the mRNA level was compared among tumour and control group of samples by the realtime qPCR. Out of the total number of monitored genes (*n* = 84) in the Human Cancer

PathwayFinderTM PCR Array, we were able to detect all of them, and only in the control group was there not a gene for the *Fas ligand* in any of the samples. In the next sections, we describe the relationship between the groups of samples for individual genes as well as the unique multicriteria analysis used for the correct interpretation of the results.

#### *2.1. Relative mRNA Ratio of Control Cells*

The selection of an appropriate control sample is a crucial initial step for group qRT-PCR analyses. On three separate controls (NHA, hRNA, and HDFa) relative mRNA gene levels associated with cancer development were detected. The Normal Human Astrocyte cell line was characterised by a significantly higher Ct ratio in the following genes: *CDH2*, *MKI67*, *LDH2*, *IGFBP7*, and *IGFBP5* (Figure 1). The *Keratin 14* gene was amplified in the NHA line exclusively. Only *Angiopoietin 1* exhibited a significantly lower mRNA ratio in NHA compared to both control groups (hRNA and HDFa). We demonstrated that there is no amplification of *AURKA*, *ANGPT2*, *FASLG*, or *GSC* products (Figure 1). *Goosecoid homeobox* gene amplification was only detected in Human Dermal Fibroblasts among the control samples. Compared to NHA and hRNA samples, the HDFa cell line exhibited overexpression of a single gene, namely *Adrenomedullin*. In contrast to the other controls, most genes were found to have lower levels. As undetected mRNA, *AURKA*, *FASLG*, *KRT14*, and *SNAI3* were considered (Figure 1a).

The total human RNA (hRNA) control sample consists of total RNAs from healthy brain donors that are commercially available. We found only hRNA control to contain the *AURKA* gene. *ANGPT2*, *CASP9*, *E2F4*, *ETS2*, *FLT1*, *GPD2*, *LPL*, *MAP2K1*, *SERPINF1*, *SNAI3*, *SOD1*, *SOX10*, *STMN1*, *TEP1*, *TERF1*, *TERF2IP*, and *TINF2* exhibited significant overexpression. In contrast, mRNA levels for *IGFBP3*, *SNAI2*, and *WEE1* were significantly higher compared to NHA and HDFa. In addition, the amplification of mRNA for Desmoplakin, Fas ligand, Goosecoid homeobox, Keratin 14, and vascular endothelial growth factor C was not detectable (Figure 1a).

#### *2.2. Relative mRNA Ratio in Tumour Cell Lines*

Results of the relative mRNA expression ratio comparison between A172, SW1088, and T98G cell lines are shown in Figure 1b. Similar cancer-related mRNA ratios were found in all tumour cell lines for 60 out of 84 genes (71.4%). The genes *CDH2*, *DDB2*, *DSP*, *EPO*, *FGF2*, and *TEK* were overexpressed in an astrocytoma cell line. In addition, the expression of *Cyclin D2*, *kinase insert domain receptor*, and *Keratin 14* genes was present exclusively in the A172 line. In contrast, we found a significantly lower mRNA ratio for *ACSL4*, *ADM*, *ANGPT1*, and *SNAI2*. Only the mRNA for the *Fas ligand* was not detected. Except for *CCND2*, *FLT1*, *FOXC2*, *KDR*, *KRT14*, and *TEK*, the mRNA expression ratio of cancer pathway-associated genes in the astrocytoma grade III cell line (SW1088) was comparable to that of other tumour cell lines (Figure 1b).

In the glioblastoma cell line (T98G), we detected the overexpression of the Cyclin D3 transcript. Significantly lower mRNA levels were detected for *ANGPT2*, *DSP*, *KDR*, *KRT14*, and *LPL*. Four genes (*CCND2*, *DSP*, *FASLG*, and *PGF*) lacked fluorescent signals.

#### *2.3. Data Preparation before Determination of Gene Expression Level*

The selection of the appropriate control is crucial when comparing mRNA levels between groups using the ΔCt method and qRT-PCR analysis. Therefore, with respect to the control group, the genes of the tested group (tumour) sought may appear to be inaccurate. Here, we used the ΔCt method by comparing the relative expression of tumour genes to that of three control genes separately to identify the validity of the results. We used a combination of several mathematical and statistical methods to prepare gene expression data.

**Figure 1.** The mean of Ct ratio of mRNA levels. (**a**) The mRNA expression levels in control group. (**b**) The mRNA expression levels in group of tumour cell lines. Ct ratio, gene of interest/housekeeping genes. Each mRNA level included three replicates.

#### 2.3.1. Principal Component Analysis

Principal component analysis (PCA) was utilised to examine the variability of gene expression profiles among various samples (see Material and Methods). Figure 2 shows the first two components, PC1 vs. PC2 as a scatter plot, with a variance of 41.2% for PC1 and 25.8% for PC2, respectively. As seen in the scatter plot, the gene profiles corresponding to tumour tissues (red circles) are closer together than the gene profiles of controls. From an overall perspective, tumour lines can be defined by a putative cluster, whereas other data are more dispersed. Expression profiles of controls demonstrate a wide variation, indicating their distinct and cell-line-specific expression. Regarding variability within the same sample–experimental replicas, only small variations are visible, and all replicas can be attributed to a given cell line.

**Figure 2.** Scatter plot of principal component analysis. The grey elliptical line represents data of tumour cell lines, dots outside the line are control samples. All values of each sample were measured in triplicates.

#### 2.3.2. Correlations between Gene Expressions

In the previous analysis, we discovered that LDA can identify and pinpoint the differences in individual genes and distinguish between the classes of control cell lines and tumour samples. Subsequently, we have decided to analyse the relationships between gene expressions in greater detail. By calculating the Pearson correlation coefficient, we focused on the relationship between the expressions of individual genes. In control cells, a general gene regulation network controls gene expression. Therefore, significant positive and negative correlations between gene expressions will result from the regulation network. Gene correlations can be lost if the regulatory network is dysregulated, such as in tumour cells. Figure 3a depicts a correlation between the genes in the control samples and the genes in the tumour samples. In control samples, the average correlation is low (0.33), but there are substantial differences between the individual genes. For example, *TERF1* and *TERF2IPa* genes had the highest correlation of 0.999, and *SNAI2* and *CASP9* genes had the highest anti-correlation of −0.996. In tumour lines, the average correlation is even lower, 0.031, indicating a significant loss of coordinated gene expressions. Given that the overall expression profiles of individual genes in tumour cells are more similar (dots in red elliptical line, Figure 2) than in control cells, which appeared more heterogeneously in the PCA plot, this may be a surprising result. The difference between PCA and gene–gene correlation is that PCA describes the variance between full profiles, whereas gene–gene correlation provides information about possible causal relations between expression values. In tumour cell lines, the relationship between the *SNAI1* and *SNAI2* genes is the highest at 0.9999, while the *LPL* and *IGFBP5* genes have the highest anti-correlation (−0.997). The descending arrangement of the gene correlation (Figure 3b) indicates that tumour samples have lower overall values than control samples.

**Figure 3.** Pearson correlation of individual genes expressions of control (**a**) and tumour (**b**) cell lines.

Kernel density estimation revealed a different relationship between gene expressions (Figure 4). The distribution of gene expression correlations deviates from the Gaussian distribution for both control and tumour cell lines. In contrast to tumours, the peak density of control samples is centred around the value of 0.95. Maximum density in the positive correlation range is roughly in the same place as in control cell lines, but the amplitude is slightly lower, indicating that some gene–gene correlations have changed. The missing positive gene–gene correlations have become negative in tumour cell lines. The density analysis of the control vs. tumour correlation values approaches 0, as shown in Figure 4.

**Figure 4.** Statistical analysis of gene expression correlations. (**a**) Descending order of correlations. (**b**) Kernel density of correlations. The green line represents data from control, red line from tumour and grey line is the mix of both groups.

In the next step, we calculated Pearson correlations between gene expression pro-files of analysed cell lines (Figure 5). First, the correlation of gene expression profiles within the control group and within the tumour group was calculated. We found that the average Pearson correlation coefficient among the control group is 0.71, which is slightly lower than the average correlation between tumour cells at 0.84. When we pairwise correlated the expression profiles of control and tumour cells, we arrived at the average correlation coefficient of 0.56. Some of the gene expression profiles of control vs. tumour cells showed higher correlation coefficients than profiles of controls-only or tumour-only. For example, the profiles of HDFa\_3 versus the expression profile of hRNA\_1 control shows a correlation coefficient of 0.47. Conversely, the expression profile of HDFa\_3 versus expression profile of SW1088\_3 from the tumour group shows the correlation coefficient of 0.72.

**Figure 5.** Pearson correlations between gene expression profiles of analysed cell lines. (**a**) Correlation of gene expression profiles within control group, (**b**) within tumour group and (**c**) between samples of control and tumour group.

#### *2.4. Fold Change of Cancer Pathway-Related Genes*

Figure 6 shows the fold regulation data obtained by correlating cancer cell lines (A172, SW1088, and T98G) with three distinct control groups (HDFa, NHA, and hRNA). Fold downregulation is represented by blue values, while fold upregulation is represented by red values. Changes in regulation that do not reach significance are highlighted in grey. Not available values (N/A) representing changes in expression are marked in white.

When comparing the expression of genes associated with angiogenesis, we obtained heterogeneous results (Figure 6). The significantly higher expression of *ANGPT1* was detected in the T98G glioblastoma cell line (2.20 compared to HDFa; 2.48 compared to hRNA; 27.98 compared to NHA; and 5.48 compared to all controls) and the SW1088 cell line compared to the NHA control (16.96). In contrast, data analysis of Angiopoietin 1 in A172 revealed a statistically significant decrease in expression levels relative to HDFa and hRNA controls. At the same time, the expression of the Angiopoietin 1 antagonist coding gene, *ANGPT2*, was found to be opposite in comparisons between SW1088 and a combination of controls (5.35). The fold regulation of *ANGPT2* was significantly lower in all tested cell lines compared to control RNAs isolated from the whole brain. In contrast, A172 and SW1088 lines exhibited elevated expression levels (7.57 and 56.2) compared to HDFa control. Compared to control samples, the expression of *CCL2* appeared to be stably increased across tested cell lines, whereas the expression of *FGF2* appeared to be stably decreased. We discovered the reduced expression of *FLT1* and *PGF*. *FLT1* expression was statistically significantly decreased in both glioblastoma cell lines, whereas *PGF* was not detected in glioblastoma cell line T98G (Figure 6).

Among the investigated apoptosis genes, lower expression of *APAF1*, *BIRC3*, *CFLAR*, and *XIAP* was detected in all test groups, with statistical significance and fold regulation varying according to divergent control groups (Figure 6). For instance, in the A172 group, expression of *BIRC3* was three times lower in correlation with NHA than in the hRNA control group. The same pattern was observed when fold regulation values were correlated with HDFa and hRNA. In the astrocytoma cell line, we found that the gene encoding Caspase 2 was upregulated in relation to HDFa control (3.15). *CASP2* deregulation was not significant in either glioblastoma cell line, but it was constantly increasing. In contrast to *CASP2*, the expression of another caspase family protease, *CASP9*, was inconsistently deregulated (Figure 6). CASP9 expression was only found to be negatively regulated in glioma cancer cell lines when compared to the hRNA control group. However, we found that all lines correlated to NHA had relatively high positive fold changes (Figure 6).

Our results in cell cycle-related genes indicate, with a few exceptions, a significant increase in the expression of genes involved in cell division (*CDC20*, *E2F4*, *MCM2*, *MKI67*, and *SKP2*). Notable is the similarity between the expression fold changes of the *CDC20* and *E2F4* genes in the control group (Figure 6). Decreased expression of *CCND3* was prominently identified in SW1088 in terms of hRNA and NHA control groups (−7.52 and −2.45). The expression of *Stathmin* was found to be decreased in all test groups. However, there are differences in fold regulation relative to controls. *WEE1* expression was found to be statistically significant in all glioma cell lines, but only when compared to hRNA controls (4.75, 2.79, 2.24).

We observed decreased expression of *BMI1*, *ETS2*, *IGFBP5*, *IGFBP7*, *SOD1*, and *TBX2* from all investigated genes associated with cellular ageing molecular pathways, but differences in negative fold regulation with respect to control groups were remarkable (Figure 6). For instance, among all glioma cancer cell lines, the fold change of *IGFBP7* expression was decreased approximately 16.5 times more in comparison to the NHA control group than in comparison to the hRNA control and approximately 2.5 times more in comparison to human dermal fibroblasts. To mention the regulation of mitogen-activated kinase's expression in glioblastoma cell lines, *MAP2K1*, *MAP2K3*, and *MAPK14* genes were only upregulated when compared to HDFa and NHA. All three MAP kinases within the tested cell lines were downregulated with varying statistical significance in terms of hRNA control.

**Figure 6.** Heat map of fold change regulation between test and control group. Test group (TG): A172, SW1088 and T98G; Control group (CG): HDFa, hRNA and NHA.

Among the eight investigated genes involved in the response to DNA damage in the A172 group, *DDB2*, *GADD45G*, and *POLB* were significantly upregulated when compared to the HDFa control group (Figure 6). There was downregulation of *LIG4* in glioma cancer cell lines. In A172, the expression of this gene was approximately 4.2 times lower when compared to *LIG4* expression in hRNA control. SW1088 and T98G cell lines exhibit a similar pattern. Despite differences in fold change values due to control group selection, other statistically significant results from Figure 6 were broadly in line with expectations. *SNAI1* and *SNAI2* were downregulated genes related to the EMT pathway that we discovered in all cell lines, but only with regard to NHA control (Figure 6). When compared to HDFa and hRNA control groups, the expression of *SNAI1* and *SNAI2* in SW1088 and T98G lines was increased. In terms of hRNA, all glioma cell lines showed a significant decrease in *SOX10* expression (−4502; −26,634; −19,800), indicating that *SOX10* is highly expressed in our normal human brain control. In comparison to HDFa and NHA controls, *SOX10* appeared to be downregulated. Other detected downregulated genes were *CDH2*, *FOXC2* and *OCLN*. Downregulation of *FOXC2* was present only in the glioblastoma cell line. Fold regulation in SW1088 cell lines was not determined.

Analysis of hypoxia-signalling factors revealed a decrease in expression in the majority of genes, including *SLC2A1*, the gene- encoding Glucose transporter type 1 (Figure 6). This result is statistically significant only in SW1088 (−7.31; −32.3; −12.8) when compared to the hRNA control in the A172 cell line (−3.89). The Erythropoietin-encoding gene *EPO* appears to be exclusively downregulated in SW1088. In contrast, fold change was detected without statistical significance in the other two cell lines (A172) or was not detected at all (T98G).

Most cellular metabolism genes investigated in glioma cell lines (*ATP5A1*, *COX5A*, *CPT2*, *PFKL*, and *UQCRFS1*) were found to be statistically downregulated. *ACSL4* was found to be downregulated in only one of the glioma cell lines, A172. Although *GPD2* and *LPL* were found to be upregulated in A172 and SW1088, with similar fold changes and statistical significance when compared to HDFa, NHA, and a combination of controls, we discovered that both genes were downregulated when compared to the hRNA control group. In contrast, *LPL* expression in T98G was significantly reduced, with the lowest achieved value in comparison to the hRNA control group. *G6PD* upregulation was revealed to be statistically significant only in the SW1088 (5.46; 2.67; 3.34) and T98G (13.7; 6.67; 8.35) cell lines (Figure 6). We also observed increased expression of *ACLY*, but none of these differences was statistically significant.

Of the eight investigated genes involved in telomere maintenance and telomerase regulation, three (*PINX1*, *TNKS*, and *TNKS2*) were found to be downregulated in all glioma cell lines when compared to individual controls (Figure 6). No significant differences in fold changes of *PINX1* expression were found in A172 or SW1088 in comparison to all three negative controls (Figure 6). The fold change in expression in T98G was approximately two times lower (−2.31; −4.74; −3.98). We identified an increase in expression of *TEP1*, *TERF1*, and *TERF2IP* in glioblastoma and astrocytoma cell lines compared to HDFa and NHA control cell lines. The study found no statistically significant changes in *TERF2IP* expression in the T98G cell line. In contrast, we found a statistically significant decrease in *TEP1*, *TERF1*, and *TERF2IP* expression when compared to hRNA control.

#### *2.5. Multi-Criteria Decision of Gene Expression in Sample Correlation*

To determine the power of individual genes in groups, a multi-criteria decision support system was used. As a result, we were able to identify a group of genes in the correlation between controls and tumours.

The alternative method for assessing gene expression is based on separate, two-value quantification of the consequences of over- or underexpression. Not only is knowledge of the data carriers and gene expression important here, but so is the emphasis on the form of the set ordering in sets of triplicates. The results in the plots are represented by a normalised (dimensionless) two-factor form called *R*+, *R*−. Each *R* represents a proportion of the numerator's dissimilarity tendencies. Both measures preferentially account for when there is either an increase (superscript +) or decrease (superscript −) in gene expression in the test subject relative to the control. The denominators in the measures only serve for normalisation because they track intra-group differences in expression. For a robust linear combinatorial representation of stochastic measures, we proposed a data-driven approach inspired by the weighting theory of measures derived from multi-criteria decision support systems theory [11]. The details of our procedures for deriving *R*+, *R*−, as well as the methods of calculation, are difficult to explain concisely; therefore, we provide a detailed description of them in the Supplementary Material.

Figure 7 depicts the resultant correlation of genes (up or down) for individual pathways. As mentioned above, the individual points represent triplicate measurements of the logarithmic values of delta Ct for all samples, correlating the control and tumour groups. Correlation 2D plots of in-plane gene expression (*R*+, *R*−) constructed for tumour cell lines include comparison with controls. A 45-degree line separates the regions of overexpression and underexpression; at this line, overexpression and underexpression are therefore balanced, the scenarios when noise predominates are covered by the (0.1) × (0.1) square. This square region is hence statistically less important. In addition to the shown genes, it is necessary to mention points that far exceeded our proposed trend scale and were therefore deemed irrelevant. We identified the following genes as having decreased expression in tumour lines compared to controls: *FGF2*, *APAF1*, *CFLAR*, *XIAP*, *STMN1*, *WEE1*, *ERCC5*, *LIG4*, *PPP1R15A*, *OCLN*, *SOX10*, *ARNT*, *LDHA*, *ATP5A1*, *COX5A*, *CPT2*, *PFKL*, *UQCRFS1*, *ETS2*, *IGFBP3*, *IGFBP5*, *IGFBP7*, *SOD1*, *TBX2*, *PINX1*, *TINF2*, *TNKS*, and *TNKS2* (out of range). *CCL2*, *CASP2*, *CDC20* (out of range), *E2F4*, *MCM2*, *MKI67*, *SKP2*, *ACLY*, *G6PD*, *GPD2*, *MAPK14*, *DKC1*, and *TERF1* were the genes with increased levels in the tumour lines.

**Figure 7.** Multi-criterial correlation plots of gene expression in tumour cell lines relative to the controls. *R*+ represents a negative proportion of the numerator's dissimilarity tendencies, *R*− positive. A 45-degree line separates the regions of overexpression and underexpression; at this line, overexpression and underexpression are therefore balanced. The scenarios when noise predominates are covered by the (0.1) × (0.1) square. There are three main regions in the graph to categorize according to the level of gene expression with three ways of marking the corresponding points: statistically significant and overexpressed with *R*+ > 1 (red circles), statistically significant underexpressed with *R*− > 1 (blue triangles), finally statistically less significant (black squares) bordered by 0 ≤ *R*+ ≤ 1, 0 ≤ *R*− ≤ 1.

#### **3. Discussion**

Glial tumours are biologically aggressive neoplasms with an abnormally high proliferative capacity and a diffuse invasion pattern. Glioblastoma (grade IV astrocytoma), composed of poorly differentiated neoplastic astrocytes, is the most malignant astrocytic tumour. Based on histopathological and molecular criteria, the WHO grading system categorises gliomas into grades I through IV [4]. Although the majority of neurological tumours derive from the glial lineage, it is unknown whether tumour cells arise from the transformation of an immature precursor or the dedifferentiation of a mature glial cell. Several genetic pathways are involved in the initiation and progression of these neoplasms, especially in the emergence of secondary GBMs.

In our study, we focused on the transcriptomic analysis of genes associated with the cancer pathways in glial tumour cells. As the experimental models, we chose the human glioma cell lines A172 (glioblastoma), SW1088 (astrocytoma), and T98G (glioblastoma). A172 and T98G cell lines are currently the most commonly used glioma cell lines for gene expression analysis. Weller's team performed the first large-scale analysis of 12 glioma cell lines, estimating the profile of 5800 genes. Their cluster and gene expression correlation analysis identified subsets of genes whose expression levels exhibited significant associations with drug sensitivity profiles [13]. Kiseleva et al. identified morphological, surface markers, and several growth factor genes or extracellular matrix genes in the characterisation of both glioblastomas, A172 and T98G [14]. Among the nine tested genes, the expression of *Alpha actin 2* was notably high in both cell lines. In addition, the data revealed a high level of activity of genes encoding major angiogenesis inducers (*VEGF*, *FGF2*, *TGFb1*) and *Thrombospondin-1*. The transcriptomic analysis of SW1088 cells was associated with individual genes or various inhibitory effects [15–17]. In our previous study, we determined the effect of ABT-737 and MIM-1 inhibitors on the mRNA level of apoptosis-associated genes in the T98G cell line [18]. As a control group, human astrocyte (HA) cells were used. Significant changes in apoptotic gene expression were obtained in both cell lines, with the greatest number of altered genes (*n* = 42) occurring in the HA line following MIM-1 treatment. Regardless of the genes involved in determining fold regulation between groups of samples, the choice of control samples will always be decisive. In addition, based on our final multivariate criterion, it is evident that results vary not only according to the choice of control but also according to the evaluation method employed. Therefore, the discussion will centre solely on the genes selected using the MCDA method.

Angiogenesis, as one of the hallmarks of cancer [19], plays a crucial role in glioblastoma growth through oncogene activation and/or downregulation of tumour suppressor genes, resulting in the upregulation of angiogenic pathways [20]. The initial step in the induction of angiogenesis in GBM is the overexpression and secretion of angiogenic growth factors, such as vascular endothelial growth factor (VEGF), followed by their binding to the receptors on epithelial cells [21]. Fibroblast growth factor 2 encoded by the *FGF2* gene is a crucial positive regulator of glioblastoma cell proliferation and survival [22]. However, our results showed a decrease in *FGF2* expression. Although the loss of the *FGF2 receptor* gene is associated with a poor prognosis in glioma patients [23], *FGF2* seems to be persistently expressed because it has been identified as an oncogenic factor in GBM [24], and its expression has been confirmed in other gliomas and meningiomas [25].

Apoptosis refers to a programmed cell death characterised by non-inflammatory cellular fragmentation [26]. It is an essential regulatory mechanism for cell proliferation and death. Intrinsic or extrinsic pathways can initiate apoptosis, with both leading to proteolytic activation of caspases and controlled cell death. Cancer cells have evolved mechanisms to sustain proliferative signalling, thereby sustaining cell growth and avoiding cell death. In the current study, only nine apoptotic genes were included in the array, with *caspase-2* and *caspase-7* being the most expressed in tumour lines. The remaining genes involved in apoptosis regulation were downregulated, including *CFLAR*, *XIAP* and *APAF1*. X-linked inhibitor of apoptosis protein is the most potent and best-defined anti-apoptotic IAP family member that directly counteracts apoptosis by binding to caspase-9 and the

effector caspases-3 and -7 [27]. The X-linked inhibitor of apoptosis protein is abnormally expressed in a variety of human cancers [28,29]. Although this is not evident in brain tissue, Murphy et al. investigated the low levels of XIAP in GBM patients and brain cell lines [30]. The protein encoded by the antiapoptotic gene *CFLAR* is a Caspase-8 and FADD-like apoptosis regulator. By binding to the death receptor, it protects cells from cell death signalling and inhibits receptor-mediated apoptosis [31]. Induction of hypoxia in the A172 glioblastoma cell line results in the expression of *CFLAR* [32]. *CFLAR* expression was detected in glioblastoma tissue samples [33]. Despite these findings, our analysis showed a significant reduction in *CFLAR* expression. Apoptotic peptidase activating factor 1 (APAF1) is a proapoptotic protein that participates in the formation of apoptosomes in response to cell death signals [34]. Overexpression of *APAF1* induced apoptosis in U-373MG human glioma cells [35]. Our previous study on apoptotic gene expression revealed a slight decrease in the expression of *APAF1* in glioblastoma patient samples [36]. In this study, we confirmed our previous findings regarding glioma cell lines. A decrease in *APAF1* expression may lead to apoptosis reduction, thereby favouring cancer cell survival [36].

The main goal of the cell cycle is to ensure accurate DNA replication in the S phase and the final formation of two identical daughter cells in the mitotic phase. The cells use various checkpoints to maintain the optimal progression of the cell cycle, which will slow down or stop the event if necessary [37]. Ki-67, a prognostic and proliferative marker expressed by the *MKI67* gene in cell nuclei during the active phases of the cell cycle (G1-M) with maximum expression at the G2/M phase interface, is used to control the malignant nature of cells [38]. In gliomas, its elevated expression, which increases with malignancy grade, has been well characterised [39]. The absolute highest expression of all genes was observed in *CDC20*. The protein of the same name is responsible for regulating the mitotic phase of the cell cycle. Jeremy Rich's team identified an increased expression of *CDC20* in GBM compared to lower grade gliomas and healthy brain tissue. Their results also indicate the importance of *CDC20* proto-oncogene expression in glioblastoma stem cells, as it plays an essential role in the regulation of proliferation, self-renewal, and survival of these cells [40]. It even contributes to glioma chemoresistance. In accordance with the aforementioned studies, we also identified upregulation of the *CDC20* gene in all monitored groups.

Stathmin is an oncoprotein "18" that is distributed throughout the cytoplasm of cells and regulates microtubule kinetics, thereby affecting cell cycle proliferation and differentiation. Many studies indicate that *STMN1* expression is elevated in glioblastomas [41] and a variety of human cancers [42,43]. Our analyses identified a significant downregulation of *STMN1* in all tumour lines relative to the average control, suggesting reduced cell proliferation and tumour cell migration. The essential cell cycle regulator *WEE1* kinase was similarly underexpressed in tumour cell lines. Its primary function is to stop the progression of the cell cycle at the transition from G2 to the mitotic phase in cells with defectively replicated or damaged DNA [44]. In addition, glioblastoma patients whose *WEE1* expression is upregulated have a shorter survival rate [45,46]. We identified elevated levels of the *WEE1* gene in all tumour lines when compared to human RNA from healthy brain tissues.

The main role of DNA repair mechanisms is to respond to environmental factors that cause DNA damage [47]. These gene mutations can result in a diminished or impaired capacity to repair DNA and an accumulation of damaged DNA, which ultimately increases the risk of cancer. Furthermore, tumour cells overexpress the genes encoding DNA repair mechanisms, increasing repair capacity and treatment resistance [48]. DNA ligase IV joins single-strand breaks in a double-stranded polydeoxynucleotide in an ATP-dependent reaction, and its low expression results in inefficient function of the repair system. Compared to normal astrocytes, brain tumour lines had lower levels of the *LIG4* coding gene, and these findings correlated with transcriptomic and genomic analyses [49]. In all tumour cell lines, decreased *LIG4* expression was observed. We also observed a significant reduction in expression of the *ERCC5* gene, whose product is part of the nucleotide excision repair

system [50]. Borderline low levels of the gene encoding Protein Phosphatase 1 Regulatory Subunit 15A have been confirmed.

Epithelial to mesenchymal transition (EMT) is the process by which epithelial cells lose their epithelial characteristics and acquire a mesenchymal phenotype, resulting in increased mobility and chemoresistance [51]. Although glioma cells are not of epithelial origin, an EMT-like process in GBM can be induced [52]. Overexpression of various growth factors, such as transforming growth factor (TGF), epidermal growth factor (EGF), fibroblast growth factor (FGF), and HIF-1, cause EMT in cancer cells [53]. As a result of growth factor-mediated signalling, transcription factors (Snail, Slug, dEF1, SIP1, Twist1, and FOXC2) are activated and induce an EMT-like phenotype [54]. Multiple signalling pathways participate in these processes. The phosphatidylinositol-3-kinase (PI3K)/Akt signalling pathway plays an important role in regulating cell growth and maintaining cancer biology. Cooperation with other signalling pathways such as transforming growth factor β (TGF-β), nuclear factor (NF)-κB, and Ras and Wnt signalling pathways leads to direct or indirect induction of the EMT process, resulting in enhanced invasiveness, aggression, chemoresistance, and apoptosis resistance of the tumour mass [55]. The transcription factor *SOX10* is one of the key determinants of oligodendroglial differentiation. Therefore, Bannykh and colleagues decided to compare the presence of *SOX10* in oligodendrogliomas and astrocytomas to determine its specificity. Although at lower levels [56], the majority of oligodendrogliomas and a significant proportion of astrocytomas, including glioblastomas, produced *SOX10*. Consistent with previous research, multivariable analysis confirmed a decrease in the expression of *SOX10* and *OCLN*, which belongs to the EMT group [57,58].

Cancer cells surrounding the necrotic nucleus lack nutrients and oxygen. Hypoxia is the primary physiological trigger of angiogenesis [59], which is activated by Hypoxiainducible factor 1 [60]. *HMOX1* is one of the many genes expressed during hypoxia induced by HIF-1. Due to its antioxidant and antiapoptotic effects, Hemoxigenase 1 plays a crucial role in tumour growth [61]. Because *HMOX1* activity stimulates angiogenesis, this enzyme is a suitable indicator of glioma neovascularization [62]. Only the T98G glioblastoma cell line was found to have elevated levels of the *HMOX1* gene. In contrast, only *LDHA* and *ARNT* were downregulated relative to controls. Lactate dehydrogenase A (LDHA) is a key enzyme in the anaerobic glycolytic pathway [63]. In addition to promoting acidification of the microenvironment, lactate production promotes the metastatic nature of the tumour [64]. Several authors have reported on the significance of glioblastoma *LDHA* expression [65–67]. Chesnelong and colleagues found low expression and high methylation of *LDHA* in IDHmt glioblastomas [68]. Kathagen-Buhmann et al. identified a decline in LDHA production in non-migrated cells [67]. Since *LDHA* expression is promoted by hypoxia, low levels of *LDHA* in gliomas that were cultivated under standard conditions in the presence of oxygen may be attributable to an oxygenated environment.

The largest number of changes in gene expression (*n* = 8) between tumour and nonmalignant groups were identified in metabolic genes (*PFKL*, *ATP5A1*, *UQCRFS1*, *CPT2*, *COX5A*, *ACLY*, *GPD2*, and *G6PD*). Even under aerobic conditions, tumour cells are known for their high glycolytic activity [69]. Along with an increase in glucose consumption and lactate production, this promotes rapid cell proliferation and GBM growth, which is correlated with the elevated activity of glycolytic enzymes [70]. Phosphofructokinase-1 is a regulatory glycolytic enzyme catalysing the phosphorylation of fructose-6-phosphate to fructose-1,6-bisphosphate. The presence of its liver isoform (PFKL) in gliomas was analysed by Stanke et al. However, they did not observe any statistically significant changes compared to healthy tissue [71]. In our samples, we identified a statistically significant decrease in *PFKL* expression, confirming that the prevalent isoform in brain tissue is the platelet isoform (PFKP), not PFKL [72]. A high expression of *Glucose-6-phosphate dehydrogenase* was spotted. Glucose-6-phosphate dehydrogenase (G6PD) is one of the pentose phosphate pathway (PPP) enzymes that catalyses the production of NADPH [73]. During normoxia, glioma and non-neoplastic brain cells both produce an abundance of these enzymes. A negative association between *G6PD* expression and survival in patients with

low-grade glioma was discovered [74]. COX5A is one of the three subunits of cytochrome c oxidase, a respiratory chain complex IV encoded by mitochondrial DNA [75]. UQCRFS1 is a respiratory chain complex III subunit. Both *COX5A* and the gene encoding another subunit of complex III, *UQCRB*, are downregulated in glioblastoma patients compared to healthy individuals. In contrast, when compared to expression in gliomas of lower grade malignancy, *COX5A* expression is significantly increased in GBM, and *UQCRB* expression is at approximately the same level. The reduced expression has also been linked to a poor prognosis [71]. In line with previous findings, we were able to identify the downregulation of *COX5A* and *UQCRFS1*.

Cellular senescence is an irreversible process of cell cycle arrest [76]. During this process, senescent cells undergo morphological changes that include flattening, increased cytoplasmic volume, or increased granularity. Only the *MAPK14* gene was found to be overexpressed in our sample cohort, while six other genes were found to be underexpressed (*IGFBP3*, *IGFBP5*, *IGFBP7*, *SOD1*, *TBX2*, and *ETS2*). *ETS2* is a transcription factor that regulates apoptotic and angiogenic genes, as well as genes involved in proliferation and differentiation [77]. Cam et al. identified *ETS2* expression in glioblastomas and, in association with ΔNp73, confirmed its role in tumour progression, angiogenesis, and improved tumour cell survival [78]. On the contrary, bioinformatic analysis of transcriptomic data from glioma patients revealed a decrease in gene expression of *ETS2* regardless of the degree of malignancy [79]. In glioma cell lines, we found a statistically significant decrease in *ETS2* expression. Superoxide dismutase 1, encoded by the *SOD1* gene, is an enzyme that converts free superoxide radicals into less harmful hydrogen peroxide and oxygen [80]. A decrease in *SOD1* expression has been identified in glioblastomas and is associated with improved response to radiotherapy and a better prognosis for patients [81,82]. *SOD1* expression was also reduced in glioma cell lines when compared to non-malignant cell controls. The only overexpressed gene involved in the regulation of senescence is *MAPK14*. Mitogen-activated protein kinase 14, a protein product of *MAPK14*, is an essential component of the MAP kinase signal transduction pathway that influences the direct activation of transcription factors in response to cell stress stimuli [83]. *MAPK14* expression was found to be elevated in glioma cells, which is in contrast to the findings of other studies, which indicated that the expression of this gene was decreased in glioblastoma samples [84].

Human telomeres, located at the ends of chromatids, are tandem nucleotide repeats of a short DNA sequence associated with various telomere-binding proteins with a predominantly protective function [85]. The primary function of telomeres is to compensate for incomplete DNA replication at chromosome ends, thereby maintaining intact genetic information [86]. However, as a result of cell division, telomeres become progressively shorter, resulting in cellular senescence and apoptosis induction [87]. PINX1 was identified as a potent telomerase inhibitor that interacts directly with the catalytic activity of telomerase [88]. Our analysis revealed a decrease in *PINX1* expression. Previous studies have shown a correlation between a decrease in *PINX1* expression and the metastatic nature and poor prognosis of cancer patients [89]. In glioblastoma cell lines with induced overexpression of *PINX1*, there was a reduction in cell migration and proliferation due to cell cycle arrest at the G1 phase [90]. In contrast, there is evidence that *PINX1* expression is associated with poor survival in glioma patients because it promotes cell proliferation [75,91]. The *DKC1* gene encodes Dyskerin, an additional protein that regulates telomerase activity [92]. Glioma is one of several human cancers in which *DKC1* is upregulated [93,94]. Consistent with previous findings, elevated expression of *DKC1* in glioma cell lines was also identified. Tankyrases (TNKS, TNKS2) are proteins involved in telomere length maintenance [95], which, together with regulation of the Wnt/β-catenin pathway, is important for cancer cell renewal and survival [96]. Expression of *TNKS* and *TNKS2* was decreased in glioma cells compared to non-malignant cells and normal brain tissue. Additionally, the expression of *TINF2* was reduced. We identified an increase in *TERF* gene expression.

Glial tumours are biologically aggressive neoplasms with an elevated, often aberrant, and diffusely invading proliferative capacity. Composed of poorly differentiated neoplastic

astrocytes, glioblastoma (grade IV astrocytoma) is the most malignant astrocytic tumour. According to histopathological and molecular criteria, the WHO grading system categorises gliomas into grades I through IV, based on their degree of malignancy. Although the majority of neurological tumours derive from the glial lineage, it is unclear whether tumour cells result from the transformation of an immature precursor or the dedifferentiation of a mature glial cell. Several genetic pathways are involved in the initiation and progression of these neoplasms, particularly during the manifestation of secondary GBMs.

#### **4. Materials and Methods**

#### *4.1. Cell Culturing*

The glioma tumour cell panel (T98G, A172 and SW1088) was purchased from American Type Culture Collection (ATCC) under catalogue numbers: CRL-1690™, CRL-1620™, HTB-12™; respectively). Cell cultures were maintained as monolayer in Dulbecco's modified Eagle's media with 25 mM glucose, and supplemented with foetal bovine serum (10%, *v*/*v*), and penicillin/streptomycin (1×; PAA). Normal Human Astrocytes (NHA) were provided from ATCC and cultured in Dulbecco's modified Eagle's media high glucose/F12 (1:1; Merck KGaA, Darmstadt, Germany) supplemented with foetal bovine serum (10%, *v*/*v*), and penicillin/streptomycin (1×; PAA). The Human Dermal Fibroblasts (HDFa; Gibco— Thermo Fisher Scientific, Waltham, MA, USA) was used as non-specific tissue control and cultured in HAM´s Nutrient Mixture F12 (Merck KGaA, Darmstadt, Germany) supplemented with foetal bovine serum (10% *v*/*v*; Gibco—Thermo Fisher Scientific, Waltham, MA, USA) and penicillin/streptomycin (1×; PAA Laboratories GmbH, Austria). Cells were cultured at 37 ◦C in an atmosphere of 5% CO2. Before each experiment, single-cell suspension was prepared using 0.05% trypsin/EDTA solution, and cells were counted using CountessTM automated cell counter (Thermo Fisher Scientific, Waltham, MA, USA).

#### *4.2. Control Brain RNA*

Commercially available total RNA from human brain tissue of single healthy normal donor was used as a control group (HR-201, Human Brain Total RNA—Amsbio, Abingdon, UK) for quantitative PCR. For quantitative PCR analysis, we used three independent transcripts into cDNA.

#### *4.3. RNA Extraction and cDNA Synthesis*

Total RNA was isolated using AllPrep® DNA/RNA Mini Kit (Qiagen Inc., Germantown, MD, USA). Concentration of isolated RNA was measured in Implen P300 NanoPhotometer (Implen GmbH, München, Germany). Two micrograms of purified cellular RNA was converted to single-stranded cDNA using RT2 First Strand Kit (330,401; Qiagen Inc., Germantown, MD, USA) according to the protocol supplied by the manufacturer.

#### *4.4. Real-Time PCR Array*

Real-time PCR (quantitative PCR) was carried out using RT2 SYBR® Green RoxTM qPCR Mastermix (330,502; Qiagen Inc., Germantown, MD, USA) in 96-well plate format of the Human Cancer PathwayFinderTM PCR Array (PAHS-033ZC; Qiagen Inc., USA). The PCR reaction mix (SYBR® Green RoxTM qPCR Mastermix (1340 μL), PCR water (1290 μL) and cDNA (50 μL) was distributed into the 96-well plate to a final volume of 25 μL per well. The sealed plate was briefly centrifuged at 1000× *g* for 1 min. Amplification was performed in the ViiA7 Real-Time PCR system (Thermo Fisher Scientific, Waltham, MA, USA). After denaturation at 95 ◦C for 10 min, fluorescence was detected over 40 cycles (95 ◦C for 15 s, 60 ◦C for 1 min).

#### *4.5. Statistical Analysis*

Samples of cDNA were measured in triplicate, and the levels of the genes of interest were normalized to the three endogenous controls (β-actin, *ACTB*; Ribosomal protein large unit P0, *RPLP0* and Glyceraldehyde-3-phosphate dehydrogenase, *GAPDH*), determined using the ΔΔCt method. The expression data from the separate control group:

These were used as a reference in the ΔΔCt method calculation for each glial cell line (T98G, A172 and SW1088) individually. The relative expression of 84 genes in tumour cell lines and non-neoplastic samples was calculated using the RT2 Profiler PCR Array Data Analysis Web Portal (Qiagen) based on 2−ΔΔCt method [97], where ΔΔCt = (CtGOI − CtHKG)TESTING GROUP − (CtGOI − CtHKG)CONTROL GROUP. Fold-change calculations were performed using Qiagen data analysis software (https://dataanalysis2 .qiagen.com/pcr, accessed on 1 January 2022). The genes with a significant difference in expression were those with an average fold-change of ≤−2.0 or ≥2.0, and statistically significant differences were those with a corresponding *p* value of <0.05.

Gene expression values were normalised to a 0–1 scale for both control and tumour cell lines. Using the KNIME Analytics Tool, sample normalisation, principal component analysis, and linear discriminant analysis were calculated.

For the statistical analyses mentioned above, only genes with detectable signals in all samples were selected from the raw data set. The Euclidean distance was used to calculate the distance between the samples. Using Pearson correlation, gene–gene expressions of control and tumour samples were correlated, respectively. The Pearson correlation coefficient for the two populations (*X*, and *Y*) is calculated as follows:

$$\rho\_{X,Y} = \frac{cov(X\_\prime \, Y)}{\sigma\_X \sigma\_Y}$$

where *cov*(*X*,*Y*) is the covariance; *σX* is the standard deviation of *X*; and *σY* is the standard deviation of *Y*. The analyses were calculated in Python using Anaconda Navigator and JupyterLab. The Pandas, NumPy, and SciPy libraries were used. Kernel density and bandwidth optimisation were calculated using the Shimazaki and Shinomoto web application (https://www.neuralengine.org/res/kernel.html, accessed on 8 March 2022). We used the Matplotlib Python library to visualise the heatmap of gene correlations.

#### **5. Conclusions**

We focused on the transcriptomic analysis of genes associated with cancer pathways in glial tumour cells. As the experimental models, we selected the human glioblastoma cell lines A172 and T98G and the astrocytoma cell line SW1088. Sixty genes were deregulated in glioblastoma cell line A172 in comparison to the HDFa control group; 57 genes in comparison to the human RNA control group; and 54 genes in comparison to the human astrocytes control group, according to transcriptomic data. In the astrocytoma cell line SW1088, we found differences in the expression levels of 57, 60, and 59 genes related to HDFa, hRNA, and NHA control groups, respectively. In correlation with T98G and HDFa, 47 significantly deregulated genes were discovered. With hRNA, 57 genes, and the NHA control group, 52 genes with varying expression levels were identified. By combining the PCA method and multi-criteria decision in the analysis of gene expression, we were able to identify altered genes involved in cancer pathways in heterogeneous sample groups. We managed to reduce the selection of significant genes based on a combined mathematical analysis. In tumour cells, we finally identified 26 genes that showed a deregulated state compared to the average expression value of three different controls. The most changed genes represented pathways involved in cellular senescence (*BM1*, *ETS2*, *IGFBP5*, *IGFBP7*, *SOD1* and *TBX2*) and then metabolism (*ATP5A1*, *COX5A*, *CPT2*, *PFKL*, *UQCRFS1*).

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ijms231810883/s1. References [98–100] are cited in the supplementary materials.

**Author Contributions:** Conceptualization, J.H. and G.Z.; methodology, Z.M., M.G. and D.H.; formal analysis, R.M. and G.Z.; investigation, M.G. and G.Z.; resources, J.H. and G.Z.; data curation, Z.M., K.D., M.G. and D.H.; writing—original draft preparation, Z.M., M.G. and D.H.; writing—review and editing, J.H., D.H. and R.M.; visualization, J.H.; supervision, J.H. and G.Z.; project administration, J.H.; funding acquisition, J.H., M.G. and G.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Slovak Research and Development Agency under the contract no. APVV-18-0088 and by the project implementation: Open scientific community for modern interdisciplinary research in medicine (Acronym: OPENMED), ITMS2014+: 313011V455 supported by the Operational Programme Integrated Infrastructure. M.G. was funded by the ERDF and Internal Scientific Grants System (VVGS) of Faculty of Science UPJŠ (no. vvgs-pf-2022-2116).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data generated and analysed during the current study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Communication* **The Value of FET PET/CT in Recurrent Glioma with a Different IDH Mutation Status: The Relationship between Imaging and Molecular Biomarkers**

**Marija Skoblar Vidmar 1,2,\*, Andrej Doma 2,3,\*, Uroš Smrdel 1,3, Katarina Zevnik 2,3 and Andrej Studen 4,5**

	- 3 Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
	- 4 Experimental Particle Physics Department, Jožef Stefan Institute, 1000 Ljubljana, Slovenia<EMAIL_ADDRESS>5 Faculty of Mathematics and Physics, University of Ljubljana, 1000 Ljubljana, Slovenia
	- **\*** Correspondence<EMAIL_ADDRESS>(M.S.V<EMAIL_ADDRESS>(A.D.)

**Abstract:** The evaluation of treatment response remains a challenge in glioma cases because the neuro oncological therapy can lead to the development of treatment-related changes (TRC) that mimic true progression (TP). Positron emission tomography (PET) using O-(2-[18F] fluoroethyl-)-L-tyrosine ( 18F-FET) has been shown to be a useful tool for detecting TRC and TP. We assessed the diagnostic performance of different 18F-FET PET segmentation approaches and different imaging biomarkers for differentiation between late TRC and TP in glioma patients. Isocitrate dehydrogenase (IDH) status was evaluated as a predictor of disease outcome. In our study, the proportion of TRC in IDH wild type (IDHwt) and IDH mutant (IDHm) subgroups was without significant difference. We found that the diagnostic value of static and dynamic biomarkers of 18F-FET PET for discrimination between TRC and TP depends on the IDH mutation status of the tumor. Dynamic 18F-FET PET acquisition proved helpful in the IDH wild type (IDHwt) subgroup, as opposed to the IDH mutant (IDHm) subgroup, providing an early indication to discontinue dynamic imaging in the IDHm subgroup.

**Keywords:** glioma; treatment-related changes; true progression; pseudoprogression; radiation necrosis; biomarkers; 18F-FET PET; IDH mutation

#### **1. Introduction**

Molecular biomarkers have fundamentally changed the understanding of glioma over the last decade. Accordingly, the fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) incorporates numerous molecular biomarkers with clinicopathologic utility that are important for more accurate classification of CNS neoplasms [1]. Molecular biomarkers also improve diagnostic accuracy and influence the course of treatment by changing treatment recommendations [2]. A marker of particular importance is isocitrate dehidrogenase (IDH). Mutations in genes encoding IDH are known to play a crucial role in the classification of gliomas. IDHm glioma generally exhibits a better disease outcome than IDHwt. The IDHm is an independent predictor of prolonged survival and its prevalence is inversely correlated with tumor grades [3,4]. In adults, diffuse gliomas have been divided into three types according to the new classification: (1) astrocytoma, IDHm; (2) oligodendroglioma, IDHm and 1p/19qcodeleted; and (3) glioblastoma, IDHwt. Prior to that, glioblastomas were diagnosed based on the histologic findings including both IDHm and IDHwt tumors with very different biological features and prognoses. In WHO CNS5, glioblastomas comprise only IDHwt tumors. In addition, IDHwt diffuse astrocytic tumors in adults without the histologic features of glioblastoma but having one or more of three genetic parameters (TERT promoter mutation, EGFR gene amplification, combined gain of entire chromosome 7 and loss of

**Citation:** Skoblar Vidmar, M.; Doma, A.; Smrdel, U.; Zevnik, K.; Studen, A. The Value of FET PET/CT in Recurrent Glioma with a Different IDH Mutation Status: The Relationship between Imaging and Molecular Biomarkers. *Int. J. Mol. Sci.* **2022**, *23*, 6787. https://doi.org/ 10.3390/ijms23126787

Academic Editors: Nives Pe´cina-Šlaus and Ivana Jovˇcevska

Received: 13 May 2022 Accepted: 14 June 2022 Published: 17 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

entire chromosome 10) are classified as glioblastomas. In the new classification, all IDHm diffuse astrocytic tumors are considered a single type astrocytoma, IDHm and are graded as gradus 2, 3, or 4. Grading of gliomas also takes into account some other molecular findings such as the presence of CDKN2A/B homozygous deletion, which results in a worse prognosis and is subsequently graded as a WHO grade 4. The separation into IDH wild type and mutant tumors is an important advancement and a key factor in the treatment, follow-up, and understanding of glial tumors [5].

The treatment of gliomas includes maximal surgical resection, possibly followed by radiotherapy (RT) and chemotherapy with either procarbazine/lomustine/vincristine (PCV) or temozolomide (TMZ). Due to the proliferative, radioresistant, and chemoresistant nature of the gliomas and high levels of intratumoral heterogeneity, the disease often recurs, and the possibilities of additional treatment are very limited [6].

In the regular clinical work, the glioma treatment response assessment is based on imaging diagnostics, primarily MRI. MRI is the mainstay of imaging gliomas to monitor both treatment and response. T1-weighted MRI without and with contrast medium, T2-weighted as well as fluid-attenuated inversion recovery (FLAIR) MRI sequences are used for anatomic imaging [7–9].

Irradiation of brain tumors causes damage to the blood-brain barrier, which can lead to extravascular leakage of the contrast medium, which may have the same appearance on magnetic resonance imaging (MRI) images as a vital residual tumor or TP. This side effect of oncology treatment is defined as pseudoprogression and begins to occur approximately three months after irradiation, with the incidence of up to 50% in combined oncology treatment. Another radiation-induced side effect is radionecrosis (RN), possibly due to vascular injury and glial cell damage, usually occurring six months after the irradiation. RN can have the appearance of tumour residue or tumor recurrence on postcontrast MRI. The incidence of RN is estimated at up to 30% and increases with the length of the time from irradiation [10,11].

TRC, such as pseudoprogression and RN, overlaps with TP. This makes the differentiation challenging, and can consequently complicate the treatment course and compromise care. Therefore, the correct differentiation between TRC and actual TP continues to be a crucial issue [12,13]. For these reasons, additional imaging methods such as perfusion MRI or MR spectroscopy and functional methods such as 18F-FET PET are used. 18F-FET PET CT is based on the evaluation of transport of 18F labeled tyrosine in tissues. In gliomas, 18F-FET uptake significantly correlates with tumor cell density and neoangiogenesis, all biological hallmarks of highly malignant glial tumors. [14–16]. The aim of this study was to assess the diagnostic performance of different 18F-FET PET segmentation approaches for differentiation between late TRC and TP in glioma patients with different IDH mutation statuses.

Since we investigated the late effects of radiochemotherapy, TRC was associated with radiation necrosis in our study.

#### **2. Materials and Methods**

#### *2.1. Subjects*

This retrospective study included 47 patients who were treated at the Division of Radiotherapy, Institute of Oncology in Ljubljana and, on the recommendation of the multidisciplinary tumor board, were referred to our Nuclear Medicine Department for 18F -FET PET imaging between April 2019 and October 2021 in order to distinguish between TP and TRC. All patients who had undergone a standard MRI were able to understand the reason for additional 18F-FET PET imaging. All patients had previously been diagnosed with adult diffuse gliomas and had a prior biopsy and radiochemotherapy according to EANO guidelines. All patients had a prior MRI suspicious of TP, as determined by the Response Assessment in Neuro-Oncology (RANO) working group criteria.

The study was approved by the institutional review board committee (approval number ERIDNPVO-0073/2021). All involved persons gave their written informed consent prior to study inclusion. The study conformed to the ethical norms and standards in the Declaration of Helsinki. All biological material was administered according to international guidelines.

#### *2.2. Determination of IDH Genotype*

The IDH mutation status was assessed by the IDH1R132H protein expression level evaluated by immunohistochemistry until early 2017 (15 pts), and after that using Next Generation Sequencing of a Glioma-Tailored Gene Panel (29 pts). For 18F-FET PET analysis, patients were split into IDHm and IDHwt groups. Next-generation sequencing (NGS) is being increasingly used in routine clinical practice, including for the diagnostics of rare entities like gliomas because it can replace multiple single-gene genomic testing technologies while requiring only one test. Gene-targeted NGS offers a cost-effective approach to simultaneous detection of multiple genetic alterations with a minimal amount of sampled DNA while achieving high sensitivity. This makes this method highly attractive for use in gliomas. Specifically designed panels for gliomas are needed for the routine diagnosis of these tumors. We use the isolation of DNA from FFPE tissue using the Maxwell RSC FFPE Plus DNA Purification kit (Promega). The NGS panel assesses mutations in specific target regions ("Hotspots") in 9 genes: BRAF, H3F3A, HIST1H3B, HIST1H3C, IDH1, IDH2, KRAS, NRAS, pTERT, analysis of the entire coding region of 11 genes: ACVR1, ATRX, CIC, FUBP1, EGFR, FGFR1, PIK3CA, PIK3R1, PTEN, SETD2, TP53,12 gene copy number (CNV) analysis: CDKN2A, CDKN2B, EGFR, FGFR1, MDM2, MDM4, MET, MYCN, PDGFRA, PIK3CA, PIK3R1, PTEN and chromosome level analysis. Analysis of results is performed with the IonReporter software package (Thermo Fisher Scientific) (reference genome hg19). Border detection (default filter) is set to 5.0% (mutation rate relative to unmutated DNA). The sensitivity of the method is 99.21% (hotspot), 96.88% (indel), 97.10% (de novoSNV), 85.71% (de novo indel), and 95.35% (fusion). Negative results (no mutations) do not exclude the presence of mutations, amplifications, or deletions below the limit of detection [17].

#### *2.3. 18F-FET PET Imaging*

The synthesis of 18F-FET was performed by IASON GmbH (Graz, Austria). We used an integrated PET/CT system (Biograph mCT 64; Siemens, Erlangen, Germany) for acquisition of dynamic PET images over 40 min, starting immediately after injection of 3MBq of 18F-FET per kg of body weight. All patients fasted for at least 6 h prior to PET acquisition. Dynamic 40-min scans were acquired using 35 sequences (200 × 200 matrix; 12 × 5 s; 6 × 10 s; 6 × 30 s; 5 × 60 s; 6 × 5 min). PET images were reconstructed with orderedsubset expectation maximization (OSEM) algorithm using 2 iterations with 21 subsets and gauss filtering to a full width at a half maximum (FWHM) of 5mm. High-resolution static images (400 × 400 matrix) were reconstructed from 20–40 min post-injection scans with OSEM algorithm consisting of two iterations with 21 subsets and Gaussian filtering to a FWHM of 3 mm.

#### *2.4. 18F-FET PET Image Analysis*

PET scans were interpreted by two experienced nuclear medicine physicians, who were blinded to the histological and clinical data. The assessment of the tumor maximal standardized uptake value (SUVmax) was performed for each lesion by placing a spherical Volume-of-Interest (VOI) over the area of maximal amino acid uptake in the tumor on summed 20–40 min post-injection PET images. The mean standardized uptake value of the normal background brain tissue (SUVmean\_bg) was determined by placing a crescentshaped VOI over the contralateral, unaffected hemisphere including white and grey matter. A tumor volume segmentation using a 3-dimensional auto contouring process with a tumor-to-background ratio (TBR) cutoff of at least 1.6 g/mL was used to determine the mean standardized uptake value of the tumor (SUVmean\_tumor). This cutoff was based on the results of a biopsy-controlled study in which a lesion-to-brain ratio of 1.6 g/mL

resulted in the best separation between tumor and non-tumor tissue [16]. Respective ratios of the TBRmean and TBRmax were calculated by dividing SUVmean\_tumor and SUVmax, respectively, by the SUVmean\_bg. For Time Activity Curve (TAC) evaluation, a spherical VOI was centered over the area of the highest tumor uptake to the entire dynamic datasets. Time-to-Peak (TTP) was determined as a time (in minutes) from the beginning of the dynamic PET acquisition up to the peak activity in the lesion. In lesions with constantly increasing TAC without an identifiable peak, TTP was defined as the end of the dynamic PET acquisition. We identified a cut-off point at 22.5 min post-injection as a dip in a distinct two-peak distribution of patients with respect to TTP with an early group reaching a distinct maximum before the prescribed threshold and a late group reaching it later or not exhibiting a peak at all. Using the cut-off, we identified the following curve categories, in order of increasing shape score:

#### *2.5. Diagnosis of TP*

Diagnosis of TP was based on histopathologic analysis following surgery, by clinical deterioration, and/or further radiological progression in a follow-up MRI at least four weeks after the initial assessment. In contrast, the diagnosis of TRC was applied in cases of negative histopathology, stable clinical conditions (with no treatment changes within the follow-up time), or stabilization/regression of the contrast-enhancing lesions at follow-up MRI (at least four weeks following initial assessment), respectively. The diagnosis for some patients was confirmed by more than one modality. Thus, the classification criteria in our study were similar to those of previous investigations [18,19].

#### *2.6. Statistical Analysis*

Descriptive statistics were used to calculate typical measures in patients' demographic and clinical characteristics. Data were expressed as median with a range, and categorical data were expressed as counts and frequencies. Statistical analyses were carried out using IBM SPSS Statistics software version 26 (Statistical package for the Social Sciences Statistical Software; SPSS Inc, IBM Corporation, Armonk, NY, USA).

We analyzed the FET outcome data using R statistical software (R version 3.1.1 (2014-07- 10), R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (accessed on 16 June 2022). To statistically assess significant differences in ROC curves we performed a non-parametric ROC analysis [20] We used the optimal operating point on the ROC curve using the Youden index, and uncertainty in cut-off values was modelled using a large number approach [21]. We compared patient groups using the Mann-Whitney-Wilcoxon rank-sum U test [22]. We combined TAC score, IDH mutation status, and TBRmean predictions using the logistic regression (LR) model. When used on patients grouped by IDH mutation status, we only used TAC score and TBRmean variables in the LR model. Results with a *p*-value below 0.05 were deemed statistically significant, and 95% confidence intervals (CI95%) were used to quantify uncertainty in statistically derived values.

#### **3. Results**

We analyzed the data of 47 patients with glial tumors who underwent 18F-FET PET for differentiation between TP and TRC. The interval between the end of radiation therapy and subsequent PET imaging was no less than 12 weeks in all cases.

Forty-four patients were eligible for analysis, and their median age was 44 (17 to 72, SD 14 years). Twenty-seven (61.4%) were male and 17 (38.6%) were female.

IDHm and IDHwt were present in 26 (59.1%) and 18 (40.9%) patients, respectively.

TP and TRC were confirmed in 32 (72.7%) and 12 (17.7%), respectively. The proportion of TRC in IDHwt 5/18 (27.8%) and IDHm 7/26 (26.9%) groups, was without significant difference (*p* = 0.61).

Diagnosis of TP/TRC was confirmed through histopathological analysis following surgery, through MRI, or based on clinical deterioration.

Regarding the verification of the diagnosis, 11 patients had surgery, 38 patients got a confirmed diagnosis after repeated MRI and 34 patients experienced clinical deterioration. Two out of 12 patients with TRC (2/12) had surgery, as opposed to nine patients with true progression (9/32). The proportion of those operated on is not significantly different between these groups.

The time from diagnosis to 18F-FET PET was 104 weeks (84 weeks in IDHwt and 130 weeks in IDHm; the difference in median time to evaluation was without significance regarding the TRC or TP (*p* = 0.5), as well as IDH status (*p* = 0.9).

The proportion of patients with TRC according to IDH status was not significantly different (χ2 *p* = 0.9).

In this group of patients, the overall survival was excellent, with median survival exceeding 500 weeks. The median survival according to IDH status was not different, though the median survival was not reached in the TRC group, the analysis is underpowered to detect significance.

18F-FET-PET parameters were then analyzed for the whole group and selectively according to different IDH mutation statuses. For the whole group, the SUVmax value had a mean of 4.04 and a median of 3.78, with the SD of 1.83 (IDHm: 4.10, 3.82, and 1.73, IDHwt: 4.02, 3.76, and 2.05, respectively). The difference between groups was not statistically significant.

When comparing the mean values for SUV parameters according to the IDH mutation status, we found that while in IDHwt patients there are no significant differences in SUV values according to radio necrosis and progression, in the IDHm, the mean values of SUVmax (TRC/TP (p): 4.7/2.49 (0.001)), TBRmax and TBRmean differed significantly (Table 1).

Figure 1 shows a comparison of ROC curves for different FET- derived variables split by IDH mutation status. For all patients, TBRmax and TBRmean together with logReg show a statistically significantly better performance compared to TTP. In groups split by IDH status, no statistically significant differences in ROC curves could be determined. Relapse was associated with larger values of TBRmax and TBRmean, while the opposite association to lower values was identified for TTP. In IDHm, the TTP ROC is below the diagonal, indicating an inverse relationship between higher TTP and relapse, which is not statistically confirmed. In IDHwt cases, TTP is the best predictor, its difference to either TBRmax or TBRmean is, however, not significant. We found a single significant variable, TBRmean, with a coefficient of 1.6 (*p* = 0.03) and equivalent odds ratio of 4.9 (CI95%: 2.1–8.5) per unit change in TBRmean in the LR model for the full patient group. We identified no significant variables in IDH-specific LR models.

Table 1 shows the ability of FET in predicting the tumor status. For each IDH group we show the predictive quality of FET variables -TBRmax, TBRmean, TTP and LR. For each variable we show its mean and range in the TRC group, mean and range in TP group, optimum cut-off based on the Youden index, sensitivity and specificity with associated 95% confidence intervals at cut-off point and the *p*-value associated with the MWU test for patients grouped by tumor outcome. The variables that can identify tumor outcome with statistical significance are shown in bold. Two regimes can be identified: in the full and IDHm group, TBRmax and TBRmean are significant and TTP is irrelevant. Conversely, TTP becomes significant and TBRmean and TBRmax irrelevant in the IDHwt group. In all cases the LR model which combines TBRmax and TAC score is a significant predictor of the tumor outcome.

**Table 1.** Analysis of sensitivities, specificities, thresholds and diagnostic accuracy of 18F-FET PET biomarkers in differentiation between late TP and TRC in glioma patients.

**Figure 1.** Receiver–operator characteristic (ROC) curves in classifying tumor outcome based on FET-derived parameters for different IDH mutation status groups. All: all patients in the study. IDH1(m): patients with mutated IDH gene. IDH1(wt): patients with IDH wild type. TBRM is TBRmax, TBR(1.6) is TBRmean at SUV 1.6 g/mL cutoff, TTP is time to peak and log Reg is the logistic regression model. The image shows AUC values with associated standard deviation. The asterisk associated brackets identify variables with statistically significant ROC curves as evaluated by a non-parametric significance test.

#### **4. Discussion**

This is the first reported study of performance of 18F -FET PET for the differentiation between TP and TRC in glioma patients based on IDH mutation status. We evaluated the diagnostic potential of static and dynamic 18F -FET PET parameters for differentiation between late TRC and TP based on different IDH mutation subgroups in mixed diffuse glioma patients. The differentiation between TP and TRC represents one of the most frequent indications for the use of amino acid PET in clinical practice. PET using radiolabeled amino acids is gaining increasing interest for the diagnostics of brain tumors because a conventional MRI is limited in differentiating tumor tissue from nonspecific tissue changes following neuro-oncological treatment. Recently, the RANO working group has recommended the additional use of amino acid PET imaging for brain tumor management [23].

There is a growing body of evidence that IDH mutations play a role in the formation of brain tumors and influence the response to neuro oncological treatment and overall survival [24,25]. *IDH* mutations in glioma are associated with significantly prolonged progression-free and overall survival compared with IDHwt tumors. There is now a greater appreciation that the biology of IDHm glioma is quite different from that of IDHwt tumors and that tumorigenic processes most likely are different as well. While the specific mechanism of IDH mutation that results in the oncogenic switch in gliomas remains unknown, potential mechanisms have been identified, including the inhibition of hypoxia-related proline hydroxylases, inhibition of DNA demethylases, inhibition of histone demethylases, and alterations in glutamate metabolism. Further work is needed to elucidate the specific role of IDH mutation and the pathological consequences that clearly affect tumor evolution and prognosis. Given the complex role of the IDH mutation in the progression, aggressive biological behavior, and response to the treatment of diffuse gliomas, it seems reasonable to observe and analyze these two molecular groups separately [26–30].

In our study, the static 18F-FET PET measures TBRmax and TBRmean outperformed the dynamic parameters in the IDH (all) group (accuracy 79 and 76% respectively, *p* = 0.001 both) and the IDHm group (accuracy 91 and 87%, p of 0.004 and 0.01, respectively); dynamic measure TTP only achieved an accuracy of 60 and 61% in IDH (all) and the IDHm group, respectively, and the p value of the MWU test was not significant. The situation was reversed in the IDHwt group, where TTP was found to be a significant predictor (accuracy of 83%, *p* = 0.05) while static measures showed a mediocre performance (accuracy of 67 and 72% respectively, non-significant p values of MWU test).

The results of our study contradict several previous reports, where authors described improved diagnostic sensitivity when dynamic metrics were added to static 18F-FET metrics alone.

The difference in the results reported could be due to distinctly different patient populations: while previous studies [31–35] did not take into account the IDH mutation status of the participants, IDH mutation was present in 59.1% of our patients, this being a high proportion compared to usual rates in glioma patients. IDH mutation rates vary substantially between different types of glioma, nonetheless they are not common [28,29].

Therefore, the significant diagnostic performance of dynamic PET acquisition in a non-divided group of glioma patients, the majority of which would typically be IDHwt, was impaired by a large IDHm subgroup included in our study.

Due to the limited availability of the radiotracer at our institution we consider performing 18F-FET PET/CT investigation particularly when MRI yields inconclusive results between TRC and TP. Important for our study is that 18F-FET PET/CT imaging was considered appropriate only if it resulted in therapeutic consequences. The patients with poor performance status and without further treatment options, typically being IDHwt, were therefore following the standard of care not assigned to receive 18F-FET PET imaging, thus further reducing the number of IDHwt subjects in our study. Therefore, a higher proportion of IDH mutations in our study is a consequence of more frequent equivocal decisions at a multidisciplinary tumor board in IDHm cases, and represents a selection of notably difficult cases [31]. A high rate of IDHm in our patients also enabled this IDH mutation status-based study.

In comparison to the static acquisition, the use of dynamic acquisition of 18F-FET PET is very time-consuming and can therefore be challenging in an otherwise busy imaging department. Dynamic imaging incurs additional costs as a consequence of the prolonged decay of the tracer intended for consecutive patients, and fewer scans are performed within normal working hours, hence shorter imaging times are preferred. In our study, the shape of the dynamic curve, in general, has not been statistically significantly associated with the TRC or TP, although there was a trend that associated the plateau-shaped curve with the progressive disease. Our results suggest that the exclusion of dynamic acquisition and the performance of only static acquisition in IDHm patients could be a cost-effective strategy without the diagnostic potential of the investigation being hampered. Nonetheless, since dynamic imaging metrics proved useful in the IDHwt subgroup, these patients should be provided with the dynamic acquisition. [32,33].

While the cutoff values of TBRmean in the all, IDHm and IDHwt subgroups (2.04, CI95%:1.8–2.3; 1.96, CI95%:1.7–2.3; and 2.09, CI95%:1.6–2.6, respectively) as determined in our study, within CL agree with previously reported cutoff values of 1.9–2.0 for the differentiation of both early and late TRC from true progression, the cutoff values of TBRmax for both all and IDHm (3.03, CI95%:2.6–3.4), and IDHwt (2.9, CI95%: 2.2–3.6) subgroups, as determined in our study, were above the values, reported by the majority of authors [31–34]. An optimal TBRmax cutoff value of about 1.9 with an accuracy of 85% was determined in late TRC glioblastoma patients, while a TBRmax cutoff of 2.3 (accuracy 96%) was determined in the early glioblastoma pseudoprogression by Galldiks et al. [18,34,35]. However, Kartels et al. estimated the optimal TBRmax cutoff value to be 3.52 in a late glioblastoma multiforme group of patients, indicating non-uniform outcomes in different patient group settings [36]. The reason for only moderate accuracy in differentiating TP from TRC might be due to a non-homogeneous group of patients because we did not limit our study to high-grade gliomas or specific treatment regimens. As reported in a meta-analysis by Cui et al., the accuracy of FET is known to be higher in high-grade glioma than in the mixed glioma patients group [37].

#### **5. Conclusions**

Differentiating TRC from TP is of critical importance for patient management and prognosis and it can often be challenging. In our study, the proportion of TRC in IDHwt and IDHm subgroups was without significant difference. We found that the diagnostic value of static and dynamic biomarkers of 18F-FET PET for discrimination between TRC and TP depends on the IDH mutation status of the tumor. Dynamic biomarkers play an important role in the IDHwt subgroup, and as opposed to the case of IDHm, the dynamic acquisition of 18F-FET PET might eventually be discontinued. Further prospective research in large sample sizes is needed to determine the value of 18F-FET PET in different molecular biomarker settings and to confirm our findings.

**Author Contributions:** Conceptualization, M.S.V. and A.D.; methodology, M.S.V., A.D. and A.S.; validation, M.S.V., A.D. and K.Z.; formal analysis, M.S.V., A.D., K.Z., U.S. and A.S.; investigation, M.S.V., A.D.; data curation M.S.V., A.D. and K.Z.; writing—original draft preparation, M.S.V., A.D., U.S. and A.S.; writing—review and editing, M.S.V. and A.D.; visualization, A.S.; project administration, M.S.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Oncology Institute Ljubljana, approval number ERIDNPVO-0073/2021.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The patient data are not publicly available due to ethical restrictions.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **The Use of Pro-Angiogenic and/or Pro-Hypoxic miRNAs as Tools to Monitor Patients with Diffuse Gliomas**

**Guénaëlle Levallet 1,2,\*, Fatéméh Dubois 1,2, Arthur Leclerc 3, Edwige Petit 1, Lien Bekaert 3, Maxime Faisant 2, Christian Creveuil 4, Evelyne Emery 3, Gérard Zalcman 5,6 and Emmanuèle Lechapt-Zalcman 1,2,7,8**

**Abstract:** IDH (isocitrate dehydrogenase) mutation, hypoxia, and neo-angiogenesis, three hallmarks of diffuse gliomas, modulate the expression of small non-coding RNAs (miRNA). In this paper, we tested whether pro-angiogenic and/or pro-hypoxic miRNAs could be used to monitor patients with glioma. The miRNAs were extracted from tumoral surgical specimens embedded in the paraffin of 97 patients with diffuse gliomas and, for 7 patients, from a blood sample too. The expression of 10 pro-angiogenic and/or pro-hypoxic miRNAs was assayed by qRT-PCR and normalized to the miRNA expression of non-tumoral brain tissues. We confirmed in vitro that IDH in hypoxia (1% O2, 24 h) alters pro-angiogenic and/or pro-hypoxic miRNA expression in HBT-14 (U-87 MG) cells. Then, we reported that the expression of these miRNAs is (i) strongly affected in patients with glioma compared to that in a non-tumoral brain; (ii) correlated with the histology/grade of glioma according to the 2016 WHO classification; and (iii) predicts the overall and/or progression-free survival of patients with glioma in univariate but not in a multivariate analysis after adjusting for sex, age at diagnosis, and WHO classification. Finally, the expression of miRNAs was found to be the same between the plasma and glial tumor of the same patient. This study highlights a panel of seven pro-angiogenic and/or pro-hypoxic miRNAs as a potential tool for monitoring patients with glioma.

**Keywords:** miRNA; hypoxia; angiogenesis; glioma

#### **1. Introduction**

Adult diffuse gliomas are the most common primary malignant brain tumors, accounting for approximately 60% of all central nervous system tumors. These tumors are characterized by a number of criteria, either morphological/histological (tumoral cells mitoses, microvascular proliferation, hypoxia/necrosis as infiltration (for review: [1])) or molecular (isocitrate dehydrogenase (IDH) mutations, 1p/19q codeletion, ATRX (alpha thalassemia/mental retardation syndrome X-linked) mutations, mutations in the promoter of *TERT* (telomerase reverse transcriptase), etc., [2–4]). Since the revision of the classification of brain tumors according to the World Health Organization (WHO) in 2016 [2], which was reviewed in 2021 [5], all these morphological/histological and molecular criteria have been integrated by pathologists to establish a histoprognostic grade of these tumors for each

**Citation:** Levallet, G.; Dubois, F.; Leclerc, A.; Petit, E.; Bekaert, L.; Faisant, M.; Creveuil, C.; Emery, E.; Zalcman, G.; Lechapt-Zalcman, E. The Use of Pro-Angiogenic and/or Pro-Hypoxic miRNAs as Tools to Monitor Patients with Diffuse Gliomas. *Int. J. Mol. Sci.* **2022**, *23*, 6042. https://doi.org/10.3390/ ijms23116042

Academic Editor: Nives Pe´cina-Šlaus

Received: 30 April 2022 Accepted: 25 May 2022 Published: 27 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

histological diagnosis. Some molecular abnormalities (for example, trisomy 7 associated with monosomy 10, mutation of the *TERT* promoter, biallelic deletion of the *CDK2NA* gene) alone allow us to classify the tumor into grade 4, independently of the histomorphological criteria [5].

Diffuse gliomas in adults are therefore now well characterized from a molecular point of view; however, there is still no molecular tool for monitoring these patients longitudinally, even though a low-grade mutated *IDH* tumor will invariably evolve into a higher-grade tumor [6]. It is therefore necessary to improve the care and follow-up of patients with diffuse gliomas by identifying such tools. It is acknowledged that the occurrence of the *IDH1/2* mutation remains the upstream genetic event in two diffuse glioma lineages: diffuse astrocytomas and oligodendrogliomas [3,4]. It is also known that the *IDH1/2* mutation, hypoxia leading to necrosis, and microvascular proliferation are interrelated [1,2,7,8]. *IDH1/2* mutation can control these phenomena by modulating the expression of miRNAs. Indeed, *IDH1/2* mutation is related to epigenetic modification by both DNA hypermethylation [9,10] and a change in miRNA (miRNA) expression (for review [8]). Interestingly, in lower-grade gliomas, the *IDH1/2* mutation has more of an impact on miRNA expression than histological and other genomic features [11].

By repressing transcription or inducing the degradation of their target mRNA molecules, miRNAs can control cell growth, proliferation, metabolism, and apoptosis [12]. Many miRNAs are dysregulated in gliomas and are linked to their development and progression [13,14] (for review: [15]). Among these, some appear oncogenic, such as tumor suppressor miRNAs [16] as well as miRNAs correlated with the grade and/or histology of the glial tumor and/or with the outcome of glioma patients, as reviewed in a recent meta-analysis based on the data of 4708 glioma patients [17]. For example, a high expression of miR-15b, 21, 148a, 196, 210, and 221 or a low expression of miR-106a and 124 predicts a poor prognosis in glioma patients, while the expression of miR-10b, 17, 20a, 155, 182, 200b, and 222 fails to predict such survival [17]. Among these miRNAs, some are induced by hypoxia and/or neoangiogenesis, the two hallmarks of glioma history [1], such as mir210 [18,19]. Interestingly, the detection of stable miRNA expression in cerebrospinal fluid, blood serum, and other bodily fluids has led to the possibility of using miRNAs as non-invasive biomarkers for clinical applications [20,21]. However, specific miRNAs still need to be elucidated in the diagnosis of a glioma, especially in the early screening stage [22].

As IDH genes play important roles in the mechanism of glioma, here, we tested the diagnostic and prognostic values of miRNAs, reflecting on the features of gliomas in the WHO 2016 classification and patients' survival in a series of 97 grade II to IV gliomas. We focused on ten miRNAs: has-mir-200b-3p, -200c-3p, -210-3p, -100-5p, -126-5p, -132-3p, -221-3p, -424-5p, -128-3p, and -451-5p. These are miRNAs which could be involved in the regulation of hypoxia/cell proliferation/differentiation of glioma cells, as described for other cell types [23–33] (for review: [15,34]).

#### **2. Results**

#### *2.1. Expression of the Pro-Angiogenic and/or Pro-Hypoxic miRNAs Studied Is Affected by IDH Mutation and Hypoxia In Vitro*

We first validated the influence of IDH1 mutation on proangiogenic miRNA expression by comparing the expression of these miRNAs between two isogenic HBT-14 (U-87 MG) cell lines only differing in their expression of either wild-type IDH1 (HBT-14 (U-87 MG) *IDH1WT*) or mutated IDH1 (HBT-14 (U-87 MG) *IDH1R132H*) (Figures 1 and 2). We report that four of the miRNAs studied had significantly altered expressions (has-mir-100-5p, 128-3p, -221-3p and -451-5p) when the cell line expressed the IDH1 R132H-mutant, while the others showed an expression variation that did not reach significance, although some looked to be substantial (for example, has-mir-210-3p and -424-5p). To note, the mir-126-5p was undetectable in these lines.

**Figure 1.** Expression of pro-angiogenic or pro-hypoxic miRNAs according to the presence (HBT-14 (U-87 MG) *IDH1*R132H cells) or absence (HBT-14 (U-87 MG) *IDH1*WT cells) of the IDH1 R132H mutation. The miRNAs were extracted from cell lines using miRNAeasy (Qiagen™), then retrotranscribed (RT) and amplified (PCR) using the TaqMan MiRNA Reverse transcription kit (Applied Biosystem). The RT-PCR data were normalized to the small nucleolar house-keeping RNA, RNA RNU48 (SNORD48) (assay ID 001006). Each miRNA was expressed in base 100 (100 being attributed to the delta-CT of the miRNA measured in HBT-14 (U-87 MG) *IDH1*WT cells) (*n* = 3, ANOVA followed by a post hoc Dunnett's test, \*: *p* < 0.05, \*\*: *p* < 0.01). The mir-126-5p was undetectable in these lines.

**Figure 2.** Morphological appearance of HBT-14 (U-87 MG) *IDH1WT* and HBT-14 (U-87 MG) *IDH1R132H* grown in normoxia or hypoxia (1% O2, 24 h). HBT-14 (U-87 MG) *IDH1WT* and HBT-14 (U-87 MG) *IDH1R132H* cells, as validated by immunohistochemical staining carried out according to standard procedures against *IDH1R132H* (left panel), reaching 60% confluence, were cultivated for an additional 24 h in physoxia or hypoxia (0.1% O2). The appearance of these cells was imaged under a phase contrast microscope: the right panel presents representative photos of these cells according to the culture condition (normoxia/hypoxia).

Microvascular proliferation is linked to hypoxia, a hallmark of high-grade gliomas, which is itself counterbalanced by this microvascular proliferation, although the neo-vessels formed are defective [1]. Thus, we next tested the influence of hypoxia by incubating HBT-14 (U-87 MG) *IDH1WT* and HBT-14 (U-87 MG) *IDH1R132H* cells for 24 h under 1% O2. Morphologically, we observed that the cells survived in these growing conditions, although

they appeared less numerous than their homologues cultivated in physoxia, showed a more star-shaped form, and featured more interconnections (Figure 2).

The quantification of miRNAs from HBT-14 (U-87 MG) *IDH1WT* and HBT-14 (U-87 MG) *IDH1R132H* grown in normoxia or hypoxia (1% O2, 24 h) revealed that hypoxia could interfere with the IDH1 mutation and influence miRNA expression when compared to cells grown in normoxia (Figure 3).

**Figure 3.** Expression of pro-angiogenic or pro-hypoxic miRNAs in HBT-14 (U-87 MG) *IDH1*R132H or in HBT-14 (U-87 MG) *IDH1*WT cells grown in hypoxia (1% O2, 24 h). HBT-14 (U-87 MG) *IDH1*WT and HBT-14 (U-87 MG) *IDH1*R132H cells at 60% confluence were cultivated for an additional 24 h in normoxia or hypoxia (0.1% O2). The miRNAs from HBT-14 (U-87 MG) *IDH1*WT and HBT-14 (U-87 MG) *IDH1*R132H cells were extracted using miRNAeasy (Qiagen™), then retrotranscribed (RT) and amplified (PCR) using the TaqMan MiRNA Reverse transcription kit (Applied Biosystem). The RT-PCR data were normalized to the small nucleolar house-keeping RNA, RNAS RNU48 (SNORD48) (assay ID 001006). Each miRNA ((**a**): mir-100-5p; (**b**): mir-128-3p; (**c**): mir-200b-3p; (**d**): mir-200c-3p; (**e**): mir-210-3p; (**f**): mir-221-3p; (**g**): mir-424-5p; (**h**): mir-451-5p) was thus finally expressed in base 100 (100 being attributed to the delta-CT of the miRNA measured in HBT-14 (U-87 MG) *IDH1*WT or HBT-14 (U-87 MG) *IDH1*R132H cells grown in normoxia) (*n* = 3, ANOVA followed by a post hoc Dunnett's test, \*: *p* < 0.05; \*\*: *p* < 0.01; \*\*\*: *p* < 0.001, NS: non-significant).

We measured significant increases in mir-210-3p and mir-424-5p in both HBT-14 (U-87 MG) *IDH1*WT and HBT-14 (U-87 MG) *IDH1*R132H when grown for 24h under 1% O2 (Figure 3). We also reported significant decreases in mir-128-3p and mir-221-5p in HBT-14 (U-87 MG) *IDH1*WT grown for 24h under 1% O2 (Figure 3), but not in IDH1-mutant cells. Therefore, hypoxia modified the expression of mir-210-3p and mir-424-5p studied here in HTB-14 (U-87 MG) cells regardless of *IDH1/2* status, unlike the mir-128-3p and mir-221-5p whose variation in expression under hypoxia seems to be linked by the presence of a wild-type IDH.

#### *2.2. The Expression of Pro-Angiogenic and/or Pro-Hypoxic miRNAs Is Strongly Affected in Patients with Glioma*

The characteristics, treatment history, and pathologic data pertaining to 97 glioma samples from the 97 patients studied are summarized in Table S1. The median age was 53.9 years [range: 22.3–79.3]. There were 37 females and 60 males. The median followup period was 28.85 months [range: 0.26–304.23 months]. According to the 2016 WHO classification [2], the 97 glioma samples were classified as follows: 11 Grade II, isocitrate dehydrogenase 1/2 (IDH)-mutant and 1p19q-codeleted oligodendrogliomas (O); 16 Grade III, IDH-mutant and 1p19q-codeleted anaplastic oligodendrogliomas (AO); 18 Grade II, diffuse and IDH-mutant astrocytomas (A-IDHMUT); 7 Grade III, IDH-mutant anaplastic astrocytomas (AA-IDHMUT); 8 Grade IV, IDH-mutant glioblastomas (GB-IDHMUT); 37 Grade IV, IDH-wild-type glioblastomas (GB-IDHWT) (Table S1).

We first assessed the level of expression of has-miR-200b-3p, -200c-3p, -210-3p, -100-5p, -126-5p, -132-3p, -221-3p, -424-5p, -128-3p, and -451-5p between glial tumors regardless of their WHO classification. We observed that the expression of each miRNA was modified by at least >2 fold (increase or decrease) as compared with non-tumoral brain tissue in 62.8 to 84.5% of patients with glioma according to the concerned miRNA (Figure 4).

**Figure 4.** The expression of pro-angiogenic or pro-hypoxic miRNAs varied markedly in patients with glioma. miRNA from the 97 FFPE surgically resected tumor specimens or the 8 healthy brain tissues were extracted using miRNAeasy-FFPE kit (Qiagen™), then retrotranscribed (RT) and amplified (PCR) using the TaqMan MiRNA reverse transcription kit (Applied Biosystem). The RT-PCR data were normalized to the small nucleolar house-keeping RNA, RNAS RNU48 (SNORD48) (assay ID 001006). Results are expressed as the fold change in glioma samples ± ESM compared to healthy brain tissue.

We report that three miRNAs (mir-221-3p, mir-132-3p, and mir-128-3p, histograms in red in Figure 4) were decreased in glioma specimens compared to normal brain tissue, while the seven other miRNAs studied were all increased (histograms in green in Figure 4). We also observed that two miRNAs (mir-210-3p and mir-451-5p) varied markedly between different subtypes of glial tumors (Figure 4). In the same patient's tumor specimen, the changes in miRNAs expression seen were not exclusively increases or decreases; some miRNAs increased when the others decreased. There was also no change in miRNA exclusive to the other miRNAs which was expected since these miRNAs reflect interrelated features in gliomas. We even report that, as detailed in Table 1, these miRNA expressions appeared to be strongly correlated with each other, except for mir-128-3p and mir-210-3p.

**Table 1.** Inter-miRNA correlation (Spearman rank correlation coefficients).

Correlations were highly significant for all miRNAs (*p* < 0.001), except mir-128-3p and mir-210-3p (*p* = 0.45).

Indeed, as seen in Table 1, the Spearman rank correlation coefficient testing the interaction between each pair of miRNAs varied from 0.344 to 0.822 and was strongly significant (*p* < 0.001) except for mir-128-3p and mir-210-3p (Spearman rank correlation coefficient: 0.078, *p* = 0.45).

#### *2.3. The Expression of Pro-Angiogenic and/or Pro-Hypoxic miRNAs Is Correlated with the 2016 WHO Classification*

Having observed that the expression of microRNAs was affected in patients with glial tumors (Figure 4), we next investigated which feature of glioma could have influenced the expression of the miRNAs studied here. We thus looked at the miRNA expression according to (1) the mutational status of IDH (Table 2), (2) the microvascular proliferation (Table 3), and (3) the histological subgroup (Table 4).

The 100 value was attributed to the miRNA expression in non-tumoral tissue. IDH: isocitrate dehydrogenase.

**Table 3.** MiRNA expression according to microvascular proliferation.

The 100 value was attributed to the miR expression in non-tumoral tissue.

**Table 4.** MiRNA expression according to the WHO 2016 classification.

A value of 100 value was attributed to the miR expression in non-tumoral tissue. A: diffuse astrocytoma, IDHmutant; AA: anaplastic astrocytoma, IDH-mutant; AO: anaplastic oligodendroglioma, IDH-mutant, and 1p19qcodeleted; GB-IDHMUT: glioblastoma, IDH-mutant; GB-IDHWT: glioblastoma, IDH-wild-type; IDH: isocitrate dehydrogenase; O: oligodendroglioma, IDH-mutant, and 1p19q-codeleted; WHO: World Health Organization.