**3. Risk factors**

There are multiple risk factors that contribute to escalating incidence of melanomas in humans (Table 1). Ultraviolet (UV) radiation especially UVA (315-400 nm) and UVB (280-315 nm) from sunlight is an important contributing factor for melanoma progression. A study by Glanz et al [5,12] revealed that 90% of all melanomas are attributed to exposure to ultraviolet radiation.

The damaging effects of UV radiation (UVR) is on account of direct cellular damage and alterations in immunologic functions. UVR causes DNA damage (by formation of pyrimidine dimers), gene mutations, oxidative stress, immunosuppressive and inflammatory responses. All these effects play an important role in photoaging of the skin and predispose to skin cancer [13]. UVR creates mutations in p53, a key tumor suppressor gene that plays an important role in DNA repair and apoptosis. Thus if p53 is mutated, the cells lose the DNA repair process leading to the deregulation of apoptosis, expression of mutated keratinocytes and initiation of skin cancer [13,14]. Darker skinned individuals have lower incidence of cutaneous melanoma primarily as a result of increased epidermal melanin. Studies indicate that epidermal melanin in African-American individuals filters twice as much UVB radiation than in Caucasians. This is on account of the larger, more melanized melanosomes located in the epidermis of dark skin individuals that absorb and scatter more light energy than the smaller, less melanized melanosomes of white skin. The incidence rate of skin cancer (both melanoma and nonmelanoma) has increased significantly in the last decade [15]; particularly among young women. For most individuals, exposure to UVR from the sun is the main source of skin cancer. Nonetheless, some individuals are exposed to high UV doses through artificial sources – sunbeds and sunlamps used for tanning purposes. Indoor tanning is widespread in most developed countries in Northern Europe, Australia and the United States [16]. Intense early sunburns and blistering sunburns are closely associated with melanoma development [17,18, 19]. Statistics indicate that one severe childhood sunburn is associated with a two-fold increase in melanoma risk [20]. Chronic UV exposure results in increased skin aging, wrinkles, uneven skin pigmentation, loss of elasticity and a distribution in the skin barrier function [21]. Chronic UVR exposure is an important risk factor in the development of actinic keratosis (precursor of SCCs).

#### **Ultraviolet (UV radiation)** UVA UVB

#### **Genetic syndromes**

Xeroderma pigmentosum Oculocutaneous albinism Basal cell nevus syndrome

#### **Ionizing radiation**

X-rays

#### **Other risk factors**

Artificial UV radiation (tanning) Smoking Color of skin (having fair skin, especially with blue or hazel eyes) History of precursor lesions Chronically injured or non-healing wounds Working outdoors Increasing age

**Table 1.** Risk factors for skin cancer [1-8]

among individuals with albinism and Xeroderma Pigmentosum. These disorders can be

**b.** Squamous Cell Carcinoma (SCC): This cancer begins in the squamous cells that form the surface of the skin, lining of hollow organs of the respiratory and digestive tracts. The earliest form of SCC is called as actinic keratosis (AK) [2] that appear as rough, red bumps on the scalp, face, ears and back of the hands. The rate at which the bumps (keratosis) invade deeper in the skin to become fully developed squamous cell carcinoma is estimated to be 10-20% over a 10 year period. Actinic keratinosis that becomes thicker and more tender could increase the possibility of getting transformed to an invasive squamous cell carcinoma phenotype. The most important risk factor is sun exposure. Lesions appear after years of sun damage in the forehead, cheeks as well as the backs of hands. Other minor factors like exposure to hydrocarbons, arsenic, heat or X-rays could predispose to

SCCs. Unlike BCC, SCCs can metastasize to other parts but are easy to treat.

the progression of melanoma, the cancer gains metastatic potential.

**3. Risk factors**

44 Highlights in Skin Cancer

to ultraviolet radiation.

**c.** Melanoma: This is the cancer of the melanocytes, the "skin-color producing cells" of the body. An estimated 132,000 new cases of melanoma occur worldwide every year [5,6,7] with approximately 65,161 deaths according to estimates from the World Health Organ‐ ization (WHO). The high mortality rate of melanoma is remarkably high considering the fact that melanoma is nearly always curable in its early stages; the high mortality rate can be attributed to late diagnosis in which the cancer spreads to other parts of the body [5]. Melanoma incidence has increased more rapidly than that of any other cancer, yet our ability to treat disseminated disease has been lagging [8,9,10]. The predicted 1 year survival for Stage IV melanoma ranges between 41% to 59% [11]. At a very early point in

There are multiple risk factors that contribute to escalating incidence of melanomas in humans (Table 1). Ultraviolet (UV) radiation especially UVA (315-400 nm) and UVB (280-315 nm) from sunlight is an important contributing factor for melanoma progression. A study by Glanz et al [5,12] revealed that 90% of all melanomas are attributed to exposure

The damaging effects of UV radiation (UVR) is on account of direct cellular damage and alterations in immunologic functions. UVR causes DNA damage (by formation of pyrimidine dimers), gene mutations, oxidative stress, immunosuppressive and inflammatory responses. All these effects play an important role in photoaging of the skin and predispose to skin cancer [13]. UVR creates mutations in p53, a key tumor suppressor gene that plays an important role in DNA repair and apoptosis. Thus if p53 is mutated, the cells lose the DNA repair process leading to the deregulation of apoptosis, expression of mutated keratinocytes and initiation of skin cancer [13,14]. Darker skinned individuals have lower incidence of cutaneous melanoma primarily as a result of increased epidermal melanin. Studies indicate that epidermal melanin in African-American individuals filters twice as much UVB radiation than in Caucasians. This

linked to either instability of the skin or diminished pigmentation.

**Gene Protein Function Commentary References**

*CDH2* N-cadherin Cell-cell adhesion Melanoma invasiveness [144,145]

*SPP1* Osteopontin Inductor of MMP Risk of metastasis [144,147] *SPARC* Osteonectin Angiogenesis Tumor progression [144] *WNT5A* Wnt5a Cell signaling Melanoma invasiveness [125,148]

*EIF2gamma* EIF2γ DNA translation Tumor progression [149]

*CDH3* P-cadherin Cell-cell adhesion Tumor progression [145,151] *CDH10* Cadherin-10 Cell-cell adhesion High risk of metastasis [144]

Acquisition of somatic mutations in key regulatory genes is the driving force behind the initiation and progression of melanoma development. For the past few decades numerous research teams around the world have researched on melanoma genetics leading to an

Approximately 8-12% of all melanomas are familial – occurring in individuals with a history of familial melanoma [24]. Two genes have been found to be associated with high pene‐ trance susceptibility – CDKN2A and CDK4. Using linkage analysis of families with high melanoma incidences, the first melanoma incidence susceptibility gene, CDK2N2A was identified at chromosome 9p21 [25,26]. The gene CDKN2A encodes two unrelated pro‐ teins – p16Ink4A and p14Arf. These proteins are tumor suppressors involved in cell cycle regulation. Numerous studies indicate that p16Ink4A inhibits G1 cyclin dependent kinase (cdk4/cdkb) mediated phosphorylation of retinoblastoma protein (pRB) resulting in cell

Cofactor DNA polymerase

Desmosomal component

**Table 3.** Genetic expression signatures associated with the progression of melanomas [24]

kinase subunit 2 Cell cycle control Poor prognosis [147]

E-cadherin Cell-cell adhesion Tumor progression [144,145,147]

damage response Melanocytic infiltration [135,136,137]

An Overview of Important Genetic Aspects in Melanoma

http://dx.doi.org/10.5772/55257

47

Genome destabilization [150]

Loss of cell adhesion [137,139,179]

ATM kinase Cell cycle control, DNA

**Upregulation** *ATM*

*MMP-1, 2* Metallo proteinases

*CKS2* Cdc28/cdk1 protein

*PCNA* PCNA

*DSC1* Desmocollin-1

overwhelming body of information.

**5. Susceptibility genes**

**Down Regulation** *CDH1*

**Table 2.** Chromosomal aberrations involving important genes found in melanoma

#### **4. Roadway to melanoma**

Malignant melanomas arise from epidermal melanocytes or the melanocyte precursor cell which are derived from the neural crest and migrate to the skin and hair follicles [22]. Mela‐ noma initiation and progression is accompanied by a series of histological changes. The five distinct changes are: 1) nevus – benign lesion characterized by an increased number of nested melanocytes; 2) dysplastic nevus – which is characterized by random, discontinuous and atypical melanocytes; 3) radial-growth phase (RGP) melanoma where the cells acquire the ability to proliferate intraepidermally; 4) vertical growth phase (VGP) melanoma – character‐ ized by melanoma cells acquiring the ability to penetrate through the basement membrane (BM) into underlying dermis and subcutaneous tissue; and 5) metastatic melanoma – charac‐ terized by the spread of melanoma cells to other areas of the skin and other organs. The most critical event in melanoma progression is the RGP-VGP transition which involves the escape from keratinocyte mediated growth control. This is consistent with tumor thickness being a strong predictor of metastatic disease and adverse clinical outcome [23].

**Table 3.** Genetic expression signatures associated with the progression of melanomas [24]

Acquisition of somatic mutations in key regulatory genes is the driving force behind the initiation and progression of melanoma development. For the past few decades numerous research teams around the world have researched on melanoma genetics leading to an overwhelming body of information.


**3. A Mexican tsunami early warning system**

As mentioned in previous sections, tsunamis (and earthquakes) are unpredictable and can happen any time. Therefore, there is a need for an effective tsunami early warning system (TEWS). A system which should include not only the technical aspect but also the human issue. This section presents a preliminary model for such a system.

In particular, it considers the Pacific and the Caribbean coasts of Mexico (Section 2). However, only those aspects associated with the "structural-organisation" of the proposed model will be discussed in some detail (i.e., the five interrelated subsystems associated with systems 1–5 and its channels of communication as shown in **Figure 6**). The proposed model is based on previous research on issues related to safety and disaster management systems [30–32].

In the context of this case study, the overall function of systems 2–5 (MTEW-SMU) is to establish the key tsunami safety policies aiming at maintaining tsunami risk within an acceptable range; this implies allocating the necessary resources, for example, to build response capabilities at national and community levels.

System 1, on the other hand, embraces the following three subsystems: TNZO (Tsunami Northern Zone Operations), TSZO (Tsunami Southern Zone Operations), and TCZO (Tsunami Caribbean Zone Operations) with their associated management units (TNZ-SMU, TSZ-SMU & TCZ-SMU). These three operations of system 1 were considered given the fact that the risk of tsunamis comes from local and remote tsunami sources as mentioned in Section 2.

Further, it is important to highlight that one of the key functions within the MTEW-SMU is that related to System 2, which is associated with what it is called here MTEW-CC (Mexican Tsunami Early Warning-Coordination Centre); its key function is the monitoring, detection of a tsunami through the following coordination centres: TSZ-CC (Tsunami Southern Zone-Coordination Centre), TNZ-CC (Tsunami Northern Zone Coordination Centre), and TCZ-CC (Tsunami Caribbean Zone Coordination Centre), as shown in **Figure 6**. The process of the flow of key information and decision making process is briefly described in **Table 3**; **Table 4**, on the other hand, presents some of the key actors involved in the existing system when compared with the features of the model.

In general, communities living in active seismic areas and along coastal regions are vulnerable to tsunamis. These natural hazards are not that common and unpredictable, but powerful and with devastating consequences to those communities in

#### **Figure 6.**

*A Mexican tsunami early warning system (MTEWS).*

their path. It is believed that tsunamis are the deadliest in terms of the proportion of people being killed [34].

Following the 2004 tsunami in the Indian Ocean, the need for a tsunami warning system (TWS) was more than evident; however, it may be argued that the existing TWS may be deficient in dealing with the mitigation of impacts of such events; moreover, there are still regions worldwide without such systems.

**201**

*The Risk of Tsunamis in Mexico*

**"Action points"**

"2&2A", "7&7A"

"3&4", "8&9", "13&14"

"4A", "9A", "14″

**Table 3.**

*DOI: http://dx.doi.org/10.5772/intechopen.94201*

**Description**

"action point" "7A".

necessary.

*Description of the key action points of the model in Figure 6.*

"1", "6", "11" Flow of data on key variables monitored by MTEW-CC through TNZ-CC, TSZ-CC,

PTWC (Pacific Tsunami Warning Centre), see **Table 4**.

decision-makers within this zone are on alert.

(i.e., TSZ-CC & TCZ-CC), see **Figure 6** and **Table 4**.

to "9A" and "14" within their respective coordination centres. "5", "6", "15″ "Action point" "5" issues the tsunami warning to the affected communities within this

zone (e.g. Acapulco, Oaxaca, Manzanillo, Zihuatanejo, etc.).

this zone (e.g. Cancun, etc.), see **Figure 6**.

TCZ-CC (e.g., earthquakes, pressure sensors, tide gauges, etc.). It should also be mentioned that this information is provided by the SSN (National Seismological Service), USGS, the

If a strong earthquake occurs, for example, within TNZO (Tsunami Northern Zone Operations), then in "2", the tsunami risk is assessed, if the key variable not withing the acceptable criteria (e.g., a tsunami), then it issues the tsunami warning to "2A", which in turn issues the warning to the TSZ-CC, even if the risk is low (Section 2), through the

In the model, the TCZ-CC also receives the warning, although in the context of this scenario, it is not necessary to warn communities within TCZO (Tsunami Caribbean Zone Operations) to take some protective actions for obvious reasons. Nevertheless, the key

"Actions points" "3&4" plan and devise measures to respond to the tsunami emergency, e.g., design of risk maps, plans to conduct drills, evacuation plans; etc. All of these aiming at better prepare the vulnerable communities within TNZO. "Action point" "3" also issues the tsunami warning to MTEW-SMU (i.e., to System 3). In the same vein, "action points" "8&9" and "13&14" perform similar functions into their respective coordination centres

Following the scenario herein, "4A" communicates the protective measures taken (e.g., evacuation) to the MTEW-CC, which in turn may devise further actions given its synergistic view of the total system through system 3, as shown in **Figure 6**. The same rationale applies

zone (e.g. B.C, B.C.S., Sinaloa, Manzanillo, etc.). Further, it implements all the protective measures to mitigate the impact of the tsunami in the coastal areas, e.g., evacuation to safe areas, etc. Moreover, it also implements plans to relocate the affected people to safe areas if

Similarly, as in "5", "6" issues the tsunami warning to the affected communities within this

"Action point" "15", issues the warning to the communities vulnerable to tsunamis within

Recent tsunami disasters have highlighted some of these deficiencies; for example, in the case of the 2010 tsunami in Chile, the entity in charge of issuing a tsunami warning failed to do so [5], p. 30 (see "action point" "2"& "7" in **Figure 6** and **Table 3**). The failure to perform this action contributed to fatalities in the coastal communities. More recently, the 28 September Sulawesi tsunami and the 24 December Anak Krakatau (AK) volcano tsunami, both in Indonesia, illustrate deficiencies in TWS too. In the former case, the tsunami warning was issued but the warning was lifted over thirty minutes [4]. However, the city of Palu, located in a narrow bay, was hit hard with waves reaching six metres of height; why were not they warned? the head of the BMKG (Indonesia Agency for Meteorology,

Climatology and Geophysics) argued that "we have no observation data at Palu…", "If we had a tide gauge or proper data in Palu, of course it would have been better" [4]. The tsunami (and earthquake) killed over 2000 people [2]. Finally, regarding the AK volcano tsunami, it is thought that there was not a tsunami warning system for the case of volcano-induced tsunamis; however, the tsunami killed 437 people [3]. It may be argued that a TWS should not be only concerned with the technical infrastructure systems (e.g., tidal gauge, network of buoys, etc.), but also the organisational and human components. Further, it may be argued that the most


International Journal of *Molecular Sciences*

## *Article ITIH5* **and** *ECRG4* **DNA Methylation Biomarker Test (EI-BLA) for Urine-Based Non-Invasive Detection of Bladder Cancer**

**Michael Rose 1,2,\*, Sarah Bringezu 1, Laura Godfrey 1, David Fiedler 1, Nadine T. Gaisa 1, Maximilian Koch 1, Christian Bach 3, Susanne Füssel 4, Alexander Herr 5, Doreen Hübner 4, Jörg Ellinger 6, David Pfister 3,7, Ruth Knüchel 1, Manfred P. Wirth 4, Manja Böhme 5 and Edgar Dahl 1,2,\***

Received: 16 December 2019; Accepted: 5 February 2020; Published: 7 February 2020

**Abstract:** Bladder cancer is one of the more common malignancies in humans and the most expensive tumor for treating in the Unites States (US) and Europe due to the need for lifelong surveillance. Non-invasive tests approved by the FDA have not been widely adopted in routine diagnosis so far. Therefore, we aimed to characterize the two putative tumor suppressor genes *ECRG4* and *ITIH5* as novel urinary DNA methylation biomarkers that are suitable for non-invasive detection of bladder cancer. While assessing the analytical performance, a spiking experiment was performed by determining the limit of RT112 tumor cell detection (range: 100–10,000 cells) in the urine of healthy donors in dependency of the processing protocols of the RWTH cBMB. Clinically, urine sediments of 474 patients were analyzed by using quantitative methylation-specific PCR (qMSP) and Methylation Sensitive Restriction Enzyme (MSRE) qPCR techniques. Overall, *ECRG4*-*ITIH5* showed a sensitivity of 64% to 70% with a specificity ranging between 80% and 92%, i.e., discriminating healthy, benign lesions, and/or inflammatory diseases from bladder tumors. When comparing single biomarkers, *ECRG4* achieved a sensitivity of 73%, which was increased by combination with the known biomarker candidate *NID2* up to 76% at a specificity of 97%. Hence, *ITIH5* and, in particular, *ECRG4* might be promising candidates for further optimizing current bladder cancer biomarker panels and platforms.

**Keywords:** bladder cancer detection; urinary biomarkers; DNA methylation; ECRG4; ITIH5

## **1. Introduction**

Bladder cancer is the most frequent urogenital malignant tumor concerning both sexes worldwide, with an estimated ~549,400 new cases and 200,000 deaths in 2018 [1], which causes the highest costs of all cancers per patient [2]. In the European Union (EU) alone, the costs were €4.9 billion, with health care accounting for €2.9 billion in 2012 [3] due to long-term survival with the need for lifelong surveillance by cost-intensive diagnostically tools [2]. Cystoscopy, the "gold standard" for the detection of bladder cancer, is an invasive and time-consuming procedure, achieving an operator-dependent sensitivity and specificity of approximately 90% [4]. In particular, repeating cystoscopy for patients with non-muscle invasive bladder cancer (NMIBC) to determine whether their disease has recurred or progressed to muscle invasive bladder cancer (MIBC) represents a major cost associated with treating bladder cancer patients [5]. Nevertheless, only 10% of haematuria patients are faced with a diagnosis of bladder cancer [6], a fact that did not increase the compliance of undergoing cystoscopy. Complementary to these procedures, the current guidelines recommend completion by non-invasive urine cytology, which, however, is characterized by poor sensitivity varying between 20 to 53% [7]. Additional non-invasive urinary assays have been developed, which could help to minimize the invasive procedure of cystoscopy and reduce its economic burden (for an overview see: [8]). Although such assays have been shown to increase the sensitivity of urine cytology, they have not been widely adopted in routine practice: either they are characterized by cost-intensive performances, like UroVysion [9], or failed as point of-care tests due to limited sensitivity or specificity, such as the NMP22-based "BladderCheckTM Test" [10,11]. Given that, none of the currently available urinary biomarkers that have been approved by the FDA can absolutely be recommended as a stand-alone test to replace cystoscopy in the clinic. Recently, several commercially available tests have been developed with improved sensitivity and specificity by using mRNA (e.g., "Xpert BC" [12]) or protein-based ELISA assay technology (e.g., "UBC" [13]), but these data must be independently be confirmed in further studies. Therefore, it is still of great interest to identify novel tumor biomarkers for urine-based early detection of bladder cancer, which might optimize existing panels and platforms to improve both the initial detection of bladder cancer and detection of its recurrence.

For several decades now, epigenetic alterations are an excellent source of biomarker candidates for cancer detection, diagnosis, and prognosis [14]. In particular, aberrant DNA hypermethylation of putative tumor suppressor genes emerged as a potential biomarker source for assessing early cancer detection, which has recently moved towards clinical practice, for instance, in colorectal cancer [15]. For non-invasive detection of bladder cancer, promising DNA methylation biomarkers have been described in various studies [16], but the FDA has approved none of the presented methylation biomarkers (panels) for routinely diagnostic procedures so far. In the presented study, we focused on two putative tumor suppressor genes in bladder cancer that may also hold a prognostic impact, namely interα-trypsin inhibitor heavy chain 5 (*ITIH5*) and esophageal cancer-related gene 4 (*ECRG4* or *C2orf40*). *ITIH5* has previously been shown to be epigenetically silenced in various cancer entities [17–19], including bladder cancer [20], where its expression was associated with tumor recurrence of the clinical important group of high-grade pT1 patients. In addition, *ITIH5* was characterized as a putative metastasis suppressor gene in breast [21,22] and pancreatic cancers [23].

We now provide evidence that *ECRG4* and *ITIH5* DNA methylation could be useful as urinary biomarkers for non-invasive bladder cancer detection. Biomarkers were assessed by comparing di fferent techniques, i.e., bisulfite-pyrosequencing, qMSP, and MSRE qPCR in comprehensive cohorts of patients with bladder diseases and controls, overall composing 474 urine samples, including a significant number of benign and inflammatory diseases. In particular, we demonstrate strong biomarker performance for *ECRG4*, which might be a suitable candidate to complete and improve current non-invasive biomarker panels and platforms.

## **2. Results**

#### *2.1. Analytical Performance of ITIH5 and ECRG4 qMSP and Pyrosequencing Biomarker Assays*

*ITIH5* and *ECRG4* have been previously identified as putative class II tumor suppressor genes, which are epigenetically silenced in various tumor entities. In the presented study, we aimed to assess the biomarker quality of both candidates to detect bladder tumors *via* urine samples. The analytical performance of quantitative methylation-specific PCR (qMSP) and pyrosequencing assays was assessed involving standard biobank processing procedure of the RWTH cBMB biobank prior to assessing the biomarker performance of ECRG4 and ITIH5 by patient materials.

For *ECRG4*, Figure 1A shows the genomic location of the qMSP and pyrosequencing assays. Both of the assays spanned CpG sites, which were characterized by strong median hypermethylation in bladder tumors within the TCGA data set [30] as compared to normal controls. CpG sites of the *ECRG4* target region showed a significant inverse correlation between DNA methylation and *ECRG4* mRNA expression (Figure 1A). Subsequently, a spiking experiment was performed to assess both sensitivity and reproducibility in dependence on a distinct number of RT112 tumor cells (range: 100–10,000 cells), i.e., RT112 bladder cancer cells harboring methylated *ECRG4* (see Supplementary Figure S1) and *ITIH5* [20] genes were spiked into 20 mL pooled urine of healthy donors (*n* = 4), respectively. Urine samples that were spiked with RT112 cells were processed according to the standard operating protocol of the RWTH cBMB. Urine pellets were either directly used for DNA extraction (probe set A) or urine sediments were stored according to the RWTH cBMB conditions (−80 ◦C) for two weeks (probe set B) (Figure 1B). We found that DNA yield was significantly higher in freshly processed samples when compared to those processed after two weeks of storage at −80 ◦C (probe set B, Figure 1C). However, this was not associated with significant changes in the detection sensitivity of *ECRG4* methylation by both assays, qMSP and pyrosequencing). Overall, the *ECRG4* qMSP assay showed the highest sensitivity with a detection limit of 25 tumor cells/ml (equivalent to 89.75 pg tumor DNA), whereas pyrosequencing-based detection of *ECRG4* methylation required 45 tumor cells/ml (161.55 pg tumor DNA) urine (Figure 1D). Furthermore, the *ECRG4* qMSP assay exhibited a robust reproducibility when comparing methylation detection rates of the two storage time points, i.e., the qMSP assay convinced with high sensitivity and strong reliability (Spearman *r*: 0.955, *p* < 0.001) (Figure 1E). In contrast, pyrosequencing missed a significant correlation of *ECRG4* methylation levels of spiking samples, which were directly used for DNA extraction and those processed after two weeks (Supplementary Figure S2).

qMSP and pyrosequencing assays for the detection of the *ITIH5* promoter methylation were established similar to *ECRG4*. In Figure 2A, the relative location of qMSP and pyrosequencing primers are indicated. TCGA BLCA data analyses confirmed differences in median DNA methylation level for the targeted region of the *ITIH5* qMSP and pyrosequencing assay, which spanned the CpG site (#10119075), showing an inverse correlation between *ITIH5* DNA methylation and corresponding gene expression (Figure 2A). Of clinical significance, *ITIH5* hypermethylation of the CG site #10119075 was associated with shorter overall survival in advanced (pT > 2) bladder tumors (Figure 2B). In concordance with the results that were observed for *ECRG4*, pyrosequencing-based detection of *ITIH5* methylation failed to perform with a suitable reproducibility when using samples of both storage time points (directly processed vs. two weeks biobank storage). The *ITIH5* qMSP assay achieved strong reliability (Spearman correlation: 0.902, *p* < 0.001, Figure 2C,D) and high sensitivity being characterized by a detection limit that ranged between 25 and 30 tumor cells/mL (89.75 pg/107.70 pg tumor DNA), whereas a robust detection of *ITIH5* methylation by pyrosequencing required at least 125 tumor cells/mL (448.75 pg tumor DNA) urine (Supplementary Figure S2). Hence, qMSP assays of both genes were selected for assessment in a clinical cohort setting.

**Figure 1.** Analytical performance of the Quantitative Methylation-Specific PCR (qMSP) *ECRG4* DNA methylation biomarker assay. (**A**) Schematic map of the human *ECRG4* gene, including the relative positions and median β-values (of normal and tumor samples) of seven CpG sites based on 450K methylation arrays of the bladder cancer (BLCA) TCGA data set and corresponding Spearman correlation between *ECRG4* methylation (colored scale bar red to white; red: high methylation, white: low methylation) and *ECRG4* mRNA expression (colored scale bar green to white; green: strong correlation, white: no correlation) for each CpG site; \*\* *p* ≤ 0.01, \*\*\* *p* ≤ 0.001. +1: *ECRG4* transcription start site (TSS). Relative position of the promoter area analyzed by bisulfite-pyrosequencing that comprises 14 single CpG sites (black dots) is shown as a black line. CpG sites analyzed by qMSP (blue lines) were indicated covering the pyrosequenced promoter region close to the TSS. (**B**) Cartoon illustrating the spiking experiment using cultured RT112 tumor cells and urine pooled from four healthy donors. Distinct cell numbers of RT112 tumor cells (100-10,000) were spiked into 20 mL urine, respectively. Afterwards, urine samples were processed according to the standard operating protocol of the RWTH cBMB biobank. Pellets of probe set A were directly used for DNA extraction while urine sediments of probe set B were stored according to the RWTH cBMB conditions (−80 ◦C) for two weeks before further processing. (**C**) Comparison of DNA concentrations (=yield) achieved of samples from probe set A (direct processing) and B (after 14 days). DNA yield was significantly higher in freshly processed urine samples. (**D**) *ECRG4* promoter methylation determined by using qMSP of spiked urines samples. Red dotted line: threshold value for positive detection; orange and blue arrow: stably exceeding the threshold (detection limit) (**E**) Correlation of *ECRG4* DNA methylation for spiked urine samples of probe set A and B.

**Figure 2.** Analytical performance of the qMSP *ITIH5* DNA methylation biomarker assay. (**A**) Schematic map of the human *ITIH5 gene* including the relative positions and median median β-values (of normal and tumor samples) of nine CpG sites based on 450K methylation arrays of the BLCA TCGA data set and corresponding Spearman correlation between *ITIH5* methylation (colored scale bar red to white; red: high methylation, white: low methylation) and *ITIH5* mRNA expression (colored scale bar green to white; green: strong correlation, white: no correlation) for each CpG site; \*\* *p* ≤ 0.01. +1: *ITIH5* transcription start site (TSS). Relative position of the upstream promoter area analyzed by bisulfite-pyrosequencing that comprises five single CpG sites (black dots) is shown as a black line. CpG sites analyzed by qMSP (blue lines) were indicated largely covering the pyrosequenced promoter region. (**B**) Kaplan–Meier survival curves display overall survival (OS) of patients with high ITIH5 methylation (β-value of CG #10119075 > 0.4, green curve) compared to low ITIH5 methylation (β-value of CG #10119075 ≤ 0.4, blue curve) based on TCGA data. (**C**) *ITIH5* promoter methylation determined by using qMSP of spiked urine samples. Red dotted line: threshold value for positive detection; orange and blue arrow: stably exceeding the threshold (detection limit) (**D**) Correlation of *ITIH5* DNA methylation for spiked urine samples of probe set A and B.

#### *2.2. Clinical Biomarker Performance of the ECRG4-ITIH5 qMSP Test for Accurate Non-Invasive Detection of Bladder Cancer*

*ECRG4* and *ITIH5* performance was initially assessed in a clinical cohort of urine samples (total *n* = 263) comprising 116 urine samples that were derived from bladder cancer patients. Patients with urological malignancies of other origin (testis, prostate, kidney) as well as benign and inflammatory urological-diseases (prostate hyperplasia, renal stones, chronic cystitis) and healthy (without pathological finding) donors that were included as controls (cohort 1a: benign - inflammatory, cohort 1b: all controls including urological cancers). *ECRG4* and *ITIH5* methylation for each urine sample is shown as the mean percentage of methylated reference (PMR) in the scatter plots of Figure 3A,B. *ECRG4* and *ITIH5* methylation were both significantly increased in the urine samples

from patients with cancers of the bladder (*ECRG4* mean PMR: 5.011, 95% CI: 2.118–7.905; *ITIH5* mean PMR: 2.634, 95% CI: 0.730–4.539), the prostate (*ECRG4* mean PMR: 1.306, 95% CI: 0.178–2.434; *ITIH5* mean PMR: 1.018, 95% CI: 0.441–1.720), and the kidney (*ECRG4* mean PMR: 0.985, 95% CI: 0.188–1.781, *ITIH5* mean PMR: 1.311, 95% CI: 0.147–2.475) when compared to healthy controls (*ECRG4* mean PMR: 0.017, 95% CI: <0.001–0.033, *ITIH5* mean PMR: 0.031, 95% CI: 0.009–0.070). In benign and inflammatory diseases, a single statistical outlier was detected, respectively, however, diagnosis had been done in a clinical setting and, thus, a true malignancy in this few cases cannot be completely excluded. No associations were found between both *ECRG4* and *ITIH5* promoter methylation and clinical-pathological characteristics, including tumor size, histological grade, age at diagnosis, and gender (Supplementary Table S1 and S2). Calculating the optimal cut-o ff value for a combined *ECRG4-ITIH5* (EI-BLA) qMSP application with robust specificity by using ROC statistics (Table 1), we demonstrated significant discrimination of bladder cancer patients from non-malignant controls (control cohort 1a) with a sensitivity of 64.3% and a specificity of 81.5% (AUC: 0.783, 95% CI: 0.716–0.850, *p* < 0.0001) (Figure 3C).

**Figure 3.** Performance of the *ECRG4* and *ITIH5* biomarker panel using qMSP technique and training cohort #1. ( **A**,**B**) Scatterplots show the PMR methylation values for *ECRG4* (**A**) and *ITIH5* (**B**) in urine sediments of urological tumors, benign lesions, inflammatory diseases and healthy samples; \*\*\* *p* < 0.001, \*\* *p* < 0.01, \* *p* < 0.05. 1a: training cohort excluding other urological malignancies 1b: training cohort including other urological malignancies. ( **C**,**D**) ROC-curve analysis illustrating *ECRG4* (red curve), *ITIH5* (blue curve) and *ECRG4*-*ITIH5* (green curve) biomarker performance based on qMSP in cohort 1a (being and inflammatory controls ( **C**)) and cohort 1b (further urological cancer entities as controls ( **D**)), *AUC*: Area under the curve.

**Table 1.** EI-BLA biomarker performance based on training cohort #1 as compared to different control groups.

Additionally, *ECRG4-ITIH5* in combination were able to distinguish bladder cancer patients from patients with neoplasms of other urological origin (control cohort 1b) with similar specificity (81.6%, AUC: 0.695, 95% CI: 0.631–0.760, *p* < 0.0001) but reduced sensitivity (50.9%) (Figure 3D). The application of both biomarker candidates in combination achieved the most reliable results as compared with single biomarker performances.

Next, the biomarker quality of *ECRG4* and *ITIH5* promoter methylation was tested and then compared to a known putative bladder cancer methylation biomarker (*NID2*) by Methylation Sensitive Restriction Enzyme (MSRE) qPCR at the independent laboratories of Biotype GmbH (Dresden, Germany). The independent urine cohort (overall *n* = 211) included 130 urines of patients that were diagnosed with primary bladder cancer as well as 81 control urines (benign lesions, inflammatory diseases, and healthy controls). *ECRG4*-*ITIH5* methylation was found to be significantly increased in the urines of bladder cancer patients (mean methylation: 10.31, 95% CI: 6.132–14.50) as compared to all control groups, i.e., benign lesions (mean methylation: 0.143), inflammatory diseases (mean methylation: 2.900), and healthy donors (mean methylation: 0.203) (Figure 4A). In this independent cohort, a close association of both *ECRG4* and *ITIH5* with increased tumor size (pTa vs. pT1-4) and age at diagnosis was determined by Fisher's exact test (Tables 2 and 3).

**Table 2.** Clinico-pathological parameters in relation to ITIH5 methylation in training cohort #2.

a Urines of cohort #2; b cut-off level MSRE = 0.22 representing >90% specificity in ROC curve statistic; c Fisher's exact test; d According to WHO 2004 classification; Significant *p*-values are marked in bold face.

**Table 3.** Clinico-pathological parameters in relation to ECRG4 methylation in training cohort #2.

a Urines of cohort #2; b cut-off level MSRE = 0.52 representing >90% specificity in ROC curve statistic; c Fisher's exact test; d According to WHO 2004 classification; Significant *p*-values are marked in bold face.

**Figure 4.** Biomarker performance of *ECRG4*, *ITIH5*, and the *ECRG4*-*ITIH5* panel assessed by an independent urine cohort (training cohort #2) and compared to *NID2* using MSRE qPCR. (**A**) Scatter plot illustrates significant increased methylation levels for *ECRG4* and *ITIH5* in urine sediments of bladder cancer compared to benign lesions, inflammatory and healthy samples; \*\*\* *p* < 0.001, \**p* < 0.05. (**B**) ROC-curve analysis illustrating *ECRG4* (red curve), *ITIH5* (blue curve) and *ECRG4*-*ITIH5* (green curve) biomarker performance based on MSRE qPCR in cohort #2. (**C**) Scatter plot showed significant increased methylation values for *NID2* in urine sediments of bladder cancer compared to benign lesions, inflammatory and healthy samples; \*\*\* *p* < 0.001. (**D**) ROC-curve analysis compares *ECRG4* (red curve), ITIH5 (blue curve), and *NID2* (orange curve) and combined ECRG4-NID2 biomarker performance based on MSRE qPCR in cohort #2. *AUC*: Area under the curve.

The ROC analyses showed that *ECRG4-ITIH5* combination achieved a sensitivity of 71.6% at a specificity of 80.2% (AUC: 0.771, 95% CI: 0.706–0.836) in a cohort that included benign lesions and inflammatory diseases of the urinary tract (Figure 4B), whereas specificity was increased up to 92% with minimally decreased sensitivity (69%) focusing on healthy controls. When comparing our single biomarkers, i.e., *ITIH5* and *ECRG4*, with the recently proposed biomarker candidate *NID2* [31], we revealed a similar biomarker performances of *ECRG4* and *NID2*, achieving a sensitivity of 73.1% (AUC: 0.780, 95% CI: 0.709–0.851) and 75% (AUC: 0.801, 95% CI: 0.729–0.873) at a specificity of 83.1%, respectively (Table 4).

**Table 4.** Biomarker performances based on training cohort #2.

According to that, *ITIH5* was characterized by reduced sensitivity (56.7%, AUC: 0.674, 95% CI: 0.592–0.755), which did not lead to improved biomarker performance when combining with ECRG4. While considering that, we were able to increase sensitivity for bladder cancer detection up to 75% with improved specificity (85.9%; AUC: 0.807, 95% CI: 0.737–0.877) in a cohort comprising benign lesions and inflammatory diseases of the urinary tract when combining *ECRG4* with the known biomarker *NID2* (Figure 4C,D). In comparison to healthy controls, the panel set of *ECRG-NID2* achieved 76% sensitivity with 97.3% specificity (AUC: 0.884, 95% CI: 0.831–0.937) (Table 4).

## **3. Discussion**

The field of liquid biopsy-based cancer detection systems is rapidly evolving, as novel (epi)genetic biomarkers have been characterized, which can be detected in biological fluids, like blood or urine, offering an easy and non-invasive application for cancer detection, prognosis, and therapy prediction. In colorectal cancer (CRC), for instance, a blood-based screening was realized by targeting Septin 9 (*SEPT9*) hypermethylation, whose Epi proColon®test has been approved by the FDA in 2016 [32]. In bladder cancer, liquid biopsy still needs improvement [33], as various molecular (epi)genetic biomarker candidates and signatures have been described, but none of those assays are FDA-approved for routine diagnostic so far.

In the current study, we present two novel biomarker candidates, *ITIH5* and *ECRG4*, which show strong potential for improving or even completing existing non-invasive biomarker panels and platforms. Already in the year 2010, Renard and colleagues identified *TWIST1* and *NID2* as putative biomarker candidates while using qMSP [31]. In the same year, Costa et. al. showed three novel gene loci, i.e., *GDF15*, *TMEFF2*, and *VIM*, whose DNA methylation could be suitable for detecting bladder cancers in urine samples [34]. Meanwhile, many more putative candidates have been presented [16], but most of the studies were characterized by small sample cohorts without taking into account crucial cohorts of non-malignant diseases like chronic inflammation. Hence, only a handful of

biomarkers such as *TMEFF2*, *NID2* and *TWIST1* meet to some degree the needed requirements and, thus, were independently studied and validated [35]. Therefore, we took great care to implement suitable steps and criteria for biomarker validation from the beginning of the study. In the first step, established biomarker assays were assessed by comparing the reliability of di fferent detection methods (qMSP and pyrosequencing) in dependency on the urine sample processing *in vitro*. Of importance, we demonstrated that the regular procedure of urine processing analogue to SOPs of the centralized biomaterial bank (cBMB) of the RWTH Aachen University (i.e., storage at −80 ◦C) did not impair biomarker detection. However, the DNA yield was considerably higher when fresh urine samples were processed. Beyond that, the qMSP technique showed both the highest sensitivity and reproducibility, which is in line with previous studies demonstrating high levels of accuracy and lower rates of false negatives as compared with other techniques [36]. In a second step, we validated the clinical performance of both biomarkers by independent cohorts and laboratories. In this setting, we included high numbers of urological benign and inflammatory diseases as controls that can be endemically and frequently found in larger population groups, thereby reflecting a much more real-world scenario. We achieved a sensitivity ranging between 64 to 72% at a specificity of over 80% by combined performance of the *ECRG4*-*ITIH5* DNA methylation biomarker panel. Importantly, we still detect approximately 50% of bladder tumors with a robust true-negative rate (>80% specificity) by including urological malignancies of other origins supporting a liquid biopsy application in the field of bladder cancer. However, a putative benefit of both biomarker candidates being further useful for future assays dealing with the non-invasive detection of other urothelial malignancies, like prostate or renal cell carcinomas, should not be excluded at this stage. Interestingly, in our independent training cohort from Dresden, *ECRG4* reached a sensitivity of over 73% as single biomarker. Hence, a suitable impact was suggested, in particular for *ECRG4*, for discriminating BPH or urocystitis from bladder tumors, which is comparable with proposed biomarker candidates, like *NID2* or *TWIST1* [33]. *CFTR, SAL3,* and *TWIST1* have been recently shown to be useful for monitoring bladder cancer in a real clinical scenario, as a sensitivity of 96% was achieved by pyrosequencing in combination with urine cytology—however with low specificity (40%) [35]. In our cohort, the ECRG4-ITIH5 biomarker performance also reached over 90% sensitivity at a specificity of 40%, however, we finally focused on the best panel according to their specificity: combining *ECRG4* and *NID2* led to an increased sensitivity (76%) at a specificity of 97% when compared to healthy controls, encouraging validation studies of this biomarker setting in the future.

In view of novel diagnostic platforms, *ECRG4* and *ITIH5* could also be part of NGS-based gene signatures, which are currently considered to be at the cutting edge of the technical development of future diagnostic applications. In 2017, the multiplex bisulfite NGS-based sequencing concept "UroMark" was described achieving 98% sensitivity and 97% specificity [37]. However, this NGS assay should be confirmed in comprehensive cohorts and the usability of a 150 CpG loci comprising biomarker assay for routinely and cost-e ffective diagnosis, in particular as a population-based screening tool, in a real-world scenario must be further considered. So far, real-world application of available urinary markers has not reduced any bladder cancer treatment costs, as predicted by decision-analytic economic models [2]. Still, biomarkers are missing, which serve as the basis for decision-making of risk stratification. According to that, DNA methylation of our markers, in particular *ITIH5,* might hold a prognostic impact, as both candidates have been characterized as putative tumor suppressor genes whose silencing could be triggered by DNA promoter hypermethylation [20]. In 2008, *ITIH5* was described to be epigenetically silenced in breast cancer [17] and five years later *ITIH5* DNA methylation has been identified as a putative blood-based biomarker for the early detection of breast cancer [38]. Since then functionally studies revealed, for instance, ITIH5 mediated suppression of breast [21,22] and pancreatic cancer metastases [23] *in vitro* and *in vivo*. Interestingly, in aggressive mammary cancer cells, ITIH5 triggered an epigenetic reprogramming which was associated with a demethylation of various promoter regions, including that of *DAPK1*, a tumor suppressor gene and putative blood-based biomarker in several tumor entities [21]. In bladder carcinogenesis, the

downregulation of ITIH5 was also associated with worse prognosis while functionally high-grade bladder cancer cells showed reduced growth *in vitro* after ITIH5 overexpression [20]. Of clinical interest, ITIH5 protein expression was shown to predict tumor relapse of the clinical important subgroup of pT1 high-grade patients [20], of which 30% never displayed recurrence after transurethral resection of the bladder, while a further 30% died due to metastatic disease [39]. In the present study, we now confirmed a putative prognostic impact as *ITIH5* promoter hypermethylation was associated with poor patients' outcome in the subgroup of advanced tumors (pT > 2) of the TCGA bladder cancer data set, while increased *ITIH5* methylation was further shown to correlate with a higher pT status in our second urine cohort. These findings may support our hypothesis that *ITIH5* could be a useful biomarker for risk stratification, helping to monitor patients for the recurrence and/or progression of bladder tumors.

In conclusion, we provide two novel DNA methylation biomarkers for non-invasive detection of bladder carcinomas. As *ITIH5* might keep prognostic information for bladder cancer risk stratification, while *ECRG4* showed a convincing diagnostic performance, in particular in combination with the known biomarker candidate *NID2*, both biomarkers, *ECRG4* and *ITIH5*, may be promising candidates to complete and improve current biomarker panels and platforms. For instance, the "Bladder EpiCheckTM" urine assay that combines 15 DNA methylation biomarkers leading to an overall sensitivity of 68.2% and a specificity of 80.0% [40] may benefit from our biomarker candidates to reduce the number of biomarkers while also improving the overall performance. Future studies should be conducted to clarify which of our biomarkers, is suitable for which clinical application, e.g. as a guidance tool for early detection, risk stratification, surveillance, and/or therapeutic management.

## **4. Materials and Methods**

## *4.1. Cell Line*

The bladder cancer cell line J82 was originally obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). The urothelial bladder cancer cell line RT112 was used for studies of the analytical performance, a gift from Dr. Alexander Buchner (LMU München, München, Germany). All of the cell lines successfully underwent an identity check (Multiplexion GmbH, Immenstadt, Germany) prior to the experiments.

## *4.2. Urine Samples*

In total, 474 urine samples were assessed in this study. The Departments of Urology of the University Hospitals of Aachen, Bonn, and Dresden provided the voided urine samples. The samples that were collected in Aachen were obtained from the RWTH centralized biomaterial bank (RWTH cBMB). The collection of tissue samples was performed within the framework of the Biobank of the Center for Integrated Oncology Köln Bonn. All of the patients gave written consent for asservation and analysis of their samples according to local Institutional Review Board (IRB)-approved protocols of the Medical Faculty of RWTH Aachen University (EK 206/09, 05 Jan 2010), the University of Bonn (EK 205/13, 16 Mar 2013), and the University of Dresden (EK 96032012, 15 Jul 2014). The urine samples derived from patients diagnosed with a primary bladder tumor (*n* = 246) were used to assess biomarker performance, while samples with a known second malignancy, such as prostate cancer, were excluded from this study. Urines from healthy donors (*n* = 49) and samples derived from patients with inflammatory (chronic cystitis), benign (benign prostate hyperplasia), and urological malignant diseases of other tissue origin (testicular tumors, prostate cancer, renal cell carcinoma) served as the controls (overall *n* = 179). For the characteristics of training cohort I (Aachen–Bonn) and training cohort II (Dresden) see Table 5. Unless otherwise stated, 10–20 mL of urines were centrifuged for 10 min. at 2000 × g, washed with PBS and sediments were stored at −80 ◦C.

a Only urine samples of patients preoperatively diagnosed with primary, bladder cancer (BCa, without any other malignancy) were included; b According to WHO 2004 classification; BPH: prostate hyperplasia; PCa: prostate cancer; GRT: germline tumor; RCC: renal cell carcinoma; na: not available

## *4.3. DNA Extraction from Urines*

The urine sediments of training cohort I (Aachen–Bonn) stored at −80 ◦C were subjected to DNA extraction by using the ZR Urine DNA Isolation Kit (ZR, Zymo Research, Freiburg, Germany), following the manufacturer's instructions. DNA extraction from the urine sediments of training cohort II (Dresden) stored at −80 ◦C in RLT bu ffer was performed by using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer's instructions. The DNA yield (ng/mL urine) and purity (A260/A280) were determined by using the NanoDrop (Thermo Fisher Scientific, Waltham, MA, USA). Only extractions from urines with a minimal total amount of 100 ng genomic DNA and a ratio of ≥1.5 were finally used for qMSP, pyrosequencing, and MSRE qPCR analyses.

## *4.4. DNA Bisulfite Conversion*

100 to 250 ng of the genomic DNA (training cohort I) were bisulfite-converted for 14 to 16 h by using the EZ DNA Methylation™ kit (Zymo Research) according to the manufacturer's instructions. Bisulfite-converted DNA was eluted in 20 μL of TRIS-EDTA buffer.

## *4.5. Bisulfite-Pyrosequencing*

The pyrosequencing of bisulfite-converted DNA was performed by using the PyroMark PCR Kit, the PyroMark96 ID device, and the PyroGoldSQA reagent Kit (Qiagen), as reported previously [20]. *ECRG4* and *ITIH5* pyrosequencing assays were designed by using the Pyromark Assay Design Software (Qiagen), and Supplementary Table S3 lists all of the primers. Primers and sequence of interest meet the following criteria: Based on TCGA data analyses, sequences of interest should cover promoter regions that a) are characterized by strong differences in mean DNA methylation between urothelial normal and bladder cancer samples and b) are located in important gene regulatory sequences, i.e., a statistically significant inverse correlation between *ECRG4*/*ITIH5* gene expression and the corresponding DNA methylation had to be observed. The EpiTect®PCR Control DNA Set (Qiagen) was used as the positive controls for unmethylated and methylated DNA in each run.

## *4.6. Quantitative Methylation-Specific PCR (qMSP)*

Bisulfite-modified DNA was used as a template for fluorescence-based real-time PCR amplified in an iCycler iQ5 (Biorad, Munich, Germany), as previously described [37] with slight modifications: The designed primers and probes were specific for amplifying bisulfite-converted DNA for the genes of interest (*ECRG4* and *ITIH5*) (for cycle conditions, primer sequences, and annealing temperatures, see Supplementary Table S4). The reference gene *GAPDH* was used for internal normalization. Eight calibration dilutions of *in vitro* methylated human leukocyte DNA (0.1%, 1%, 5%, 10%, 20%, 30%, 50%, 100%) and unmethylated sequence (human leukocyte DNA from a healthy donor), as well as multiple water blanks were included in each run. The gene of interest was called methylated if the cycle threshold (Ct) of at least two of three qPCR replicates for each specimen had a value of less than 45 cycles. The amount of methylated DNA (percentage of methylated reference, PMR) at a specific locus was calculated by dividing the GENE/*GAPDH* ratio of a sample by the GENE/*GAPDH* ratio of SssI-treated human leukocyte DNA and multiplying by 100, as specified [37]. The primer binding sites of the qMSP assays were located in the same genomic promoter region as covered by pyrosequencing. The efficiencies of real time MSP were calculated according to the equation: E = 10[−1/slope of calibration dilutions] [41] and the mean efficacy of ECRG4 and ITIH5 qMSP was 76.57% and 77.76%, respectively.

#### *4.7. Methylation Sensitive Restriction Enzyme qPCR (MSRE) qPCR*

Isolated genomic DNA (125 ng) was used for double restriction digest. Methylation-sensitive restriction enzymes AciI and HpaII (New England Biolabs, NEB, MA, USA) were selected based on their capacity to distinguish methylated from unmethylated DNA sequences. Two independent digestion reactions (test reaction and control) were prepared for each patient DNA. Restriction digest was performed within a total volume of 25 μL in CutSmart Buffer (NEB) for 1 h at 37 ◦C and followed by heat inactivation for 20 min. at 80 ◦C. The control samples were treated in the same way but without the addition of the enzymes, 50% glycerol was added instead. Finally, DNA digest was diluted with 1x TE buffer before MSRE qPCR. The designed primers and probes for MSRE qPCR are specific for amplifying unrestricted DNA for the genes of interest (ECRG4, ITIH5, and NID2).

MSRE qPCRs were carried out while using the Roche LightCycler 480 II Real-Time PCR detection system. Mono color hydrolysis probe detection (FAM) was used. All of the samples were done in duplicate in 25 μL reactions containing 5 μL Reaction Mix B (Biotype GmbH, Dresden), 3 U Multi Taq 2 (Biotype), 1.5 μL primers and probes (5 μM each), nuclease-free water (Biotype), and 2 μL of digested DNA (2.5 ng/μL, test or control template). For *ECRG4* and *NID2,* the addition of Combinatorial Enhancer Solution (1× CES, [42]) was necessary due to the very high GC content of the amplified region.

For cycle conditions, primer sequences and annealing temperatures, see Supplementary Table S5. Ct values were analyzed while using LightCycler 480 Software (Ho ffmann-La Roche AG, Basel, Switzerland).

Undetected Ct values were normalized to 47 for calculations. The methylation level of the amplified region was calculated by using the following equation: percent methylation = 100 × 2−ΔCt, where ΔCt is the average Ct value from the test reaction minus the average Ct values from the control reaction. Methylation values exceeding 100% were set to 100%.

## *4.8. Analytical Assay Performances*

RT112 wildtype bladder cancer cells harboring a methylated *ECRG4* and *ITIH5* promoter were cultured for two weeks. After cell counting RT112 cells were spiked into pooled urine of healthy donors (*n* = 4). Serial dilutions (10 to 10.000) of RT112 cells were added to 20 mL pooled urine, respectively, which was subsequently processed at the RWTH cBMB laboratories according to its standard operating protocol. Afterwards, urine pellets were either directly used for DNA extraction (probe set A) or urine sediments were stored according to the RWTH cBMB conditions by using two-dimensional (2D) barcoded LVL tubes (LVL technologies, Crailsheim, Germany) at −80 ◦C for two weeks (probe set B). Pooled urines without any spiked RT112 cells served as the control for normalization and threshold calculation by defining methylation cut-o ffs. Next, DNA was bisulfite-treated, as mentioned earlier, and the *ECRG4* and *ITIH5* qMSP as well as pyrosequencing assays were performed for *ECRG4* and *ITIH5*, respectively. The gene of interest was called methylated if the PMR (qMSP) or mean percent of CpG methylation (pyrosequencing) stably exceed the background noise and certainly maintained this threshold (=cut-o ff).

## *4.9. TCGA BLCA Data Set*

Infinium HumanMethylation450 BeadChip data (level 2) and RNASeqV2 data (level 3) of the tumor and normal tissue samples were obtained from the TCGA data portal [30] and analyzed, as previously described [43].

## *4.10. Statistical Data Acquisition*

Two-sided p-values that were less than 0.05 were considered to be significant. The non-parametric Mann–Whitney U-test was applied in order to compare two groups, whereas, in the case of more than two groups, the Dunn's multiple comparison test was used. Correlation analysis was performed by calculating a non-parametric *Spearman's rank* correlation coe fficient. Statistical associations between clinico-pathological parameters and DNA methylation of *ITIH5* and *ECRG4* were determined by Fisher's exact test by using SPSS software version 25.0 (SPSS Inc., Chicago, IL, USA). Survival curves for overall survival (OS) were calculated using the Kaplan–Meier method with log-rank statistics. OS was measured from surgery until death and it was censored for patients alive without evidence of death at the last follow-up date. The receiver operating characteristics (ROC) curves and AUC values were calculated to assess the biomarker performance of *ECRG4* and *ITIH5* methylation similar to our previous report [43]. The ROC curves of combined biomarkers were based on the binary logistic regression model using the probability as test variable.

**Supplementary Materials:** Supplementary materials can be found at http://www.mdpi.com/1422-0067/21/3/1117/ s1.

**Author Contributions:** Conceptualization, M.R., N.T.G., M.B. and E.D.; Methodology, M.R., S.B., L.G., D.F., M.K., A.H., M.B.; Software and Statistics, M.R.; Resources, C.B., S.F., D.H., J.E., D.P.; Writing—Original Draft Preparation, M.R.; Writing–Editing, S.F. and M.B.; Writing—Review, all authors; Visualization, M.R.; Supervision, M.R., R.K., M.B., M.P.W. and E.D. All authors have read and agreed to the published version of the manuscript.

**Acknowledgments:** The authors appreciate the excellent technical support of Sonja von Sérenyi. Genomic 450K methylation and RNA seq. data used in this study were provided by the TCGA Research Network BLCA datasets (http://cancergenome.nih.gov). This work was supported by a grant from the Medical Faculty of the RWTH Aachen University (START program project 149/08).

**Conflicts of Interest:** M.R. and E.D. have been inventors of a patent application (Biomarker for Bladder Cancer. US Patent App. 14/389,393, 2016) that was later dropped by RWTH Aachen University for reasons of costs. All other authors declare that they have no conflict of interest.


**1. Introduction**

Problems related to petroleum resource depletion and the potential dangers of microplastics are continuously raised, and the need for eco-friendly materials continues to rise. The consumption of reusable or biodegradable natural polymers is essential, especially when taking raw materials into account. Since ancient times, natural fibers, such as wool, silk, and cotton, have been used in clothing, and cellulose, which is a wood-based polymer, has been actively employed in the paper industry. In addition, since the inception of FRPs, studies using natural fibers as reinforcing agents have been repeatedly reported.

Natural fibers are not only abundant and inexpensive, but are also biodegradable due to their nature-derived properties. Therefore, researchers have examined their application in FRP reinforcement [1]. However, the hydrophilic properties of natural fiber reduce compatibility with synthetic polymer matrices. Chemical modification of natural fiber surfaces has been attempted to improve the compatibility between natural fibers andmatrix polymers [2]. Although, this surface modification process mostly inhibits the economical and environmentally-friendly properties of natural fibers.

Nanocellulose is a nanomaterial obtained from wood and agricultural byproducts [3–6], as well as from bacteria [7,8], and has a diameter numbering tens of nanometers [9]. This nanomaterial has low density, high strength, and excellent transparency [10]. As a result, research is being performed to utilize nanocellulose in various applications ranging from food packaging materials, composite-material nanofillers, electronic devices, and biomaterials [11]. Due to the nanomorphology of nanocellulose and the resulting strong mechanical strength, composite materials with various general-purpose polymers, such as polyolefin and polyester [9], are also being developed [12]. In addition, as biodegradable polyesters such as polylactic acid (PLA), polycaprolactone (PCL), and polybutylene

**Citation:** Bang, J.; Lee, H.; Yang, Y.; Oh, J.-K.; Kwak, H.W. Nano/Micro Hybrid Bamboo Fibrous Preforms for Robust Biodegradable Fiber Reinforced Plastics. *Polymers* **2021**, *13*, 636. https://doi.org/10.3390/polym1304 0636

Academic Editor: José Miguel Ferri

Received: 4 February 2021 Accepted: 18 February 2021 Published: 20 February 2021

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

**Copyright:** © 2021 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/).

succinate (PBS) were developed, studies to apply nanocellulose to compensate for the insufficient mechanical strength were also actively conducted [7]. However, nanocellulose also has hydrophilic properties similar to natural fibers and wood-based biomass, which are their raw materials. Thus, insufficient compatibility with a polymer matrix is considered a limitation [13]. Biomimetic methods have been tried to overcome this drawback. When bacteria biosynthesized nanocellulose, a water-soluble polymer, such as hydroxyethyl cellulose or polyvinyl alcohol, was introduced to prepare a nanocellulose/polymer composite [7,14]. These biomimetic nanocellulose/polymer composites showed higher tensile properties than composites prepared by simple mixing [12]. However, this method has the disadvantage of interfering with the cellulose biosynthesis of bacteria, and mass production is impossible [15,16].

Recently, attempts have been made to use nanocellulose as a "green" binder in the manufacture of preforms for hierarchical composites. Lee et al. used bacterial cellulose as a binder to prepare sisal fiber preforms for the reinforcement of polyacrylated epoxidized soybean oil (AESO) [16]. As a result, it is evident that the mechanical properties of the preform improved significantly. The mechanical properties of poly AESO/sisal preforms manufactured by thermal curing following vacuum-assisted resin injection are also extensively improved. It is known that the excellent binding and adhesion ability of these nanocelluloses can be clearly expressed in natural fibers, including sisal and silk fibers [17,18]. However, most of the research results indicate that bacterial cellulose was used as a binder [19], and there has been no effort to manufacture a hierarchical fibrous-composite preform using microfibers and nanocellulose produced from the same tree species.

Bamboo is a plant resource that is cultivated worldwide [20], especially in Asia and South America. Since ancient times, bamboo has been steadily used in furniture and construction materials due to its rigid mechanical properties [20]. In addition, as bamboo fiber extraction technology advances, attempts to use it as a sustainable raw material for high-performance composite materials and advanced carbon materials are steadily progressing. [21–23]. Furthermore, various types of nanocellulose manufacturing technologies utilize bamboo fibers as raw materials [24–26]. Therefore, in the development of eco-friendly composite materials, it is very attractive to develop a facile preforming process using the hierarchical nano/micro morphology of bamboo fiber [27,28].

This study's primary purpose is to fabricate a fibrous composite preform with improved mechanical properties through a multiscale, hybridizing process using bambooderived microfibers and cellulose nanofibers. First, the optimal preparation conditions (temperature and time) were established to minimize the energy consumption required to manufacture the fibrous composite preform. The physicochemical properties of the prepared micro/nano bamboo-fibrous composite preform were analyzed according to the nanocellulose content, and the changes in mechanical properties were confirmed accordingly. Finally, FRP was prepared using PBS, which is an actual biodegradable polyester, and changes in mechanical properties of FRP due to the reinforcing effect of nano/microfiber preforms were observed.


*Edited by Luis Loures*

Ongoing landscape transformation worldwide has raised global concerns and there is a need to rethink landscaping to protect the environment. This is especially true for previously developed sites, currently abandoned or underused. Instead of consuming green lands, these derelict landscapes need to be redeveloped and given new life, enabling their transition to an increasingly sustainable urban setting. In this scenario, the present book, considers a set of subjects that highlight the diverse nature of the scientific domains associated with landscape reclamation, emphasizing the need to acknowledge that the contribution of each sustainability dimension is equally important. This will offer complementary development opportunities, while enabling redeveloped landscapes to fulfill multiple functions in an integrated way and underline the relevance of multifunctionality to promote sustainable landscape reclamation, planning, and development.

Published in London, UK © 2020 IntechOpen © GaudiLab / iStock

Landscape Reclamation - Rising From What's Left

Landscape Reclamation

Rising From What's Left

*Edited by Luis Loures*


**7. Conclusions**

The iSGECs generated in our study represent a preliminary model system for the development of therapies targeting salivary gland dysfunction. We have demonstrated that iSGEC-nSS2 cells derived from a sicca female patient retained the ability to form spheroids with differentiated cell types at late passages (p-80) and exhibited a significant proliferation capacity (>100 passages) when cultured as a monolayer. Overall, iSGECs could be used as an alternative to currently available cell culture models in salivary gland research.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2077-0383/9/12/3820/s1, Supplementary Table S1: RT-PCR primers; Supplementary Table S2: Antibodies and dilutions used in ICC and Western blots; Supplementary Figure S1: Semi-qRT-PCR analysis of SV40Lt expression in late-passaged iSGECs; Supplementary Figure S2A: Phase contrast images of iSGEC-nSS2 p-80 single-cell colonies; Supplementary Figure S2B–G: Phase contrast images of iSGEC-pSS1 and iSGEC-nSS1 spheroids; Supplementary Figure S3: expression of characterization markers in iSGEC-nSS2 cells by ICC.

**Author Contributions:** Experimentation, B.D.N., A.G.; conceptualization, B.D.N., J.-L.C.M., F.B.M., M.T.B.; manuscript preparation B.D.N., J.-L.C.M., F.B.M.; data analysis B.D.N., J.-L.C.M., F.B.M. All authors read and approved the final submitted version of this manuscript.

**Funding:** N/A. This work was supported by Atrium Health-Carolinas Healthcare Research Fund.

**Acknowledgments:** Salivary gland cell lines HSG and HSY were a generous gift from Bruce Baum (NIDCR, NIH). HMC-3A cells were a generous gift from Jacques E. Nör (University of Michigan School of Dentistry, Ann Arbor, MI, USA). We would like to thank our clinical research staff, Jenene Noll and Cathleen Peterson, for their help with patient recruitment, the collection of clinical data, and the collection and transport of LSG biopsy samples to the lab.

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