Similarly, SpliceSeq quantifies and compares reads covering exons, while the splicing junction approach is used to identify the change in splicing pattern. Many of these methods commonly focus on the level of splicing events instead of full-length splicing variants. Various methods are available such as MISA [66], ALEXA-Seq [67], FDM [68], rDiff [69], and rSeqDiff [70]. Genome-independent methods for the analysis of splicing variants, especially used in case of species not having sequenced reference genome or huge variation in RNA transcript (diseased condition) compared with the reference genome. To assemble and differentiate splicing variants, methods are based on transcriptome preassembled from RNA-Seq reads, i.e., transcriptome-based approach includes RSEM [71], IsoEM [72], BitSeq [73], and recently developed such as Rnnotator [74] and KisSplice [75].

#### **7.3 Functional analysis of identified genes**

Once the differentially expressed genes are revealed, there comes the need to understand the biological functions of those genes. Functional analysis of identified genes is an important part of data analysis, and it has been carried out at multiple levels such as biological pathways, gene ontology, and gene networks. Many different tools are available for functional analysis such as DAVID, g:profiler and clusterProfiler [76, 77] used for the analysis of GO and biological terms, GSEA [78], which

**Table 7.** *Tools for annotation.*

**Table 8.**

*Tools for data visualization of transcriptome data.*

is used for functional analysis of the entire gene set, and IPA (ingenuity pathway analysis) for gene network analysis. The online sources for gene annotation such as OmicsBox (https://www.biobam.com/omicsbox/), Panzzer2 (http://ekhidna2. biocenter.helsinki.fi/sanspanz/), EggNOG (http://eggnog-mapper.embl.de.) are widely deployed (**Table 7**).

#### **7.4 Data visualization**

After annotation, variants can be visualized using genome browsers and visualization tools. Many RNA-Seq data visualization tools are available such as Genome Browser (https://genome.ucsc.edu/), Integrated Genome Viewer (https:// software.broadinstitute.org/software/igv/), and Jbrowse (https://jbrowse.org/ jb2/). Alternative splicing visualization tools such as Alexa-Seq, SpliceSeq, SpliceGrapher, and SpliceViewer are also available. These visualization tools help to understand the information of variants including reads, mapping reads, and annotation information such as consequences, scores, and impact of variants. To demonstrate the large changes in gene expression volcano plot can be used. In the volcano plot, each dot is a representation of a gene, whereas the x-axis and y-axis represent the log-fold change based on FPKM values and log10 (p-values), respectively (**Table 8**).

#### **8. Analyses with PacBio and NanoPore datasets**

Long read sequencing of the transcriptome is done generally to qualitatively understand the expression of the genes/transcripts in the organism. This is done by

#### *Gene Expression and Transcriptome Sequencing: Basics, Analysis, Advances DOI: http://dx.doi.org/10.5772/intechopen.105929*

understanding where the genes are localized and whether there are events such as fusions/deletions impacting the genes. Unlike Illumina or other short-read sequencing approaches, where the number of reads can be in millions, thus capturing the expression multiple times, long reads generally are in a few thousand but offer the capture of full-length transcripts depending on the libraries prepared. For starters, earlier in 2010–14, most of the expression data used in the range of 25–100bp long were either paired or unpaired. Currently, the short-read technology can sequence up to 250bp long, which essentially means that the transcripts are going to be fragmented a few times.

PacBio's IsoSeq and Nanopore's direct cDNA/amplified cDNA sequencing can capture the complete expressed transcripts in the range of up to 90Kbp, with the median being around 1400 for PacBio and 770bp for Nanopore (depending on Nanopore's preps). Generally, IsoSeq and direct cDNA capture are done to confirm the existence of long repetitive regions, gene isoforms, and gene fusions. This then aids in annotation of the genome, capturing alternative splice-sites, etc. **Figure 8** depicts the analysis to be executed in the transcriptomics datasets.

**Figure 8.**

*A complete overview of the downstream analysis to be executed for the transcriptomics datasets.*

#### **8.1 Generalized workflow for PacBio/Nanopore**

The general steps of analyses for IsoSeq are as follows:

The general steps of analyses for Nanopore are as follows:

Once the gene models or their transcript sequences are identified, the next few steps are to understand if these sequences exhibit any sort of a function. For example, are these sequences coding or non-coding, are these protein-coding transcripts, or are these long non-coding transcripts? Fortunately, there are multiple approaches to solve this issue.

One of the easiest ways to know this is to evaluate the coding potential of these transcripts. This can be done using tools such as Coding Potential Calculator, Coding– Non-Coding Index, and Coding Potential Assessment Tool. Once we know which transcripts are coding based on the results from these approaches, we can select the remaining ones and call them long non-coding RNAs.

Another approach is to convert the transcripts into peptide sequences using something like TransDecoder or Evidential Gene, which can then be used to get functional assignments from PFAM, RFAM, eggnog, InterPro databases, etc. The above approaches do not necessarily require the presence of a reference genome as recent developments/tools can directly use partial-order alignments (POAs) based approaches to generate clusters, which can be then used to derive consensus directly.

#### **8.2 Combinatorial analyses with short reads**

The consensus sequences can then be used in place of a reference genome to study the transcriptome of the organism directly, by using short-read data to quantify the expression of the transcripts. The long reads provide a qualitative expression of the organism, whereas the short reads will give the actual measure of expression due to the sheer quantity of data. Once the gene models from long reads are available, annotated, and curated approaches such as Salmon/Kallisto/RSEM, etc., can be deployed to quantify the expression using short reads based on which differential expression can

then be performed. Similarly, tools such as SQUANTI/TAMA, etc., exist, which can use short-read data to augment the long-read data by annotating with CAGE peaks, polyA sites, NMD prediction, etc., which can be used for downstream analyses (Appendix).

#### **9. Transcriptomic databases**

Transcriptomic studies provide enormous information beyond the aim of experiments, which can serve as a base for other scientific communities. This large amount of data generated through experiments may be deposited in publicly available databases. In transcriptome, it quantifies the expression of genes along with small RNA and noncoding RNAs in cells, organs, particular growth stages, or stress conditions [79, 80]. Identified information in transcriptomic studies such as differentially regulated genes under the stress condition that can be targeted in the crop improvement programs [80–82]. Presently, a lot of information is available publicly through databases for *in-silico* studies. NCBI GEO is one of the highest updated and curated databases, which provides information regarding microarray data, RNA sequences, and functional annotation [83]. In addition to the earlier discussion, there are many other databases available that account for the RNA co-expression (http://atted.jp), plant-pathogen interaction, phosphorylation sites, RNA editing events, and transcription factors.

#### **10. Validation of the RNA-seq data**

The gene expression data obtained from RNA-seq studies need to be validated experimentally. The high-throughput large datasets emanate a large number of genes, and practically validating all the relatively expressed genes has limitations. Hence, validation can be performed on small or large subsets as per the design, sampling, and tissues of the experiment. Such validation should be ideally done using the same samples subjected to RNA-seq or microarray. Quantitative real-time PCR (qRT-PCR) is the most widely used technique for the validation of gene expression on account of reliability, accuracy, and sensitivity. It is considered a mediumthroughput gene expression analysis technology and is largely used for the validation of transcriptome studies. Other relatively less deployed techniques are translational fusion reporters using reporter genes, functional assays, etc. The virus-induced gene silencing (VIGS) is an RNA interference-based technology deployed to transiently knock down the target gene expression by utilizing modified plant viral genomes. It is an emerging resourceful tool for functional validation of more number of genes [84].

The qRT-PCR remains a widely adopted mandatory technique for the validation of gene expression. Nevertheless, it holds the best with the use of the same samples as assayed for RNA-seq. This one is always well meant when other replications from the same sampling population are assayed using the qRT-PCR . The reference genes or housekeeping genes or endogenous genes whose expression is expected to be stable in a particular tissue at a given time play a critical role in the quantification of gene expression. The variables in the experiment are taken care of by the appropriate usage of reference genes in the experiment [85]. The most commonly used reference genes are 18s rRNA, GAPDH, actin, ubiquitin, elongation factor, tubulin, etc. The selection of a reference gene set is very crucial in differential expression studies as it is known that varying reference genes work in a particular tissue in a spatiotemporal

manner. The available software/tools for validation of the reference genes are delta (∆Ct), geNorm, qBASE, NormFinder, BestKeeper, and RefFinder [86]. The selection of the number of reference genes depends on the M value and V value.

Livak's 2−ΔΔCT method is the most popular method for quantifying relative gene expression using the target and reference gene Cq (quantitative cycle) or Ct (cycle threshold) used Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines to describe the information required for publishing the qRT-PCR-related data in terms of transparency, accuracy, reliability. The readers are encouraged to refer to [87, 88].

#### **10.1 Critical points for qRT-PCR validation**

#### **11. Critical factors to be considered in expression studies**

Numerous points need to be taken into account for gene expression studies. First, and the most important, factor to be considered is the sample tissues. As the expression studies have an enormous variation depending on the developmental stage and the aim of the experiment. Hence, the time of sample collection and proper storage holds crucial importance while experimenting. The selected individuals should be representative of the species and should possess strong genetic background. This will enable to extract adequate information on a large scale. Further, the isolated nucleic acid quality and quantity should be thoroughly checked before performing the NGS to get accurate outcomes. Several points need to be taken into consideration for the selection of the required sequencing platform and assembly tools/software/program to get proper and accurate results. The choice of the sequencing platform to use will influence the cost and success of the assembly process. Different types of sequencing platforms generate different types of data that can be analyzed using different assembly programs. The assembly program is very specific for the type of data to be

*Gene Expression and Transcriptome Sequencing: Basics, Analysis, Advances DOI: http://dx.doi.org/10.5772/intechopen.105929*

analyzed so the analysis pipeline should be decided before prefer sequencing. The biological factors include the selection of individuals with pure genetic backgrounds and good representatives of the species to be used for DNA isolation. The basic studies regarding the biochemical, morphological, and physiological should be known. When considering the technical factors, the computational tool has an enormous role as for the proper genome assembly, to run proper analysis process, storage. The accurate selection of the annotation program to be used is also a critical step. So that the gene/ transcript has a low copy number and must not escape from the study. The proper stringency level of the bioinformatics tools/software should be maintained for the final interpretation of the data generated. For the proper alignment of assembly and annotation of coding regions, the RNA sequencing data must be generated by extracting RNA of the same sample used.

#### **11.1 Critical points for biology consideration before the start of transcriptome sequencing**

#### **11.2 Critical parameters affecting a transcriptome**

#### **11.3 Critical points before the start of the experiment**

#### **11.4 Critical points for data accuracy**

### **12. Integration of transcriptomics with other techniques for unravelling the gene expression**

The techniques of gene expression especially the RNA-seq are widely deployed in plants, humans, and animal sciences for quantitative and qualitative profiling. The data emanated from the RNA-seq can be analyzed for differential gene expression, annotation, isoform identification, metabolic pathways, domain identification,

#### *Gene Expression and Transcriptome Sequencing: Basics, Analysis, Advances DOI: http://dx.doi.org/10.5772/intechopen.105929*

alternative splicing variant identification, insertion-deletion variations, single nucleotide variants, gene expression co-network analysis, mapping with already identified regions, or quantitative trait loci (QTLs). Along with the mentioned downstream analysis, the RNA-seq gene expression data can be generated and integrated with other techniques for better understanding of specific human or animal processing depending on the tissue, complexity, and the metabolic and biological processes. The data can be generated *de novo* (sequencing from the samples), or the available data in the publically available databases can also be mined and utilized [89].

Specific to the human and animal sciences in view of the complexities associated with the varying diseases, responses to disease conditions and healthcare medications, the following advances of RNA-seq techniques have been majorly deployed for studying the gene expression. The spatial gene expression in tissue sections retains the precise location of biological molecules in tissue samples and then can be sequenced for knowing the morphological differences. Similarly, the formalin-fixed, paraffin-embedded (FFPE) tissues and antibodies tagged with cell-surface proteins can be sequenced. For better analysis of the relative gene expression studies, RNA-seq techniques are being combined with DNA methylation, degraded RNA samples, protein, and chromatin studies for a thorough understanding of gene expression at a given time point. The circulating RNA can also be captured by using modified protocols (during the initial isolation steps from tissues) and sequenced for the identification of transcripts. In the clinical aspects of the treatment of diseases, it is essential to characterize the immune repertoire at the single-cell level. The techniques such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) combine single-cell RNA-Seq with cell surface protein analysis and facilitate analysis of cell-surface proteins. The specific region of interest can also be sequenced using an enrichment probe-based approach that can also be deployed to target the transcripts of interest called targeted enrichment RNA-seq. The understanding of alternative splice variants also forms a major application of RNA-seq in clinical research [90].

The RNA-bulk seq is a modified technique of bulk segregant analysis wherein the extreme bulks are made for the identification of QTLs and the gene expression patterns associated with the trait of interest. The RNA samples from contrasting types of tissue are bulked and sequenced called RNA-bulk sequencing, which can be combined with spatial RNA-seq for quantitating the gene expression of tissues at a given time [91].

The epi-transcriptomics pertains to the transcriptome analysis to understand the RNA modifications such as N6-methyladenosine, 5-methylcytidine, and 5-hydroxylmethylcytidine. Specific antibodies are used for precipitating the RNA (RNA immunoprecipitation (RIP)) with modifications that are then sequenced on a highthroughput platform. The Oxford Nanopore RNA-Seq can detect the modifications directly without the need for antibodies [92].

Dual-RNA seq is a persuasive method for analyzing the simultaneous gene expression patterns of the host and microorganism during their interaction. The interaction can be beneficial as has been observed in the growth-promoting microorganisms or during an infection process. The transcripts from the host and the microorganisms are concurrently captured, and the genome-wide transcriptional changes from the host as well as from the microorganism can be accessed. This technique unravels the mechanism of the beneficial organism or invading pathogen enabling the understanding of the effectors and molecular processes of host colonization. Nevertheless, the practical procedures of isolating the interactive transcriptome require specialized protocols and further bioinformatics analysis [93].

The RNA-seq datasets generated through sequencing can be utilized further for mapping the trait of interest. The genetic variants present in a particular region called expression quantitative trait loci (eQTLs) regulate the expression levels of local or distant genes and explain the variation in the gene expression. The genome-wide association studies (GWAS) results can be integrated with the eQTL data in an approach called transcriptome-wide association studies (TWAS). The gene expression levels for GWAS samples can be combined with the gene expression datasets for that trait expression (trait values) in order to identify the gene-trait associations (the involvement of that genic region/genes associated with the trait). The TWAS is a potential approach to ascertain the causal genes at the GWAS loci [94]. In addition, the differentially expressed genes can be mapped with the already known reported QTLs for a particular trait of interest. These co-localized genes increase the confidence of the study in terms of linkage with the QTL.

In the specific applications of understanding the biogenesis, development of noncoding RNA, transcription sites, or finding the binding sites of transcriptionally active RNA polymerase II (RNAPII), the Global Run-On sequencing (GRO-seq) is utilized. The GRO-seq allows the unbiased mapping of nascent transcripts. Brominated nucleotides (5-bromouridine 5′-triphosphate (Br-UTP)) are deployed for immunoprecipitation and enrichment of nascent RNA followed by cDNA conversion and sequencing [95].

#### **Appendix Example: bioinformatics pipeline of transcriptome analysis**

1.QC of the data—FastQC

2.Adapter low-quality data trimming—FastP

3.Alignment—STAR/HiSAT2

4.Alignment QC—Biotype plot, duplicate marking, strandedness identification

5.Quantification—Feature counts

6.Sample correlation and principal components—DESeq2

7.Comparative analyses—DESeq 2: Volcano plots, HeatMaps

8.Term enrichments—GO overrepresentation, reactome, and KEGG pathways

#### **1. QC of the Data—FastQC**

FastQC is generally used to judge the quality of the data based on Phred Scores. Phred Scores are negative log score that is used to assign the quality of the base that is called.

**Q = -10 log10P. The probability of the base calling increases, quality increases** FastQC gives visual confirmation that all is well with the data. GC content plots can be used as an assessment check to know if there are any contaminants present in the sequences. Weird distribution at the starting or the ending of the reads can be a signature of library artifacts or systemic biases of the sequencer, esp. Illumina.

Distribution of the bases at the starting and ending of the reads is an example of biases of the sequencer of improper library preps

#### **2. Adapter/low-quality data trimming—FastP**

Fast is a brilliant all-in-oneQC tool. It gives a summary of the data before the removal of bad regions in a read and after removing the bad regions.

Bad regions can be specified as a stretch of bases with a lower quality than expected. For example, if the average read quality is to be Q35, i.e., roughly greater 99 than 95% accuracy, but a few bases have a Q15 score, i.e., around 93% accurate, then we can remove these.

FastP can also trim off the head or tail of the reads in **Figure 2** above, do a duplicate analysis to see how much data are duplicated, and what is the common motifs present in the data.

#### **3. Alignment—STAR/HiSAT2**

Fast trimmed reads are aligned to the genome using STAR/HiSAT2, which are both splice-aware aligners. This means for higher organisms such as eukaryotes where the mRNA is formed by splicing out the introns, the aligner can try to truncate the reads partially at the exon-intron boundary and try to align starting at the next intron-exon junction.

STAR uses a sparse function to store the representation of the genome, whereas HiSAT2 stores the indices in a hierarchical linked manner to align the reads.

STAR alignment scores

A good sample would have the largest set of aligned reads mapped uniquely. A large representation of multi-mapped reads suggests rRNA contamination

#### **4. Alignment QC—Biotype plot, duplicate reads, strandedness identification**

The alignments can specify a plethora of information about the samples and the sequenced data. A few pointers to note are:

Sequencing reaction type also affects the duplicacy rates

Identified by annotating the alignments, can tell if the library prepared has captured the protein-coding genes or auxillary contamination due to failed library preparation

#### **5. Quantification—Feature counts**

Once the alignment QC is done and looks satisfactory, the next step is to use the annotation of the reference organism to quantify which genes have a heightened expression or reduced expression. This is the step that is going to help perform the following analyses. This is also the part where the effect of strandedness can be observed.

#### **6. Sample correlation and principal components**

Once the counts are obtained, the subsequent steps are to interrogate whether there are any clusters observed among the various samples.

Ideally, the correlation and principal components (as a by-product) should tell you if the replicates sequenced are clustering together. Are there any batch effects or other covariates to be adjusted for?

A simple Pearson correlation, which explores a linear relationship, would tell on a scale of -1 (no)—0 (bad)—1 (good) correlation in the sample.

PCA plots can tell which individuals here have similar sets of gene expression profile

#### **7. Comparative analyses—DESeq2: Volcano plots, heatmaps, etc.**

Depending on the experiments planned, you prepare metadata stating what the datasets represent. Are the samples differing based on treatments, populations, time points, etc.? Once the objective is understood, you tend to model the formula (called design) in DESeq2, which looks something like:

design= ~ condition + age

design= ~ batch + condition

The thing to note here is that the variable immediately after "~" is called the controlling feature, and after the "+" is the effecting feature. You want to control things such as sequencing batches, populations, genders, etc., while exploring the impact of the experiment on the condition/age, etc. In the absence of the + sign, the first feature gets explored.

Heatmap of the top "N" differential genes and volcano plot of the gene expression

Genes in red are positively regulated while in blue are negative. Genes can also be clustered hierarchically based on expression patterns to see which genes are expressing together

A volcano plot tells about the expression modulation in the context of the confidence intervals. A general threshold that people use for the confidence interval is 0.05 (adjusted p-values) or false discovery rates. This is drawn on the outcome reported by DESeq2 using the log of fold changes vs negative log of the P-adjusted values/false discovery rates.

DESeq2 is one of the approaches, which uses regularized logarithm transformation to normalize the counts. People also use variance stabilizing transform, TMM, UQ, etc.

#### **8. Term enrichments—GO overrepresentation, reactome and KEGG pathways, etc**

One of the end goals of an RNA-Seq study is to generally understand biologically what the perturbations are. These can be cell cycle disruption, increased metabolic processes, cell senescence, cell growth, increased trafficking of vesicles, etc. A few tools to perform these are: Profiler, cluster profile, etc.

Reactome, KEGG, etc., are databases that are created by understanding physiologically how the genes are arranged and flow in a pathway, or transfer them from systems for which we have already understood them.

GO/gene ontology is similarly a technique that is universally applied to the tree of life. A GO term for a gene is assigned after studying the function of the gene and then assigned a generalized role to it.

By figuring out genes that have a similar role in the expressions, and their modulations, we can try to understand what would have been the impact of these genes on the organism.

*Gene Expression and Transcriptome Sequencing: Basics, Analysis, Advances DOI: http://dx.doi.org/10.5772/intechopen.105929*

KEGG pathways. The colored circles are scaled according to the number of genes involved in the particular pathway. The q value tells the confidence of the assignment. Rich factor is a ratio of genes observed in the study/genes seen in the pathway.

In this case, ~20 genes would be involved in glutathione metabolism with a q value of 0, showing that this pathway is being impacted.

While KEGG/reactome/GOs are shown as an example, one can create their versions of databases and try to compute the impact of the genes observed accordingly.

#### **Author details**

Nakul D. Magar1†, Priya Shah2†, K. Harish3 , Tejas C. Bosamia4 , Kalyani M. Barbadikar1 \*, Yogesh M. Shukla<sup>5</sup> , Amol Phule6 , Harshvardhan N. Zala7 , Maganti Sheshu Madhav1 , Satendra Kumar Mangrauthia1 , Chirravuri Naga Neeraja1 and Raman Meenakshi Sundaram1

1 Biotechnology Section, ICAR-Indian Institute of Rice Research, Hyderabad, India

2 International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India

3 Ikigai Informatics, Vadodara, India

4 Plant Omics Division, CSIR-Central Salt and Marine Chemicals Research Institute, Bhavnagar, India

5 Faculty of Agriculture, Anand Agricultural University, Anand, India

6 ICAR-Indian Institute of Rice Research, Hyderabad, India

7 College of Basic Science and Humanities, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, India

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
† Equal contribution.

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section 2

## Gene Expression and Human Diseases

### **Chapter 6**

## *APC* and *MSH2* mRNA Quantitative Gene Expression and Bayesian Analysis of Proband in Hereditary Colorectal Carcinoma

*Tjahjadi Robert Tedjasaputra, Mochammad Hatta, Muhammad Nasrum Massi, Rosdiana Natzir, Ilhamjaya Patellongi, Marcellus Simadibrata, Rina Masadah, Muhammad Luthfi Parewangi, Prihantono, Andi Asadul Islam, Agussalim Bukhari, Rinda Nariswati, Shirly Elisa Tedjasaputra, Vincent Tedjasaputra and Jonathan Salim*

#### **Abstract**

Heterozygote relatives have approximately 80% lifetime colorectal cancer (CRC) risk. mRNA gene expression and Bayesian theorem can calculate CRC's family risk through the initial pedigree proportion appended with conditional information. The study is the first to report such an application. The present cross-sectional and translational investigation tracked CRC patients' tissue and blood measurement of adenomatous polyposis coli (*APC*) and MutS homolog (*MSH*)*2 mRNA* quantitative gene expressions, control matching, and ancestral analysis by pedigree and Bayesian theorem. Among 40 CRC patients, mean tissue level and hereditary cutoff of APC are 13,261 (670) fold-change (fc) and 12,195 fc, while 12,219 (756) fc and 11,059 fc for MSH2. A quarter of the CRC patients had a history of familial CRC. Meanwhile, four CRC patients and 10 probands were evaluated for recurrence risk via pedigree, quantitative PCR, and Bayesian analysis. We determined a cutoff point for hereditary mRNA quantitative expression. *APC* and *MSH2* levels in the CRC subjects were significantly lower than controls. The Bayesian analysis builds ways to calculate relative risk in CRC patients' family members and application in clinical practice.

**Keywords:** hereditary CRC, APC gene, MSH gene, Bayesian analysis

#### **1. Introduction**

The continuing morbidity and lethality of colorectal cancer (CRC) do not always stop at the diseased person. In fact CRC holds the top place in familial inherited case prevalence [1]. Hereditary CRC with clear-cut forms overall can be divided into lynch syndrome (LS) or hereditary non-polyposis colorectal cancer (HNPCC) and familial adenomatous polyposis (FAP), which are inherited by autosomal dominant pattern [2]. Those who related to parents or grandparents with an autosomal dominant trait has at least an 80% chance for lifetime risk of CRC incidence [3].

Screening for cancers is dependent on every individual. Those without any familial cancer history can start colonoscopy for CRC screening at 50 years old. Nevertheless, the age is smaller by a magnitude if you have a CRC first-degree relative. A CRC individual will raise your risk by two to three times more than normal; however, more relatives with the disease may equal to an exponential risk increase [4].

Familial characteristics such as age, disease onset, size, and health history often pose precarious conditions to the internist and gastroenterologist who did CRC hereditary screening by the Amsterdam and Bethesda criteria. These were illustrated in the low guidelines' performances from both Revised Bethesda Guideline and Amsterdam II Criteria against molecular tumor analysis with 50 and 25% sensitivity and 7 and 38% positive predictive values [5]. We accordingly need a more swift and stable method to screen for hereditary CRC, such as with the implementation of family history, molecular expression, and Mendel inheritance concept [6, 7].

The Mendel hereditary concept is well-performed in screening or determining autosomal and gonadal patterns risks, as it can compute recurrence probability; however, it cannot be quickly adjusted for mutation, external factors, and coverage changes, since it focused more on empirical recurrences. Yet the application of only such analysis is questionable, as most traits are not generalizable. Hence, family members' recurrence risk should be calculated with prior Mendelian risk and geared with personal genotyping and environmental conditional probability [8–11].

Genetic studies are becoming more present recently with research around genetic matters like DNA sequencing or polymorphism [12]. They open up a new horizon for disease susceptibility and inheritance analysis, including malignancy. However, RNA study was still rare as mistakes in the nucleotide base or elsewhere will be quickly dealt by the proofreading and the mismatch repair (MMR) genes [13].

Adenomatous polyposis coli (*APC*) gene exhibited a unique causal relationship to the incidence of hereditary FAP from mutation on the fifth chromosome's second region and first band. LS conversely rises from mutations in the second, third, and seventh chromosome of several different genes, including human MutL homolog 1, human MutS homolog 2 and 6, as well as human post-meiotic segregation 1 and 2 (*hMLH1*, *hMSH2*, *hMSH6*, *hPMS1*, and *hPMS2*) [14].

In commencing the current study, research operators or the authors need to be more aware of their surroundings. This time, huge complex calculations and uncommon Bayesian prior and posterior analysis were implemented. There is no former report on the *APC* and *MSH2* genomic RNA expressions to CRC risk with modified Bayesian estimation per the authors' knowledge [15–17]. We hoped the current study was able to officialize an adequate hereditary measure through gene expression and the families able to incorporate Bayesian into their risk of CRC.

#### **2. Materials and methods**

#### **2.1 Study design**

The current translational study adopted a cross-sectional design in assessing 71 subjects from May 2018 to December 2019. Medical Ethics Committee of Hasanuddin University

ensured the research commencement had followed Helsinki declaration and institutional review board (IRB) standards with certification of 884/H<IP_ADDRESS>/PP31-Komite/2018. Every subject had understood and agreed to participate as shown by the signed informed consent form. The author priorly measured the minimum sample size by 5% alpha and 80% power.

#### **2.2 Subject enrollment**

Subjects were consecutively gathered from Tarakan General Hospital in Jakarta and Siloam Hospitals Lippo Village in Tangerang. The case group broadly enrolled all 41 CRC patients who had undergone a biopsy in either hospital. Gastroenterologists and oncologists made the CRC diagnosis based on the clinical symptoms, physical examinations, and supporting investigations (i.e., endoscopy and histopathological findings). We contrarily enlisted normal patients or CRC patients' relatives who had been matched by age, sex, and body mass index to the control group.

Exclusion of patients from either group may happen at any time of the study if they had: (1) presence or history of other malignancies or inflammatory bowel disease, (2) ever done chemotherapy or radiotherapy, (3) illnesses that inhibit communication, and (4) refuse to participate.

#### **2.3 Data and sample collection**

The current study investigated *APC* and *MSH2* quantitative genotypic expressions as well as hereditary possibilities. 0.3 ml of blood samples were taken from all 71 subjects using one cc syringes, yet only CRC subjects did colonoscopy biopsy. Each of the blood and tissue samples was then laced into separated sample tubes containing L6 buffer preservative, specifically created by Hasanuddin University from a slightly modified version of the buffer in the Boom RNA extraction method. **Figure 1** showed the complete RNA extraction techniques by the NucleoSpin technique (#740200.50) [18]. The isolated extraction results were subsequently amplified using a real-time PCR

#### **Figure 1.**

*RNA extraction technique. Step by step pathway of the RNA extraction with NucleoSpin technique (#740200.50) [18].*

#### **Table 1.**

*Genetic primers.*

(RT-PCR) and then measured with a Bio-Rad CFX Manager 3.1 (Bio-Rad Laboratories, Hercules, USA) [19–21]. After that, we also applied the Bayesian probability analysis on the probands' age, *APC*, and *MSH2* data to yield CRC risk estimations.

#### **2.4 Polymerase chain reaction (PCR)**

The present study used RT-PCR to detect the mRNA expression of *MSH2* and *APC* genes with the following primers (**Table 1**). First, they entered the initial denaturing phase with 94°C for 3 minutes. Then the process continued with 38 cycles of annealing stage in 54°C for 30 s and extension stage in 72°C for 30–40 s [19–21]. Note that each gene has its unique amplicon length. For example, *APC* is 89 bp long, 81 bp for *GAPDH*, 215 bp for *MSH2*, and 109 bp for ß-actin [23].

We procured the RT-PCR materials from Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, USA). Additionally, we used CFX Connect realtime PCR system from Bio-Rad Laboratories for the measurement [22, 23].

#### **2.5 Bayesian probability**

The current investigation quantified the CRC risk among family members through Bayesian analysis of the Mendelian hereditary, genetic, and direct mutations data [4, 16]. We derived a posterior probability equation (Eq. (1)) to estimate the family CRC risk from the coupling of the conditional and prior probability theorems.

$$\operatorname{p}\left(\mathbf{A}\mid\mathbf{B}\right) = \frac{\operatorname{p}\left(\mathbf{B}\mid\mathbf{A}\right)\operatorname{p}\left(\mathbf{A}\right)}{\operatorname{p}\left(\mathbf{B}\right)} \text{ and } E\left(\boldsymbol{\beta}\right) = \stackrel{\circ}{\boldsymbol{\beta}}\operatorname{p}\left(\boldsymbol{\beta}|\boldsymbol{\gamma}|\boldsymbol{\cdot},\mathbf{\theta}\right)d\boldsymbol{\beta}$$

$$p\left(\beta|\gamma|,n\right) = \frac{p\left(\gamma,\beta,|\mathfrak{H}\right)}{p\left(\gamma|\mathfrak{H}\right)} = \frac{p\left(\gamma,\beta|\mathfrak{H}\right)}{\int p\left(\gamma,u|\mathfrak{H}\right)du} = \frac{f\left(\gamma|\beta\right)\varpi\left(\beta|\mathfrak{H}\right)}{\int f\left(\gamma|u\right)\varpi\left(u|\mathfrak{H}\right)du} \tag{1}$$

APC *and* MSH2 *mRNA Quantitative Gene Expression and Bayesian Analysis of Proband… DOI: http://dx.doi.org/10.5772/intechopen.102800*

#### **2.6 Statistical analysis**

Statisticians used descriptive statistics to univariately portray the subjects' demographic characteristics and histological findings, as opposed to Shapiro-Wilk for determining the numeric data normality. Further bivariate analysis of the parametric numeric variables used *t*-test, while non-parametric used Mann-Whitney. Meanwhile, they tested categorical variables by either *x*<sup>2</sup> or Fisher exact. Bayesian analysis was employed last for adjusted estimation of the CRC risk in the probands (i.e., relatives of the hereditary CRC patients).

#### **3. Results**

Forty CRC patients and 31 healthy controls had a 100% participation rate within the research period. Those with CRC on average live 5.19 years longer with 1.21 kg/ m2 lower body mass index (BMI) than the counterpart. The sex difference was also apparent with a 1.11:1 vs. 0.72:1 male to female ratio among the case and control groups, respectively. We also determined the cellular differentiation levels among the biopsied CRC subjects, with results in **Table 2**.

There was a significant difference in blood *APC* levels between CRC and control subjects. There was a lower mean value of *MSH2* in CRC but no substantial difference between CRC and control subjects because of the outlier (**Table 3**).

Hereditary screening of the CRC subjects came next. Analyzing the CRC risk with Bayesian Analysis is futile if the disease is not hereditary in the first place. There are however no prior validated data on cutoff amounts for CRC hereditary trait from *APC* and *MSH2* gene expression. Hence, we established the required cutoff values through the fifth percentile technique.

**Table 2.** *Baseline characteristics.*

#### **Table 3.**

*APC and* MSH2 *gene expression between groups.*

**Table 4** showed the percentiles distribution of both *APC* and *MSH2* quantitative expressions among the control group. The fifth percentile of both genes adequately fits to be hereditary cutoff values since it had no significant difference to the first and third percentile. Henceforth, hereditary CRC was very likely in those with over 12,195.80 fc *APC* or 11,059.60 fc *MSH2*.

The CRC subjects were then distributed nicely into either the hereditary or sporadic category with the determined cutoff. Gene expressions equal to and above the cutoff positively correspond to a hereditary status. A majority proportion (52.5%) of the 40 people with colorectal cancer had hereditary nature based on both *APC* and *MSH2* cutoffs. Nonetheless, the hereditary rate decreased by 2.5% and 20.0% if only using cutoff from either one (**Table 5**).

Complete pedigree analysis of the family age, health, gender, and family history of diseases is essential for the estimation of CRC risk. There were merely eight subjects with positive CRC in the family and even then, half were dropped because of vague recollection or retracted permission. We consequently extracted only 10 probands from the four CRC families for Bayesian analysis (**Figure 2A**–**D**).

#### **Table 4.**

*Gene expression percentile distribution.*

APC *and* MSH2 *mRNA Quantitative Gene Expression and Bayesian Analysis of Proband… DOI: http://dx.doi.org/10.5772/intechopen.102800*

#### **Table 5.**

*CRC subjects' hereditary distribution.*

#### **Figure 2.**

*Hereditary CRC subjects' family pedigree. The pedigrees symbols correspond to the standardized human pedigree nomenclature [17], where a circle denoted a woman, a square for a man, a straight line for a relationship, and a diagonal strikethrough line for death. Colors also exhibited a similar trend. Red represented the proband, yellow for malignancy other than CRC, and light green for CRC subjects. A: Pedigree of 67-year-old man CRC, B: Pedigree of 44-years-old woman with early-onset CRC, C: Pedigree of elderly 62-years-old woman with CRC, D: Pedigree of 47-years-old woman with CRC.*

#### **Table 6.**

*Proband Bayesian CRC risk estimation.*

**Table 6** described the process of CRC risk estimation among the probands. Bayesian analysis conditionally tweaked each proband's initial risk ('prior' column) with his or her age and gene expressions to yield adjusted odds ('phi' column) in developing or carrying CRC ('Y' column).

The combination of the pedigree in **Figure 2** and Bayesian estimation in **Table 6** gave rise to the complete story of CRC risks in 10 selected probands relative to the four subjects with CRC. A 67-year-old man with CRC of late-onset had a son with almost 100% CRC risk carrier or development (**Figure 2A**). Meanwhile, a 44-year-old woman with early-onset CRC and paternal death due to CRC had a mother with 93% unlikely, a son with 50.51% likely, and another son with 50% unlikely carrier or incidence of CRC (**Figure 2B**). An elderly woman with paternal death of unknown origin and CRC death of the aunt had an adult daughter with 50% of the CRC risk (**Figure 2C**). On the other side, a 47-year-old CRC diseased woman with a huge family and paternal death due to prostate cancer had a 50% likely risk on her sister, but three 50% unlikely probability on her mother, younger sister, and female offspring (**Figure 2D**).

#### **4. Discussion**

Several autosomal dominant diseases may appear to be majorly asymptomatic until adulthood or beyond puberty. Given the notion, sophisticated comprehension of the hereditary risk of neoplasm is critical. Direct genomic examination and molecular diagnosis of nucleic acids from the blood, tissue, or other bodily fluids have become more prominent as a screening and investigation standard [4].

DNA is the building block of every living cell in the world. In doing its job, DNA is often destringed into a single-stranded DNA (ssDNA) for DNA replication or mRNA transcription. The first case goes by attachment of complementary nucleic acid base pairs to the corresponding one in both the leading and lagging strand of the partly unzipped ssDNA by DNA polymerase. This created an exact copy of the source DNA. While for the latter case, the RNA polymerase enzyme works separately on the sense and antisense part of the ssDNA. Each strand of ssDNA produced a single mRNA, thus there will be two mRNAs for every transcription of a DNA. These mRNA then moved to the ribosome for translocations into amino acids and eventually proteins.

#### APC *and* MSH2 *mRNA Quantitative Gene Expression and Bayesian Analysis of Proband… DOI: http://dx.doi.org/10.5772/intechopen.102800*

Cancerous cell arises due to mutations or faulty repair of the nucleic acid bases. Even one deletion, addition, or translocation of the bases drastically changes the transcribed mRNA, codon, and hence the protein. Commonly, uncontrolled proliferation of cells happened if the mistakes occurred on oncogenes or tumor suppressor genes. Constantly activated oncogenes or inhibited tumor suppressor genes direct the cell cycle to bypass checkpoints and not return to the resting phase.

Familial adenomatous polyposis and Lynch syndrome are the top two subtypes of hereditary colorectal cancer with the most incidence count. FAP is almost solely generated because of mistakes in the tumor suppressor gene of *APC*. Whilst a lot of MMR genes can be responsible for LS or HNPCC (e.g., *MLH1*, *MSH2*, *MSH6*, *PMS2*, and epithelial cell adhesion molecule) [24]. The present study accordingly chose APC and one of the repair genes to accommodate both hereditary subtypes of CRC. We inevitably selected *MSH2* among the other mismatch repair (MMR) genes due to the prevalence and missenses amount. Kim et al. stated that approximately 90% of the mutations in the MMR occurred in either *MLH1*, *MSH2*, or both genes [25]. Furthermore, a 13% increase of missenses (i.e., a type of gene mutation which renders genotypic reading and interpretation to be considerably harder) was measured on *MLH1* as opposed to *MSH2* [26]. *MLH* is also more often found in sporadic colon cancer [14]. Next, a three countries assessment on lynch syndrome also gathered that *MSH2* had a substantially higher 10-year-risk of severe adenoma (∆ = 10.1%) and tumor pathogenic variants (11.4 vs. 11.3%) over *MLH1* [27].

Observation among the CRC versus the control group displayed a lesser mean mRNA level of *APC* gene expression than the control group (∆ = 1683.06 fc, *p* = 0.014). However, the reverse is true for the *MSH2* gene expression (∆ = 808.82 fc, *p* = 0.465). The minute discrepancy can be because of many gene mutations also somatically involved *APC*. Engel et al. confirmed that from *MSH2*, *MSH6*, and *MLH1* tumor variants, somatic mutations of *APC* happened in 75, 100, and 11% cases [27].

The current study and its prior version in the Indonesian Medical Journal [28], swiftly acted on a novel proposition to determine *APC* and *MSH2* gene expressions cutoff for hereditary cancer classification. Looking over the percentile distribution of healthy controls, the measure of the first to fifth percentiles only had negligible insignificant differences. Following the wrapped Cauchy distribution of circular data techniques with M, D, and A statistics [29], we officialize the fifth percentile mark as the hereditary cutoff (*APC* = 12,195.80 fc and *MSH2* = 11,059.60 fc).

Interesting hereditary proportions had been exhibited by the 40 CRC subjects. Hereditary using only *APC* gene enlisted 50%, while *MSH2* gene gathered 32.5%, and both genes combination enticed 52.5% of the subjects. Only the one with *MSH2* cutoff showed akin prevalence to the 20–30% global familial CRC [30].

The brief, simple, yet informative presentation of the family medical history can be conveniently reflected through a visual pedigree. Taking accurate information on family history should be standard medical practice. The pedigree will subtly enhance oncology prevention, diagnosis, and treatment together with recent genomics advancements. Family history can substantially alter not only genetic testing results but also oncology prevention, including digestive cancer [16]. For instance, a study found that cancer occurrence in an individual relative to a bowel cancer was dependent on familial cancer prevalence, duration of onset, and closeness to the diseased [14]. The current study employs a family pedigree to analyze the health status of CRC patients' relatives. The diagram clearly outlines familial relationships; thus, it will be easier for recognition and interpretation of the inheritance patterns [16, 17].

Individualized medical care has been on the rise of attention. Instead of general medicine for a certain disease, therapeutic care needs to start keenly observing the patient and prescribing medicine that has been tailored for that particular individual [31]. One of the ways in achieving such goals is through personal genetic and biological factors consideration. Particularly, extra attention should be imposed on hereditary disorders like malignancy.

Relatives of a confirmed cancer patient may not experience any cancer-predisposing syndrome from a clinical viewpoint although having an increased risk of developing one. Decreased penetrance and onset age must be factored into consideration especially for autosomal dominant hereditary disorders like colorectal cancer [4, 7, 8, 32].

Investigation on the hereditary risk of diseases in relatives and families accordingly requires a lot of extra information and tests. The natural history of the disease firstly should be made clear (i.e., what is the diagnosis, how does it spread, how does it inherit, and what about its epidemiology). Next, focus on medical examinations or observations that include genetic data (i.e., marker, expression, mutation, and then clinical data). Lastly, we can examine the family pedigree and make objective evidence-based inferences for analysis.

Bayesian analysis of the pedigree from the familial and genetic factors displayed an interesting finding. It was obvious that the majority of probands on the next generation of the current CRC patients had a higher likely probability of CRC incidence or carrier. Probands below the current CRC patients' generation (i.e., the first, fourth, fifth, sixth, and tenth probands) on average had a 60.1% likelihood of CRC risk. Those in the same generation had 50.1% risk, while those above (i.e., the second and seventh probands) had 28.4% risk. These observations were moderately suitable with the autosomal dominant disease hereditary pattern.

The current study bridged the knowledge gaps of CRC hereditary cutoff, gene expressions risk of CRC, and Bayesian pedigree analysis. Nevertheless, some limitations are still presented. First, a small sample size increased the chance for a biased result. The sample selection in the study is also confined to a segmental niche of Indonesian urban citizens, hence the result may not be generalizable to rural or foreign populations. There were also no immunohistochemical or DNA sequencing mutation tests for objective comparisons. Lastly, the method of selecting cutoff from a fifth percentile has not broadly checked for its credibility yet.

#### **5. Conclusion**

The current study explored the relationship between *APC* and *MSH2* gene expressions to colorectal cancer risk assessment. Bayesian analysis computed that downregulation of the mRNA gene expression will induce a higher risk of developing or exacerbating CRCs. Yet only *APC* had significance while *MSH2* did not. Therefore, the study establishes the foundation of utilizing *APC* and *MSH2* gene expressions for CRCs risk indicators. Future novel or multiplicity studies should consider family pedigree as a part of CRC prevention strategy among the patient's relatives with expanded cohorts and sample pool, including more profound Bayesian analysis and application with other essential hereditary genes.

APC *and* MSH2 *mRNA Quantitative Gene Expression and Bayesian Analysis of Proband… DOI: http://dx.doi.org/10.5772/intechopen.102800*

#### **Acknowledgements**

The authors appreciate all of the participants in this study.

#### **Conflict of interest**

The authors declare no conflict of interest. The authors acknowledge that although Vincent Tedjasaputra is currently an American Association for the Advancement of Science (AAAS) Science and Technology Policy Fellow; yet, the current paper is not affiliated with AAAS nor is the product of his position at the National Science Foundation.

#### **Declarations**

Please note that an earlier version of this manuscript has been published in the Indonesian Biomedical Journal volume 12 on 2020 with a digital object identifier (DOI) of 10.18585/inabj.v12i4.1329 [28].

#### **Abbreviation summary**

### **Author details**

Tjahjadi Robert Tedjasaputra1 \*, Mochammad Hatta2 , Muhammad Nasrum Massi3 , Rosdiana Natzir4 , Ilhamjaya Patellongi5 , Marcellus Simadibrata6 , Rina Masadah7 , Muhammad Luthfi Parewangi8 , Prihantono9 , Andi Asadul Islam10, Agussalim Bukhari11, Rinda Nariswati12, Shirly Elisa Tedjasaputra1 , Vincent Tedjasaputra13 and Jonathan Salim14

1 Internal Medicine Department, Tarakan General Hospital Jakarta, Jakarta, Indonesia

2 Faculty of Medicine, Molecular Biology and Immunology Laboratory, Universitas Hasanuddin, Makassar, Indonesia

3 Faculty of Medicine, Microbiology Department, Universitas Hasanuddin, Makassar, Indonesia

4 Faculty of Medicine, Biochemistry Department, Universitas Hasanuddin, Makassar, Indonesia

5 Faculty of Medicine, Physiology Department, Universitas Hasanuddin, Makassar, Indonesia

6 Faculty of Medicine, Internal Medicine Department, Universitas Indonesia, Jakarta, Indonesia

7 Faculty of Medicine, Pathology Anatomy Department, Universitas Hasanuddin, Makassar, Indonesia

8 Faculty of Medicine, Internal Medicine Department, Universitas Hasanuddin, Makassar, Indonesia

9 Faculty of Medicine, Oncology Surgery Department, Universitas Hasanuddin, Makassar, Indonesia

10 Faculty of Medicine, Neurosurgery Department, Universitas Hasanuddin, Makassar, Indonesia

11 Faculty of Medicine, Nutrition Department, Universitas Hasanuddin, Makassar, Indonesia

12 Statistic Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia

APC *and* MSH2 *mRNA Quantitative Gene Expression and Bayesian Analysis of Proband… DOI: http://dx.doi.org/10.5772/intechopen.102800*

13 American Association for Advancement of Science (AAAS), Science and Technology Policy Fellow, National Science Foundation (NSF), Washington, DC, USA

14 Faculty of Medicine, Pelita Harapan University, Tangerang Indonesia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 7**

## Circular DNA: How Circular DNA Assists Cancer Roll with Therapeutic Punches

*Parvaiz Yousuf*

#### **Abstract**

DNA within cells is either present in the form of long strands as in eukaryotes or circular shapes in Yeast plasmids, mitochondrial DNA, and double minutes in tumor cells. Apart from them, ribosomal or telomeric DNA has been found to produce specialized forms of extrachromosomal circular DNA (eccDNA). eccDNA was discovered in both normal and cancer cells in recent times, indicating a much more significant role. The eccDNA has been found to promote tumor proliferation, survival, and aggressiveness in almost half of all cancers by increasing oncogene copy numbers. This chapter will discuss the biogenesis and function of eccDNA and how it promotes tumor adaption under changing microtumour environmental conditions, as in the case of drugs.

**Keywords:** DNA, DNA, circular, neoplasms, antineoplastic protocols, oncogenes

#### **1. Introduction**

DNA is the basic genetic material present in most living species. It was first discovered by Friedrich Miescher in 1869 [1]. This genetic material exists in the form of chromosomes that consist of linearized double-stranded DNA fused with histone proteins within the nucleus and store most of the genetic information of an organism [2]. However, DNA or genes are not confined to the nucleus only as they can also be found in other extra-nuclear structures. Eukaryotes have circular DNA present within the mitochondria and chloroplast and are structurally similar to the bacterial genome [3, 4]. In addition, other forms of circular DNA also exist within the cytoplasm and nucleus [5]. This DNA has been named eccDNA, and its size varies from a few base pairs to millions of base pairs. On the basis of size and sequence, this form of DNA has further been categorized into 100 bp to 10 kb long small polydispersed DNA (spcDNA), 100 to 400 base pair long micro-DNA, millions of base pairs long extrachromosomal DNA, and telomeric circles or t-circles consisting of base pairs that are multiplies of 738. In addition to eukaryotes, viruses and bacteria also possess circular DNA in different forms with varying lengths [6–15], which are summarized in **Table 1**. In recent years, a lot of studies have been done on this form of DNA that has improved our knowledge. Their biological, physiological properties, as well as

#### **Table 1.**

*Circular DNA is present in several forms among viruses, bacteria and eukaryotes with different size ranges and functions.*

their functions, are becoming more noticed. This has led to the discovery of more previously unknown forms of circular DNA [16]. Some researchers believed that DNA exists in rings within the cytoplasm of higher organisms, which was later verified with the eccDNA discovery [17]. However, scientists observed this circular DNA in many other organisms before they were seen present in many heterogeneous cancer cells. For instance, Alix and Yasuo had observed the different lengths of eecDNA in sperm of boar observed under an electron microscope [5]. In many other eukaryotic species, such as humans, Yeast, hamsters, mice, and Drosophila, this form of DNA was observed [18–23]. At the same time, the size of eccDNA present in normal eukaryotic cells has been found to be very small and is usually less than 500 base pairs [20, 22–25]. However, eccDNA of a much bigger size has also been discovered in certain types of tumor cells. After the tumors were surgically removed and mitotic abnormalities were investigated after being stained by Cox, Spriggs, and acetic orcein, small chromatin bodies of the size of an intact chromosome were observed [26]. The name given to these chromosomes was double minutes (DMs) as they were present in pairs. Studies by Sprigg later concluded that such chromatin bodies were present more in malignant childhood brain tumors [27]. These DMs were also spotted in HeLa cells using the buoyant density method [28]. Thus, it is clear that the eccDNA was more commonly found in genetically unstable cells, such as tumor cells, and was not as common in normal cells [29, 30]. Although this DNA has been found to be homologous to the genomic DNA, it is clear that the presence of eccDNA means genome instability. This is why most of the tumors have exceptionally high levels of eccDNA, and the oncogenes usually occur in this genome only. Much research has been conducted to find out how this DNA carries driver oncogenes and contributes to tumor resistance and heterogeneity. The levels of eccDNA are much more common in tumor cells than was earlier thought. It makes tumors highly resistant to the

therapeutic drugs by increasing the oncogene copy number and heterogeneity. This chapter explains in detail the eccDNA discovery in tumors and how it will shape the therapeutic operations for the treatment of varied cancer types.

#### **2. eccDNA: from hypothetical existence to role in cancer in eukaryotes**

The eccDNA was once thought to play some secondary roles in both prokaryotes and eukaryotes. However, with the advent of technology, it became clear that eccDNA plays more pivotal functions in eukaryotes than ever thought. It is estimated that over half of all tumor types have eccDNA, enabling them to develop resistance against different therapeutic drugs.

#### **2.1 Historical perspective**

The eccDNA was discovered by Yasuo Hoota and Alix Bassel [5]. They were actually investigating a theory proposed by Franklin Stahl in 1964 that higher organisms' chromosomes consist of DNA rings [5]. Their experiments lead to the discovery of DNA circles of varying sizes that range from 100s to 1000s of bp's resembling DNA of higher organisms. Double minutes (DM'S) was the name given to these large circles of DNA. Later on, scientists began investigating the presence of eecDNA in other cell types. For instance, another group of researchers discovered the DMs in human tumor cells by preparing karyotypes and utilizing purification by CsCl gradience [26, 28]. The DNA from many other organisms was studied using EM imaging techniques and CsCl gradient purification [22, 31–36]. The researcher used the technique of Southern Blotting to determine the homology between genomic DNA and eccDNA. Most of the eccDNA observed was less than 500 bps in size and is called poly-disperse circular DNA (spcDNA) [24, 37]. Moreover, most of the DNA obtained came from repetitive sequences of DNA, while some of the spcDNA molecules fused with particular sequences. Even some researchers observed that 9–11 base pair long direct repeats flank few non-repetitive spcDNA sequences on both sides [19–21]. This indicated that DNA circles were formed by certain DNA repair pathways, such as microhomology-mediated end joining or homologous recombination, which mediate the joining of ends in between small base repeats. However, many later studies proved that no repetitive sequences are needed to mediate this end-to-end joining. This occurred when eccDNA with specific sequences was isolated and sequenced with no repetitive areas within or flank the DNA [38]. Around the same time period, another group of researchers utilized exonuclease III for quantification of eccDNA and concluded that there occur varying levels of eccDNA among mice tissues [39]. As far as the formation of eccDNA is concerned, many groups used techniques studying eccDNA from repetitive sequences to determine their formation. 70 times increase in eccDNA formation occurred in murine cells when Cycloheximide (a protein synthesis inhibitor) was used. The same results were obtained with many other chemicals, such as hydroxyurea (DNA replication inhibitor, and 7,1-dimethylbenz[a]anthracene (a carcinogen) [20]. Similarly, a higher concentration of longer eccDNA molecules was observed in cells obtained from patients suffering from Fanconi Anemia (wherein particular DNA repair pathway errors occur) [30]. Afterwards, smaller eccDNA molecules were explored upon the usage of 2-dimensional gel electrophoresis, which showed that eccDNA levels are increased by carcinogens, and different stages of development show varied eecDNA formation in flies and frogs. It has been

found that even the foreign DNA can result in the formation of eccDNA and DNA sequence organization within tandem repeats prepared eccDNA preparation from the DNA [40–42]. In a nutshell, it suggests that the formation of eccDNA depends on DNA organization, DNA damage repair, and the sequence of DNA. All this indicates how eecDNA has the tendency to code for driver oncogenes as eccDNA are amplified in terms of oncogenes leading to drug resistance in most cancers [43–45].

#### **2.2 Recent advances in eccDNA**

The eecDNA is not well understood, and researchers have been trying to understand it better. Human cancer and mouse tissue cells were lyzed, and eccDNA was isolated to recently sequence the entire length of an eccDNA compliment. This was done using a paired-end high sequencing technology that allowed the characterization of this form of DNA [46]. The junctional sequences of this eccDNA were identified through high-end sequencing technology on exonuclease-resistant eecDNA that was amplified using a rolling circle mechanism [46]. After these studies, it was found that all human cancer and mouse tissue cells showed consistent patterns with specific eccDNA sizes peaking around 180 and 380 base pairs. Around 5% of these molecules extended in the range of 2–3 kilobase pairs. The term given to this DNA was microDNA. However, the study may have resulted in the under-representation of long eccDNA molecules as small DNA circles are amplified more efficiently than largesized circles. Moreover, most of the eccDNA circles were smaller, as represented by electron microscopy [47]. After mapping this eccDNA, many bases extended to over 100–1000 specific sites with the genome. It also showed enrichment in some particular regions, transcriptionally active chromatin, hotspots that include CpG regions and UTR regions, and DNA patches with high GC content (around 60%) [46]. Most microDNA is flanked by the genomic DNA containing 2–15 base pair repeats, suggesting a micro-homology mode generates DNA circles [46]. The sites where eccDNA is formed might be associated with cell lineage, which is indicated by weak clustering of eccDNA in the ovarian and prostate cell lines. Other studies claimed that if MSH3, which encodes a peptide in the DNA mismatch repair pathway, is deleted, it results in an 80% decrease in the eccDNA levels [47]. Moreover, there is no clear proof of whether this small eccDNA replicates or not. However, there have been rough estimates of electron microscopy about the eccDNA abundance prepared from a specific number of cells, indicating that 125–200 circles of eccDNA exist per DT40 cell [47]. Similarly, another study was performed in *Saccharomyces cerevisiae,* where researchers found 1/4th of the Saccharomyces being covered with about 2000 DNA circles. Moreover, this study ignored smaller eccDNA (less than 1-kilo base pair in size), and there was no dependency on the junctional sequence identification to label a particular DNA sequence as eccDNA. Therefore, the eccDNA was 1–38 kilobase pairs long, which were significantly enriched with circles from repeated genome parts, such as gene duplications, ribosomal DNA circles, and transposons. This suggests that circles were formed in a homologous recombination form. Furthermore, specific sequences were the precursors of around 60% eccDNA, and seven base pair direct repeats were revealed by over 90% of the genomic sites.

The mechanism responsible for eccDNA formation is yet to be understood clearly. However, researchers have found that tandemly repeated genomic sequences are present in eccDNA [30, 48, 49]. It indicates that eccDNA formation primarily occurs from such tandemly repetitive DNA sequences [48]. At the same time, nonrepetitive

#### *Circular DNA: How Circular DNA Assists Cancer Roll with Therapeutic Punches DOI: http://dx.doi.org/10.5772/intechopen.102687*

DNA also results in eccDNA formation. For instance, a group of researchers isolated eccDNA from HeLa S3 cells and found the presence of eccDNA [28]. May it be coding or non-coding regions, eccDNAs originate from both. Many studies concluded DMs bear drug-resistant oncogenes and another oncogene [50]. Different researchers have proposed four distinct models of how eccDNA formation occurs. The first one is the translocation-deletion-amplification model, in which genes' rearrangements occur near the chromosome's translocation site [51]. The fragments closer to the translocation breakpoints can be deleted, amplified, and circularized, which results in eccDNA formation [52, 53]. The second model-Chromothripsis model, explains that multiple acentric DNA fragments can form due to chromosome shattering, with some of the fragments self-ligating to form eccDNAs [54, 55]. The third model is the breakage-fusion-bridge (BFB) model [56]. Losing a telomere by a chromosome is what initiates the BFB cycle. Two chromatids are formed during anaphase after chromosome duplicates. Afterwards, the fusion between broken chromosome ends (chromatids) occurs, which results in the formation of a dicentric chromosome [57]. A bridge is formed between these two chromatids during anaphase due to the presence of two centromeres that are disrupted when two centromeres are directed to opposite poles of the spindle: The chromosome breakage and uneven distribution of genetic material results in the centromeres being segregated into daughter cells [58]. Thus, one of the daughter cells has a chromosome with an inverted repetitive base sequence on its ends, while the other has a chromosome with deletion at the ends. When DNA replication continues in the next cycle, the fusion of sister chromatids occurs once again, resulting in the repetition of the BFB cycle. All the events result in DNA sequence amplification at the telomeres that eventually loops out to form eccDNAs [59]. The fourth model is the episome model, which explains that excision of small circular DNA results in episome formation, which eventually results in recombination or over-replication, resulting in eccDNA formation [34, 60]. It is well known that eccDNAs can lead to mutations, amplifications, translocations, deletions, etc. In human somatic cells, certain loci that include HLA, DAZ4, and KIR have been found to be more prone to circular product formation, resulting in chromosomal deletions [61]. Similarly, in a study performed on Yeast, identification of 1756 eccDNAs was made, which covered 23% of the total Yeast genome [62]. This means it is highly likely for an oncogene to be present on eccDNA. Similarly, the presence or absence of DMs is a vital factor to consider as it relates to the clinical outcomes of the patients. For example, some oncogenes, such as oncogene Sei-1, induce DM formation [63]. Similarly, another study found that oncogene Met's higher amplification and expression occurs when present on DMs. The promotion of Sei-1 induced DM generation results because of this Met signaling cascade pathway. Moreover, in the patients' ovarian cancer (OC) cells, a greater copy number amplification of eukaryotic initiation factor 5A2 (EIF5A2) was found by way of DMs, indicating the role of DMs in carrying and sustaining oncogenes [64].

Recently, a group of researchers studied human cell lines and *Caenorhabditis elegans* by using a mechanism that relied on Cesium Chloride- Ethidium Bromide (density gradient centrifugation) and high-throughput sequencing and tagmentation [65]. They reported circles that mapped on non-coding and coding genomic regions. The protein-coding regions of the DNA encoding titin and mucin lead to the appearance of eccDNA in these cases. The study concluded that the eccDNA promotes and interferes with the particular exon transcription, thereby leading to the expression of various isoforms of a genetic code [46]. In cancerous cells, the eccDNA has been found to play complex roles to promote tumourigenesis. The changes in DMs,

eccDNAs between normal and cancerous cells have been quantified by fluorescent microscopy [12, 66]. The amplification of myc and EGFR genes in tumor cells occurred through a few passages by eccDNA formation [66]. Similarly, another group of researchers combined fluorescence imaging with software analyzing images for quantification of certain oncogene copies. There was a 40% amplification of EGFR and MYC gene in the examined human cancer cells, whereas normal cells showed no enrichment [12]. What surprised researchers were that more oncogene amplification occurred through a mechanism involving eccDNA formation rather than a mechanism involving chromosomal amplification [12]. The detection of eccDNA within the normal tissues may be primarily because of having no procedures helping circle enrichment and not enough dye binds to small microDNA (eccDNA) for microscopic detection. All of these studies prove solid evidence that tumor heterogeneity is a result of eccDNA, wherein they also assist cancer cells in evolving through an increase in oncogene copy number [47]. The validation of these studies was done by another group of researchers that found MET (an oncogene) in glioblastoma cells showed amplification on eccDNA as was indicated by FISH [67].

#### **3. Extrachromosomal oncogene amplification drives tumor evolution and genetic heterogeneity**

Human tumors are highly heterogeneous, and they evolve and adapt in quickly changing microenvironments from individual cells to a mass of genetically heterogeneous cells. Only those cells are selected by the Darwinian selection, which adapts quickly to their environments. Tumor heterogeneity offers a mutation pool from which tumor-friendly mutations are selected [68–72]. The progression of neoplasm and resistance to therapeutic drugs is driven when mutations are passed on to daughter cells after cells have acquired the mutations enhancing fitness. For instance, one of the usual mutations of cancer cells is oncogene amplification, which is either present on chromosomal DNA or eccDNA parts that include DMs too [60, 73, 74]. Compared to the chromosomal DNA, the eccDNA does not have high stability and segregates to daughter cells in an unequal fashion (**Figure 1**). As per the Mitelman database, 1.4% of cancers possess DMs, with neuroblastoma showing a maximum of 31.7% DMs [75]. However, no accurate quantification of the eccDNA has been done in the cancer cells, as there has been no systemic examination of oncogenes present in this eccDNA. At the same time, how eccDNA impacts tumor cell evolution is yet to be understood well. Although we can sequence DNA in an unbiased manner to analyze the cancer genes, the spatial resolution of amplicons is not possible yet to determine the specific regions of chromosomes of eccDNA. Moreover, DNA circularity can potentially be inferred by using bioinformatic analysis [76], but eccDNA amplicons may show variations from cell to cell. This means there has been a great underestimation of oncogene amplification related to eccDNA. Although by cytogenetically analyzing the cancer cell metaphases, the localization of amplicons can be done, there is always some bias associated with this technique. Recently, a group of researchers quantified the eccDNA spectrum in human tumor cells and systemically interrogated the contents by integrating the Whole Genome Sequencing (WGS) of 117 human tumor cell lines, varied tumor tissues from various cancer types, and cancer cell cultures derived from patients [12]. The researchers analyzed 2049 metaphases from around 72 samples of cancer cells bioinformatically and cytogenetically. In addition, they analyzed a total of 233 metaphases from eight normal tissue cultures and 290 metaphases from 10 immortal

*Circular DNA: How Circular DNA Assists Cancer Roll with Therapeutic Punches DOI: http://dx.doi.org/10.5772/intechopen.102687*

#### **Figure 1.**

 *Environmental stresses lead to the formation of circular DNA from chromosomal DNA, which can be (I) equally distributed or (II and III) unequally distributed as in most of the cases, thereby increasing heterogeneity of oncogenes when present.*

cancer cell cultures with a sum of 2572 metaphases. The eccDNA is detected by the DAPI, 4′, 6-diamidino-2-phenylindole (a fluorescent dye), which FISH and genomic DNA probes confirm. As expected, they found that eccDNA is present abundantly in cancer cell samples and was seldom present in normal cells. This shows eccDNA may have a closer association with the tumor cells than earlier thought. Moreover, almost 30% of the eccDNAs were found to be paired DMs [ 12 ]. However, it is necessary to mention that different tumors showed different levels of eccDNA, and the levels were much higher in the cultures obtained from patients. In two of 20 metaphases, the conservative metric of two eccDNAs was used, and approximately 40% of cancer cell lines and 90% of brain tumor models from patients were found to possess eccDNA [ 12 ]. There is no association between levels of eccDNA and either treated versus untreated samples, metastatic versus primary status, tumors not irradiated versus tumors that were irradiated. Moreover, with respect to the size of samples taken, it is tougher to determine the effect of different therapies on eccDNA levels undertaking the various treatment options available. There is a great variation of eccDNA number within this tumor cell culture between cells. Thus, it is confirmed that the levels of eccDNA in cancer cells are very common; however, there may be variations from cell to cell. At the same time, the eccDNA levels in normal tissues are quite rare. Using Whole Genome Sequencing, focal amplifications were revealed from cancer cell lines of different types that included amplified oncogenes defined earlier from 13 varied types of tumors [ 77 ]. Surprisingly, most cancer oncogenes are present on eccDNA only and other Homologous Staining regions of the chromosomes. Moreover, mRNA transcripts are high expression levels within the oncogenes present on eccDNA. At the same time, the diversity in the copy number of oncogenes present on eccDNA is much greater than the diversity of the copy number of genes presents on other chromosomes ( **Figure 2** ) [ 12 ].

 Researchers have studied the origin of both intra- and extra-chromosomal structures and try to determine whether they originate from the same or different

#### **Figure 2.**

 *EccDNA plays complex roles in a TME which include (a) tumor heterogeneity: Tumor cells shown in red receive an unequal number of circular DNA molecules shown as yellow dots. (b) Oncogene amplification: Increase in a number of a particular oncogene as depicted by red-tailed blocks (c) liquid biopsy: A test used to detect cancer by measuring circulating eccDNA levels as shown by dark-red ovals (d) inducing drug resistance: Oncogenes develop resistance to drugs depicted as yellow cylinders.*

precursors. The relationship that exists between amplicon and sub-nuclear regions was understood by taking advantage of GBM39 subclone cells, which occur spontaneously and in which EGFRvIIIs high copies shift from eccDNA to Homogeneous Staining Regions [ 12 ]. The eccDNA amplicon on separate GBM39 replicates showed a circle-shaped structure with about 1.29 Megabases that contained 1 EGFRvIII copy. Surprisingly, the GBM39 subclone, which harbored EGFRvIII on homogeneous staining regions, showed a similar structure with tandem duplications containing multiple EGFRvIII copies. This indicates that EGFRvIII, which contained eeCDNA structures, integrated to form Heterogeneous Staining Regions. On this eeCDNA, the reversible loss of EGFRvIII leads to resistance in GBM39 cells to the inhibitors of EGFR tyrosine kinase [ 78 ]. The EGFRvIII amplicon that contains eccDNA is conserved in the naïve cells as indicated by the structural analysis of these structures, which indicates that eccDNA maintains its primary structural properties by relocating to HSRs on chromosomes [ 13 , 60 ].

 Now, whether is the localization of eccDNA is beneficial or not is yet to be studied. However, a hypothesis about the same exists, which postulates that eccDNA amplification may be responsible for helping an oncogene get a high copy number. This way, tumor genes are able to replicate and start forming products necessary for the overall survival and growth of a tumor. This hypothesis may be true as eccDNA containing oncogenes is unequally segregated to the tumor cells due to intrachromosomal amplification [ 79 ]. Moreover, experiments have been carried out using a branching process (Simplified Galton-Watson) to explain how tumors evolve. In these experiments, either replication or death of a cell in the current generation occurs to build the upcoming generation. Considering some assumptions, they found that independent segregation of the eccDNA copies into the two daughter cells occurs during the process of cell

#### *Circular DNA: How Circular DNA Assists Cancer Roll with Therapeutic Punches DOI: http://dx.doi.org/10.5772/intechopen.102687*

division. Moreover, the same study confirmed that oncogene favors the development and replication of an oncogene in a better way. To strengthen and prove this point, the researchers have found a greater copy number of oncogenes c-MYC and EGFR in the eccDNA rather than in the chromosomes. Moreover, the intra-chromosomal amplification of an oncogene results in the stabilization of heterogeneity of a tumor at a lower level. In comparison, the eccDNA is unequally segregated, resulting in greater tumor heterogeneity and maintaining it [80, 81]. Also, the copy number of an oncogene may increase the tumor heterogeneity but is likely to do so quite slowly if present on a chromosome than if the same oncogene is present on an eccDNA molecule. Moreover, there is strong evidence that it is quite difficult to cure genetically heterogeneous tumors [80]. The tumor cells have the ability to maintain the oncogene transcriptional level and copy number variability from cell to cell, which results in resistance to drugs and the progression of tumors. The worst thing about eccDNA is that the oncogene amplification in eccDNA enables the tumor adaptability more effectively, helping it survive tough microenvironment conditions. This is further done because of an increase in the chances that a particular cell population expresses a specific oncogene at such a level that considerably increases the survival and proliferation of a tumor [60, 78, 82–85]. This worsens the situation even more as it becomes immensely difficult to treat such tumors after they become progressively aggressive. Thus, the extra and intrachromosomal amplification mechanism varies greatly, leading to high copy number heterogeneity and greater amplicons copy number. It means that even if oncogenes present on eccDNA confer a little selection advantage increase, it will still lead to a greater advantage of fitness for a tumor. Thus, as far as the evolution of a tumor is concerned, there is certainly a great role for oncogene present on eccDNA to play as they are linked to tumor heterogeneity and greater survival. This does not happen when the same gene (even if) is present on a chromosome. Thus, it is of immense importance and vitality to understand how a tumor evolves on a molecular level and how oncogene amplification occurs in eccDNA. This will help the scientific community to effectively treat tumors by either preventing their progression or successfully eradicating them.

When it comes to detecting eccDNA, the circulating levels of this DNA aid in the noninvasive diagnosis of tumors, thereby helping manage them. This becomes possible because tumors and normal tissues have the property of releasing eccDNA into circulation. The eccDNA has been observed in the mammalian tissue nuclei and other cell lines. However, researchers are demonstrating that eccDNA with unique mapping regions are detectable in serum and plasma obtained from both humans and mice [85]. Moreover, the eccDNA obtained from the serum and plasma has longer lengths with over 250 base pairs than the one found in cells (around 150 base pairs). Researchers have detected human microDNA in mouse circulation after transferring human cancer lines. Moreover, when microDNA from normal cells and tumor cells was compared, it revealed that longer micro-DNA occurred in the tumor cells. Moreover, researchers even collected the micrDNA samples from cancer patients before and after the surgery and found that longer and higher concentrations of microDNA are released into circulation when the tumor is there and shorter when the tumor is excised. This indicates that circular DNA is not confined to cells (cytoplasm) only but can be thought of as everywhere in the presence of a tumor [86].

#### **3.1 Functional importance of eccDNA with emphasis on tumors**

The eccDNA performs a varied number of functions within and around the cells. In tumor presence, a person shows a higher concentration of circular DNA in and around the cells, indicating an ever-thought greater role. Although insufficient research has been performed on the eccDNA and its functions in tumors, they certainly have been found to contribute to genetic heterogeneity in tumor cells. They are responsible for oncogene amplification, a hallmark of many cancer types, thereby inducing drug resistance. Thus, a particular tumor is more likely to express an oncogene on the eccDNA, providing it with a suitable environment for the proliferation, progression, and metastasis of a tumor. Apart from this, numerous theoretical functions of eccDNA have been reported, including the gene dosage compensation, heterogeneity among cells, transcription factor sponging. Moreover, they have a predominant role in producing a mutational pool of DNA that helps tumors evolve, a role in intercellular communication, aging, stimulating certain innate immune pathways, and uses in the technique of liquid biopsy. Numerous researchers have studied the role of eccDNA in tumors and unequivocally revealed the role of eccDNA in tumors and inducing drug resistance. For instance, several drug-resistant genes and oncogenes are carried on DMs, which leads to the advancement of cancer by the phenomenon of amplification of genes [13, 87–89]. With more advanced research being done in the field, it was revealed that it is much more common for eccDNA to mediate amplification of genes that thought earlier. Thus, the presence or absence of eccDNA is vital for the evolution and heterogeneity of tumors. For instance, around 40% of the cancer cell lines and around 90% of brain tumor cell lines from patients showed eccDNA presence [41]. Apart from this, researchers have provided experimental and mathematical evidence suggesting that heterogeneity among tumors and amplification of driver oncogenes are much greater upon amplification on eccDNA than when the same happens on chromosomal loci. Furthermore, an immense proliferation advantage is acquired when eccDNA is distributed randomly to the daughter cells. A greater eccDNA copy number with an oncogene can be inherited [12]. Tumor cells engage in great adaptive mechanisms that help cancer cells survive in whatsoever conditions. For example, with respect to environmental conditions, the particular eccDNA number in tumors is changed, thereby helping tumor cells adapt with a different mechanism. This has been found to occur in glioblastomas, wherein mutation of EGFR is common, which results in the development of an oncogenic EGFRvIII variant. Tumor cell proliferation and growth are promoted by EGFRvIII, which also increases the sensitization of cancer cells to tyrosine kinase inhibitors [78]. Moreover, when cells lose DMs that carry mutated EGFR, they become resistant to the EGFR tyrosine kinase inhibitors [78]. It is also believed that extrachromosomal driver mutations are possible that occur when eccDNAs amplify, which proves vital for helping tumors evolve [81]. Thus, it can be said that the presence of eccDNA occurs more commonly in tumors resulting in adaption, heterogeneity of tumors, and thus their evolution (**Table 2**).

Apart from these functions, eccDNA has a role in aging, which is directly or indirectly linked to the tumors. In Yeast, the accumulation of eccDNAs containing ribosomal RNA genes occurs, thereby contributing to Yeast cell aging [88]. These ribosomal DNAs can replicate because of the presence of an Autonomously replicating sequence (ARS) sequence [88]. Furthermore, they show a tendency to transfer to mother cells during each cell division that leads to a higher accumulation of eccDNAs in aging mother cells. The lifespan of the daughter cell is prolonged with this lesser eccDNA concentration. However, researchers do not know the exact mechanism of how senescence is triggered by the eccDNAs, eventually leading to the mortality of aging cells. But it is presumed that it affects the replication and transcription mechanism by titrating different components involved [90]. The phenomenon that

*Circular DNA: How Circular DNA Assists Cancer Roll with Therapeutic Punches DOI: http://dx.doi.org/10.5772/intechopen.102687*

#### **Table 2.**

*Difference between chromosomal DNA and extrachromosomal circular DNA in promoting tumour survival and aggressiveness.*

eccDNAs occur abundantly in these cells may be responsible for senescence and mortality of aging cells. Considering this hypothesis, expressing ARS plasmid at an abnormal place is enough to arrest aging cells, which eventually leads to their death [90]. Since eccDNAs have been found to accumulate in normal cells, too, the discoveries have led scientists to scratch their heads whether accumulating levels of eccDNA cause aging in other higher eukaryotes or not. Besides relating to aging in Yeast, eccDNA has a similar role in mammals [90–92]. The concentrations of eccDNA in cells serve as an index of aging, as was found in senescence-resistant SAM-R mice, where they showed a higher amplification [93]. Another study supported this finding in which a stronger ERC load resulted due to sgs1 gene mutation, which associates with reduced life span and premature aging [90]. Another study studied the eccDNA replication in the cell cycle, where the concentrations increased in an exponential manner. This may be a sort of clock determining the yeast life span. In Yeast, the rise in the mortality rates also correlates with the increase in ERC numbers [90]. Some other studies focused on the relationship between the formation of eccDNA and transcriptional activity. It was found that certain genes that are sensitive to environmental stimuli are transcriptionally stimulated, which triggers protein-coding eccDNA generation in aging budding yeasts [94]. Similarly, under aging conditions, the structural characteristics of eccDNA were studied in cultured human lung fibroblasts and

rat lymphocytes. The results showed a greater size of eccDNA, and high dispersion with the eccDNA almost doubles in number [91]. This means that the accumulation of eccDNA in tumors is primarily because of the upregulation of the synthesis rather than the downregulation of degradation.

In addition, other roles of eccDNA may also exist, such as gene compensation [95]. For instance, in Yeast two pairs of genes-HTA1-HTB1 and HTA2-HTB2 encode histones H2A and H2B, respectively. Upon deletion of HTA1-HTAB1, HTA2-HTB2 genes are amplified through dosage compensation by forming a naïve eccDNA. This eccDNA contains 39 kilobase pairs of the 2nd chromosome, including a centromere, HTA2-HTB2, H3-H4 locus of histones, and several origins of replication [95]. The creation of this naïve eccDNA occurs when two Ty1 retrotransposon elements recombine, flanking this portion [95]. Thus, the compensation for the H2A and H2B decrease occurs through elevation in HTA2-HTB2 eccDNA formation in these deleted strains of HTA1-HTB1. In comparison to DMs, the smaller types of eccDNAs occur more commonly, but we do not know much about them yet [46, 84]. Their size is little to have genes encoding proteins but larger enough to encode parts of genes or short RNAs with regulatory functions. Moreover, microDNAs have the property of acting as molecular sponges; indirect gene expression occurs when they sponge different transcription factors. More recently, it became clear that the microDNAs may occur in plasma and serum of both human beings and mice as circulating DNA [84]. In the technique or liquid biopsy, they may act as potential biomarkers. A novel cell communication mechanism may exist if microDNAs are present in other cells. This may be just a theoretical assumption right now but may open new fields of investigation in the future. Another important query to solve is how this eccDNA survives autoimmune pathways in the cytoplasm. If the naked DNA is present in the cell cytoplasm, then the cGAS pathway is activated, which stimulates the immune system through interferon expression [96, 97]. This is how our bodies respond to any foreign antigen entering our bodies and vital parts of the immune system. EccDNAs release may be stimulated during mitosis, which either leads to their degradation by TREX1 like enzymes or activation of cGAS pathways. Thus, it can be said that the eccDNAs are usual endogenous antigens if not having chromatin protection, which leads to activation of several autoimmune pathways.

#### **3.2 Clinical utility**

Tumors possess specific characteristics that help in their prognosis and identification. One of them is the presence of eccDNA, which may be an essential tool for tumor prognosis. However, having said that, normal tissues also release eccDNAs; thus, it becomes pivotal to identify these differences between normal and cancerous tissues. One of the ways to do so may be identifying the eccDNA length. For instance, research indicates that tumor cells of human origin show longer eccDNA molecules than those found in the normal mouse cell lines [46]. Similarly, both normal and cancerous lung tissues showed the presence of microDNA, with both the types showing the same known properties of eccDNA [84]. However, the eccDNA was removed and analyzed from human patients who have lung cancer, and the length of eccDNA was measured. It was found that eccDNA from the same patient shows variations in length, with tumor eccDNA larger in size than the normal cell eccDNA [84]. This property will certainly help researchers to differentiate cancerous and normal tissue and might prove a good biomarker. This will be true if eccDNA from normal and tumor cells show a predictable behavior. At the same time, many researchers have focused on

#### *Circular DNA: How Circular DNA Assists Cancer Roll with Therapeutic Punches DOI: http://dx.doi.org/10.5772/intechopen.102687*

liquid biopsy, which used high-throughput sequencing technology for the identification of linearized DNA fragments specific to tumors in the plasma or serum [78]. This discovery has rather been more recent [84]. Moreover, the microDNA obtained from mice, mice tissues, human tumors, and chicken showed the same characteristics as that of the microDNA obtained from circulation. These properties include distribution in genome, higher GC content, direct repeats that are 2–15 bases long and flank the source genomic sites, etc. Moreover, it is an intergenic and genic region that gives rise to eccDNA. As mentioned, the microDNA obtained from lung cancers was of greater length than microDNA from normal tissue of the same person. The surgery also affects the eccDNA length, as observed in some studies [84]. Researchers found longer circular DNA levels in patients prior to surgery and smaller lengths post-surgery (6 weeks after surgical resection) [84]. This indicates the stability of eccDNAs in comparison to linear DNA and thus may be advantageous to use eccDNA for the purpose of liquid biopsy.

#### **4. Conclusion**

The eccDNA has a great role in the evolution of cancer as driver oncogene amplification contributes to heterogeneity of tumors and resistance to drugs. What makes the condition worse is that driver oncogenes present on eccDNA prove to be more harmful than in chromosomal DNA. The frequency of eccDNA has been much more common than was earlier thought, helping tumors evolve and become resistant to various drugs. The latest research conducted on the topic also found that the presence of eccDNA in nearly all tumors is much more common, providing immunity against therapeutic drugs [12]. The team explored the eccDNA presence after analyzing cell lines from 17 different tumor types. This was done by taking metaphase chromosomes from 2572 dividing cells whose ECdetect (a software package for conducting unbiased analysis and detecting eccDNA) was developed to study structural and functional properties. They concluded that almost half of the human cancers showed eccDNA presence. Moreover, eccDNA aids in better driver oncogene amplification than the chromosomal DNA, thereby accelerating transcript level [12]. Importantly, it is clear that eccDNA has a much greater role in promoting drug resistance, diversity, and growth of the tumor cells than the same genes located on chromosomal loci. This explains how the evolution and diversification of cancers occur. Moreover, around 90% of the tumors (patient-derived) models show eccDNA presence, meaning that eccDNA is more likely to harbor cancer oncogenes than chromosomes. The copy number of an oncogene and intratumoural heterogeneity is increased more effectively by the eccDNA amplification as predicted by mathematical models [12]. Moreover, a growing number of studies are showing the role of eccDNAs in harboring proto-oncogenes. The eccDNA is not only offering them a suitable environment to sustain themselves but also making them resistant to numerous drugs. Different genes show a link with different cancers, such as DHFR gene with colon, cervical and breast cancer, CA125 gene with ovarian cancer, MDR1 gene with oral squamous cell carcinoma (OSCC), a c-Myc gene with colon cancer and leukemia, HER2 gene with breast cancer, MDM2 and EGFRvIII with glioblastoma, as depicted in **Table 3** [78, 98–106]. Thus, tumors maintain their high gene number and heterogeneity more easily with the help of eccDNA. Moreover, the distribution of eccDNA to the daughter cells occurs randomly, meaning that a tumor may have either whole or no eccDNA. This increases the variation in the copy number of oncogenes, which ultimately makes the tumor more heterogeneous in terms of

#### **Table 3.**

*Some common oncogenes that are contained on eccDNAs.*

resistance to any kind of environmental changes, like due to drugs. The more common discovery of eccDNA in different cancer types is surprising as researchers have been focusing more on which genes cause cancer rather than where these genes occur. Although some cancer biologists reported the eccDNA presence as early as the 1960s, the tools to quantify eccDNA were lacking. More studies are demanding to know the exact mechanisms of eccDNA formation and maintenance and how the Tumor Microenvironment changes eccDNA levels by altering its composition.

#### **Conflict of interest**

The authors declare no conflict of interest.

### **Other declarations/Notes**

None.

#### **Abbreviations**

*Circular DNA: How Circular DNA Assists Cancer Roll with Therapeutic Punches DOI: http://dx.doi.org/10.5772/intechopen.102687*

### **Author details**

Parvaiz Yousuf Department of Zoology, Central University of Kashmir, Ganderbal, Jammu and Kashmir, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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