EBV-associated epigenetic signature in a colorectal cancer multicase family.

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Reem Hamad, Ommniyah Yousif, Razaz Idris, Rayan S Yousif, Bidour Kamal, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3643167/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: EBV is the first culprit virus that has been linked to human cancers. It has been shown recently to act through various epigenetic mechanisms, including DNA hypermethylation, EBV-related miRNA, and RNA editing enzymes. Methods: Whole exome sequencing of an extended multi-case colorectal cancer family revealed a potential oncogenic viral etiology. Investigation of such putative involvement in the same family was carried out by interrogating exome sequences of the tumor tissue for the presence of EBV signatures, in addition to immune histochemistry analysis and PCR of the LMP and EBER genes. Due to previously encountered strong signals for the involvement of APOBEC3b as an RNA editing enzyme, quantitative PCR was performed to quantify APOBEC3b. Various bioinformatics tools have also been used to detect virus-related miRNAs. Results: The EBV 2 sequence was retrieved from patient tumor sequences and detected by PCR and immunohistochemistry in the tumor samples. APOBEC3b was found to be sixfold higher in the sample than in controls, which explains the high C/T transition occurrence among tumor sequences. Bioinformatic analysis revealed that has-miR-29 b and has-miR-130 were among the top related miRNAs to EBV and colorectal cancer which was further supported by expression analysis. Conclusions: These results, in addition to expanding the list of EBV-related cancers, highlight potential epigenetic mechanisms that might help explain the oncogenic functionality of the virus and the ontology of tumor complexity. Colorectal Cancer EBV APOBEC mi-RNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Epstein Bar Virus (EBV) was the first carcinogenic virus detected in human cancer cells in 1964 in African Burkitt's lymphoma (1). The prevalence of EBV is endemic worldwide, with more than 90% of the population infected, the majority asymptomatic or mild infection (2). EBV accounts for approximately 1.5% of all cancers globally (2). EBV is associated with many human cancers originating from B lymphocytes and epithelial and mesenchymal cells (2,3). EBV-associated neoplasms affect both immune-competent hosts and immunocompromised patients. The development of an EBV-associated neoplasm is an outcome of environmental factors and genetic susceptibility to viral infection that is allied with immune deregulation (3). EBV infection affects several cells signaling pathways that induce alterations in host methylation profiles, leading to extensive methylation of both human and viral genomes, resulting in the abnormal expression of genes involved in cytokine regulation, cytoskeleton formation, cell proliferation, and cell adhesion, which promote viral persistence and propagation (4-7). EBV infection is associated with many elevated transcription factors levels by an epigenetic mechanism that remains active even after a complete loss of the viral genomes (8). Therefore, EBV infection can induce 'hit-and-run' mutagenesis in a cell, a process that, in theory, could contribute to carcinogenesis, tumor progression, or even metastasis. Higher mutation rates were found specifically in CRC metastatic lesions when compared to corresponding primary tumors, some with evidence of EBV-derived sequences [9,10]. The current paper is a continuation of previously published work where these colorectal samples are hypermutated (11). In the literature, hypermutated cancers may be associated with AID/APOBEC enzymes. During the dynamic host‒pathogen equilibrium of EBV and its human host, the virus survives and thrives within infected individuals who respond by a mechanism of innate immunity, such as single-strand DNA editing by Apolipoprotein B Editing Catalytic subunits proteins 3 (APOBEC3s), which is a robust and well-conserved system of innate immunity that mutates and inactivates viral genomes [12, 13]. APOBEC3 cytidine deaminases can edit single-stranded viral genomes such as EBV by inducing cytidine-to-uracil mutations in viral DNA [14]. In addition to activation of RNA-editing enzymes, EBV exploits host miRNAs to escape the immune system. EBNA2 is a viral protein that is expressed during type III latency and upregulates miR-21, which subsequently downregulates myeloid differentiation factor 88 and IL-1 receptor-associated kinase 1 (15). The miR-17-92 cluster, which is essential for immune cell differentiation, is highly expressed in EBV-positive tumors, such as NPC (16) and DLBCL (17). High expression of miR-17-92 in B cells, T cells, NK cells, macrophages, and dendritic cells is known to inhibit cellular differentiation and function (18).In EBV-infected B lymphocytes, viral LMP1 activates NF-κB signaling and host miR-155. However, miR-155 attenuates NF-κB signaling to stabilize persistent infection (19). Here, we extend our analysis of a CRC family where evidence of nonrandom mutagenesis was observed initially (11) to unravel the molecular basis of these patterns, including the possible role of editing enzymes and other epigenetic factors. The dual role of AOPBEC enzymes with viruses makes the consideration of this family in carcinogenesis all more interesting. Viruses are a major etiological agent for several tumors in Sudan, where the family under investigation comes from Materials And Methods Sample collection Whole exome sequencing (WES) was carried out for two cases and three controls from the same family as described previously (11). Additionally, six biopsies from nonfamilial CRC cases were collected to confirm the in-silico work at the laboratory. A histopathology test to confirm CRC was conducted on all biopsies. A-EBV detection in colorectal cancer tissues: We combined the three available methods for EBV detection in colorectal cancer as follows: 1-3-Next Generation Sequencing: The main idea is to detect the EBV sequences in the familial colorectal Exome samples. Firstly, alignment of the fastq files with hg19 reference genome using bowtie2. After obtaining the BAM files, we extracted the unmapped read to human genome and aligned them with EBV reference genome using blat. 2-In situ hybridization (ISH): ISH was carried outfor the available biopsy materials of CRC patients from the same family with an EBV peptide nucleic acid (PNA) probe/fluorescein (Dako, Denmark) and detected by a PNA ISH detection kit. Negative and positive controls were supplied by the manufacturer. A nasopharyngeal carcinoma type III biopsy served as an additional positive control. A case was positive if the nucleus of the tumor cell stained dark blue or black (20,21). 3-DNA amplification using PCR : Six biopsies from nonfamilial CRC cases were collected to confirm the presence of EBV DNA. Specific regions of the viral genome were amplified by using two primers (Epstein Barr Encoded RNA (EBER) gene and Latent Membrane Protein-1 (LMP-1) gene). Primer designs were described previously (22,23). DNA from EBV-positive nasopharyngeal carcinoma (NPC) was used as a positive control, and nuclease-free water to replace DNA was used as a negative control. B-APOBEC family analysis: 1-Evaluation of colorectal cancer SNPs In WES, SNPs were queried whether they were in known CRC genes or cancer hit genes or had a high impact, and functional analysis was performed according to ACMG guidelines (24). The proportion of (G/A- C/T) transitions was calculated in all SNPs. Networks of all SNPs were analysed using Cytoscape. 2-APOBEC gene SNP prioritization: Using the WES samples, SNPs in APOBEC family genes were analysed according to ACMG guidelines by VEP (25), SNP-nexus (26) and Varsome (27). 3-APOBEC3b expression Analysis: Due to the strong association of APOBEC3B with hypermutated colorectal cancer, APOBEC3B expression analysis was carried out among the six biopsies from sporadic CRC using qRT‒PCR. C-miRNA analysis: Human miRNA analysis was carried out using different bioinformatic tools ( Figure 1 ). 1-Prediction of 3` UTR miRNA dysfunction Given that 80% of the miRNAs are in the 3` UTR variants, all 3`UTR regions of the WES were analysed. Moderate- and high-impact SNPs in the 3’UTR and CRC-related genes were selected according to ACMG guidelines. 2-Identification of SNP-related miRNAs PolymiRTS (Polymorphism in microRNA Target Site database) was used to predict and prioritize miRNAs related to genes with significant SNPs. (21) 3-Functional prediction and network analysis of related miRNAs: For the selected miRNAs, biological pathways were analysed with the following tools: miRNet for network analysis and the relation between disease and miRNA (28). DIANA miRPath (29) was used to investigate the combinatorial effect of microRNAs, mutated SNPs, and biological pathways. Moreover, the Co-expression Meta-analysis of miRNA Targets (CoMeTa) tool (30) was used to rank miRNA targets according to their degree of co-expression, identify miRNA regulatory networks, and predict biological function. Finally, the tool for annotations of miRNAs (TAM) was used to investigate families, clusters, functions, associated diseases, and tissues. (31) D-Expression analysis of miRNA: miRNA130 and miRNA-29 were selected due to their strong association with CRC and EBV upon the insilico analysis. miRNA was verified using qRT‒PCR from six CRC patients and their matched normal tissues and blood. RNA was isolated using the EasyBlue isolation regent (iNTRon., Korea) cat no. 17061. Results EBV detection: TheEBV-2 sequence was detected in the available CRC WES which was supported by ISH. The confirmation of EBV DNA presence in the six unrelated cases by positive PCR ( Figure 2 ). APOBEC family analysis: CRC gene SNP analysis: We focus here on mutation analysis intending to understand tumorigenesis from the angle of somatic changes, i.e., the incidental changes that set-in action cancer functional phenotypes. We started with an initial input of 43186-44886 total SNPs in CRC cases and 43832-44967 in controls. First, we identified the SNPs that are in cancer-hit genes. Then, we prioritized them according to the ACMG guidelines and chose pathogenic and likely pathogenic ones. ( Supplementary table 1 ) Upon analysis of filtered SNPs, the total read of variants in tumor sample exomes was twofold higher than that in controls (Fisher exact test P = 0.001) ( Figure 3a ). Editing signatures based on analysis of the transition/transversion ratio for novel SNPs show an excess of C/T, T/C, G/A, and A/G transitions (89%) in the exonic parts in tumor tissues (P=0.0001), while the 3' UTR shows a more uniform distribution of classes ( Figure 3b ). Cancer patients' networks could be distinguished from the control's network by their network topology of the novel damaged SNVs in visualization ( Figure 4 ). Interestingly, this figure highlights the role of these somatic changes in the formation of cancer circuitries when compared to noncancerous tissue. It also shows the complexity of the cancer network that should be considered to understand the disease pathogenesis. Not only are the proteins of centrality in the control disproportionately few, but the top scores in centrality analysis in CRC patients were cancer-associated proteins, while the top proteins in control samples were not, perhaps accounting for the tumor's capacity to execute multiple functions and phenotypes, including phenotypes known as hallmarks of cancer ( Figure 4 ) Although both tumors displayed adequately complex networks, there were shared and unique circuitries indicating the unique features of each tumor. APOBEC gene variant analysis: The total number of SNPs that occurred in APOBEC family genes among cases and controls was 124 SNPs, 52 of which were nonsynonymous SNPs, while the other 72 were synonymous. From the total number of SNPs, 70 were exonic, 37 were intronic and 17 were in the 3’UTR (Supplementary Table 1). Only one exonic nonsynonymous SNP (rs17000556) was found in A3A in the control sample “26”. ( Supplementary table 2 ) The total number of SNPs in A3G in all samples was 10; two of them were intronic, while the other 8 were exonic. Of those 10 SNPs, 3 were nonsynonymous. These nonsynonymous SNPs were found in the control samples. Two of the 3 A3G SNPs (rs8177832) and (rs17496046) were nonsynonymous in the control samples, while the rs5757465 SNP was synonymous but shared between cases and controls. All of the SNPs were found to be benign. ( Supplementary table ) APOBEC3b expression analysis: The APOBEC3B gene expression was approximately sixfold higher in cancer tissues than in noncancerous tissues. miRNA analysis: A total of 2700 genes with 3`UTR dysfunction were found in the WES. Only 7 genes had moderate and high impact SNPs at the 3'UTR. ( Supplementary Table 3 ). Eighteen of the colorectal cancer actionable genes had 3'UTR dysfunction ( Supplementary Table 4 ). The Polymerit database showed 107 miRNAs related to colorectal actionable genes and 37 miRNAs associated with the selected genes with high and moderate impact 3`UTR dysfunction. ( Supplementary table 3 and table 4 ). The Mi-Net tool showed that hsa-miR-214-p3, hsa-miR -484, hsa-miR-766-3p, and hsa-miR -130-2p are associated with colorectal cancer. In addition, hsa-miR-17-3p and hsa-miR-26 are associated with EBV ( Figure 5 ). Prediction based on the miRNA regulatory effect on damaged SNPs: The results showed that 30% of these genes were controlled by hsa-miR-29a-3p. Pathway analysis using the DIANA tool: Different binding sites were controlled by hsa-miR-29a-3p and hsa-miR-130-2p. Our list of SNPs and hsa-miR-29a-3p was significantly associated with CRC (p= 0.0018) The most significant biological functions for miRNA130a using the COMETA tool were cell migration (p= 0.0001), negative regulation of transcription from the RNA polymerase II promoter (p= 0.0003), negative regulation of cell growth (p= 0.0004), and covalent chromatin modification (p= 0.0005). Interestingly, a significant correlation between miRNA29 and the apoptosis pathway was predicted (p = 0.002, FDR: 0.032). Expression analysis of miRNAs: The expression levels of hsa-miR-130-2p and hsa-miR-29a-3p in tissue samples were 6.5 and 3.71 higher, respectively, than those in normal controls. Expression profiles in the matched blood samples showed a lower level of expression in hsa-miR-130-2p (0.72) but not in hsa-miR-29a-3p (2.14) ( Figure 6 ). Some similarity in the expression pattern was observed between hsa-miR-29a-3p and hsa-miR-130-2p. Discussion The association of EBV with cancer among Sudanese patients has been established in several cancers, especially epithelial-origin cancers, including esophageal cancer (26), gastric carcinoma (32), breast cancer (22), and nasopharyngeal carcinoma (23,33). Here, we investigated the possible epigenetic mechanisms by which EBV can induce carcinogenesis in CRC. We were able to confirm the presence of EBV in our samples by different methods of detection to overcome every method limitations (34). Although the association between EBV and CRC is still debatable (34,35), some research has hypothesized that EBV can induce CRC (36-39). In situ hybridization has the advantage of discerning the virus location, whether in the B lymphocytes—its native location—versus the epithelial tissue where carcinogenesis takes place. Interestingly, especially within the intestine, it was reported that EBV-derived molecules can transmit from B lymphocytes to epithelial cells via microvesicles [40]. These microvesicles can contain different EBV-derived molecules, such as LMP1, one of the major EBV-related oncogenes, or noncoding RNAs (ncRNAs; EBERs) (41,42). For EBV to exert its oncogenic effects, this occurs through manipulating the host epigenetic mechanisms by the virus, which includes the methylation machinery (4) and editing enzymes, namely, the cytidine aminase/AID family of proteins that evolved originally as a well-conserved system of innate immunity that mutates and inactivates viral genomes and endogenous retroelements (43,13). In the current report, we investigate the possibility of editing in cancer tissues through members of the apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC) family of enzymes by comparing the percentage of A–G and C–T transitions, which turned out to be significant (11). This is added recent genomic observation that APOBEC signatures were enriched in some CRC primary tumor and corresponding metastasis samples [9] and is supportive of the association between EBV and APOBEC3, since APOBEC3 enzymes, among other activities, have specific functions in the defense against viral infections [9,44,45]. Recently, APOBEC3 proteins linked viral infections to cancer development. For example, in breast cancer, APOBEC3B mRNA was overexpressed in normal breast epithelial cells transfected with HPV, which caused a significant increase in γ-H2AX foci formation and DNA breaks, which were blocked by the knockdown of both HPV and APOBEC3B (46). AID is an activation deamination enzyme that leads to antibody diversity (47). APOBEC3 comprises eleven members in Homo sapiens enzymes” and has a role in cancer development. APOBEC3-mediated deamination in the TCW motif (W = A or T) was found in multiple tumors, such as colorectal cancer (46,48-53). APOBEC3G in humans, such as mice, is associated with CRC with liver metastasis (54). Marouf and his colleagues showed that 4 SNPs in the APOBEC3B gene and 1 SNP in the APOBEC3A gene were associated with increased breast cancer risk and/or clinical outcome (55). Cancer was viewed for quite some time as a random mutagenesis process. This view is progressively recessing, with mounting evidence of a highly regulated process of organizational nature where several effector molecules are prominently involved in APOBEC-mediated DNA deamination as a potential cause of cancer-associated somatic mutations (56,57), as well as other endogenous regulators such as miRNA that are largely responsible for the specificity of the various tumors and their mutagenic capabilities, which are more likely to spark a tumorigenesis process (11). The ability to initiate hallmark aspects from cell division to metastasis is intriguing. To view carcinogenesis as a complex procedure that engenders a group of phenotypic cellular changes that bring into the limelight molecules such as the APOBEC family of proteins, as the complexity of the carcinogenesis process insinuates a role of molecules that can control and regulate such disparate functions, this was attested by the network topology of the three cases versus the controls where the connections were concatenated in a more complex fashion implying a potential highjack by the tumors of cellular mechanism through an EBV/APOBEC-based mechanism. Although both tumors displayed adequately complex networks, there were common and unique circuitries indicating the unique features of each tumor. Not only are the proteins of centrality in the control disproportionately few, but the top scores in centrality analysis in CRC patients were always cancer associated proteins, while the top proteins in control samples were not, perhaps accounting for the tumor capacity to execute multiple functions and phenotypes, including phenotypes known as hallmarks of cancer. Of those genes of centrality that appeared in our network associated with EBV, CRC, and miRNA, as shown in Figure 5, are PIK3R2 and PIK3CG. The former gene is involved in tumor metastasis and increased expression in melanoma, breast, and colon cancer, as well as in lung squamous cell carcinoma (LUSC) (58-60), which is also involved in EBV-infected cells. PIK3CG overexpression also promotes CRC tumorigenesis and progression (61). How do these genomic changes resulting in cancer hallmarks and unique features of epigenetic organization take place in the body of cancerous patients? Human cells are under continuous genetic and epigenetic changes along their life span resulting from endogenous and external environmental onslaughts. Cancer is driven by the accumulation of genetic mutations (somatic + germline mutations). Somatic mutations are generated from mutational processes of exogenous and endogenous exposures, DNA enzymatic modifications, and failure of DNA repair [62-64]. ssDNA-specific AID/APOBEC cytidine deaminases are among the most prominent endogenous enzymatic mutagens (9). Previous studies have identified and confirmed more than 50 distinct signatures of single-base substitution (SBS) derived from the analysis of whole-genome and whole-exome sequences (WES) of multiple cancer types [44,62, 65–69]. Mutational processes result in different mutation types with characteristic combinations of mutation types constituting different mutational signatures [62,65]. Transition of C>T is the most common mutation in human cells and is more common in many types of cancer (70). Mutational signatures reflect the activity of mutational processes that have been active throughout a person’s life [63]. The identified SBS signatures reflect processes commonly found across cancer types and processes confined to a particular cancer type. For example, signature SBS2 and SBS13, both attributed to the enzymatic activity of the APOBEC family of cytidine deaminases, are present in multiple cancer types [44]. Clustering of these mutations was recently highlighted within breast, head/neck, and other cancers (71). These clusters have been named kataegis (57), which are linked to one or more of the nine active DNA cytosine deaminases encoded by the human genome (9,11,57,70-72). Based on analysis of WES data in the 3’ UTR regions using bioinformatics approaches, we first excluded the premise that genetic changes in the 3’ UTR are driven by incidental mutation through the cytosine deaminase enzyme because of the lack of mutational signatures in the miRNA domains, which we have argued in favor of for exonic mutations (11). The synchronous expression profiles and upregulation of mi-RNA130 and mi-RNA29 are in accordance with previous studies (73-77). miR-29a-3p and miR-130a-5p are used as circulatory markers for some cancers (74,78). They are known to control many important cellular and biological functions, many of which are cancer-associated pathways, i.e., enhancement of cell proliferation and migration. Inhibitors of these miRNAs were found to affect cell survival in CRC cell lines (79). Interestingly, miRNA 29 and encoded miR-29 seed (miR-BART3) have been shown to share several mRNA targets with miR-29 (80). EBV is an established transforming virus in human B cells, so the importance of viral mimics of cellular miRNAs in viral tumorigenesis (81) and of viral analogs to miR-29 merits additional investigation. LMP-1 induces miR-29b, which results in miR-29b-mediated downregulation of T-cell leukemia gene 1 (TCL1), a protein with roles in cell survival and proliferation (82). Conclusion We present through computational approaches and molecular testing, evidence of EBV etiology in a CRC multi-case extended family. The oncogenicity of the virus is associated with epigenetic regulation mainly through APOBEC enzymatic activity, resulting in unique mutational patterns and features of tumor organization, as evident in the molecular networks of both dysfunctional genes and miRNAs. In addition, this report helps further our understanding of the complexity of cancer pathogenesis and presents cancer as a genuine multigenic process. Abbreviations CRC: Colorectal cancer EBV: Epstein Bar Virus AID/APOBEC: Apolipoprotein B Editing Catalytic subunits proteins. NPC: Nasopharyngeal carcinoma SBS:single-base substitution Declarations Ethics approval and consent to participate. The research project approved by the Ethical Committee of the Institute of Endemic Diseases, University of Khartoum. Written informed consent from all participants have been obtained. Consent for publication: Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The study was partly funded by the University of Khartoum and the Ministry of Higher Education and Scientific Research, Khartoum. Authors' contributions R H, re-analyzed the data, wrote the manuscript. OY, performed the laboratory experiments and part of the in-silico work. R I analyzed the APOBEC family. R Y, Performed part of the bioinformatics and in -silico analysis. B H, performed mi-RNA analysis. LA, analysed the EBV sequence data. T I, performed the PCR for APOBEC A S, Supported the experiments and revised the manuscript. SS,provided the patients. M I, conceived of the study, supervised the work and revised the manuscript. Acknowledgements: Much gratitude also goes to the colorectal cancer families and patients for their kind collaboration in the current study. Authors' information (optional) References Epstein MA, Achong BG, Barr YM. Virus particles in cultured lymphoblasts from Burkitt's lymphoma. 1964; 1:702–3. 10.1016/S0140-6736(64)91524-7 Kieff, E. and Rickinson, A.B. Epstein-Barr Virus and Its Replication, In Knipe, D.M., Howley, P.M., Griffin, D.E., Lamb, R.A., Martin, M.M., Roizman, B. and Straus, S. E., Eds., Fields Virology, 5th Edition, vol. 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Targeted depletion of PIK3R2 induces regression of lung squamous cell carcinoma. Oncotarget . 2016;7(51):85063-85078. doi:10.18632/oncotarget.13195 Semba S, Itoh N, Ito M, et al. Down-regulation of PIK3CG, a catalytic subunit of phosphatidylinositol 3-OH kinase, by CpG hypermethylation in human colorectal carcinoma. Clin Cancer Res . 2002;8(12):3824-3831. Alexandrov LB, Kim J, Haradhvala NJ, et al. The repertoire of mutational signatures in human cancer [published correction appears in Nature. 2023 Feb;614(7948):E41]. Nature . 2020;578(7793):94-101. doi:10.1038/s41586-020-1943-3 Helleday T, Eshtad S, Nik-Zainal S. Mechanisms underlying mutational signatures in human cancers. Nat Rev Genet . 2014;15(9):585-598. doi:10.1038/nrg3729 Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature . 2009;458(7239):719-724. doi:10.1038/nature07943 Alexandrov LB, Nik-Zainal S, Wedge DC, Campbell PJ, Stratton MR. Deciphering signatures of mutational processes operative in human cancer. Cell Rep . 2013;3(1):246-259. doi:10.1016/j.celrep.2012.12.008 Alexandrov LB, Jones PH, Wedge DC, et al. Clock-like mutational processes in human somatic cells. Nat Genet . 2015;47(12):1402-1407. doi:10.1038/ng.3441 ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes [published correction appears in Nature. 2023 Feb;614(7948):E39]. Nature . 2020;578(7793):82-93. doi:10.1038/s41586-020-1969-6 Nik-Zainal S, Davies H, Staaf J, et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences [published correction appears in Nature. 2019 Feb;566(7742):E1]. Nature . 2016;534(7605):47-54. doi:10.1038/nature17676 Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res . 2019;47(D1):D941-D947. doi:10.1093/nar/gky1015 Vejbaesya S, Luangtrakool P, Luangtrakool K, et al. NIH Public Access. 2010;199(10):1442-1448. doi:10.1086/597422.Tumor. Taylor BJM, Nik-Zainal S, Wu YL, et al. DNA deaminases induce break-associated mutation showers with implication of APOBEC3B and 3A in breast cancer kataegis. Elife . 2013;2013(2):1-14. doi:10.7554/eLife.00534. Rebhandl S, Huemer M, Greil R, Geisberger R. AID/APOBEC deaminases and cancer. Oncoscience . 2015;2(4):320-333. doi:10.18632/oncoscience.155. Chen X, Zhao W, Yuan Y, et al. MicroRNAs tend to synergistically control expression of genes encoding extensively-expressed proteins in humans. PeerJ . 2017;5:e3682. Published 2017 Aug 14. doi:10.7717/peerj.3682 Aslam MI, Patel M, Singh B, Jameson JS, Pringle JH. MicroRNA manipulation in colorectal cancer cells: from laboratory to clinical application. J Transl Med . 2012;10:128. Published 2012 Jun 20. doi:10.1186/1479-5876-10-128 Rossi M, Pitari MR, Amodio N, et al. miR-29b negatively regulates human osteoclastic cell differentiation and function: implications for the treatment of multiple myeloma-related bone disease. J Cell Physiol. 2013;228(7):1506-1515. doi:10.1002/jcp.24306 Xi Y, Formentini A, Chien M, et al. Prognostic Values of microRNAs in Colorectal Cancer. Biomark Insights . 2006;2:113-121. Volinia S, Calin GA, Liu CG, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A . 2006;103(7):2257-2261. doi:10.1073/pnas.0510565103 Zhang H, Hao Y, Yang J, et al. Genome-wide functional screening of miR-23b as a pleiotropic modulator suppressing cancer metastasis. Nat Commun . 2011;2:554. Published 2011 Nov 22. doi:10.1038/ncomms1555 Liu GH, Zhou ZG, Chen R, et al. Serum miR-21 and miR-92a as biomarkers in the diagnosis and prognosis of colorectal cancer. Tumour Biol . 2013;34(4):2175-2181. doi:10.1007/s13277-013-0753-8 Riley KJ, Rabinowitz GS, Yario TA, Luna JM, Darnell RB, Steitz JA. EBV and human microRNAs co-target oncogenic and apoptotic viral and human genes during latency. EMBO J . 2012;31(9):2207-2221. doi:10.1038/emboj.2012.63 Grundhoff A, Sullivan CS. Virus-encoded microRNAs. Virology . 2011;411(2):325-343. doi:10.1016/j.virol.2011.01.002 Anastasiadou E, Boccellato F, Vincenti S, et al. Epstein-Barr virus encoded LMP1 downregulates TCL1 oncogene through miR-29b. Oncogene . 2010;29(9):1316-1328. doi:10.1038/onc.2009.439 Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Supplementarytable2.docx Supplementarytable3.docx Supplementarytable4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3643167","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":252748879,"identity":"2e04bbcb-1178-4d29-8d64-5b3b381bb533","order_by":0,"name":"Reem Hamad","email":"","orcid":"","institution":"Institute of Endemic Diseases, University of Khartoum","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Reem","middleName":"","lastName":"Hamad","suffix":""},{"id":252748880,"identity":"0a8b6927-a51c-40af-b22e-713400e952f7","order_by":1,"name":"Ommniyah Yousif","email":"","orcid":"","institution":"Institute of Endemic Diseases, University of Khartoum","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ommniyah","middleName":"","lastName":"Yousif","suffix":""},{"id":252748881,"identity":"bd284947-269a-4cea-9da0-28f0516ae6c1","order_by":2,"name":"Razaz Idris","email":"","orcid":"","institution":"Institute of Endemic Diseases, University of Khartoum","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Razaz","middleName":"","lastName":"Idris","suffix":""},{"id":252748882,"identity":"4a8a8629-dd76-4dd4-a6f6-e04af7a4124a","order_by":3,"name":"Rayan S Yousif","email":"","orcid":"","institution":"Institute of Endemic Diseases, University of Khartoum","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rayan","middleName":"S","lastName":"Yousif","suffix":""},{"id":252748883,"identity":"afbf17a6-1473-410b-ae91-20174fc6b02a","order_by":4,"name":"Bidour Kamal","email":"","orcid":"","institution":"Institute of Endemic Diseases, University of Khartoum","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bidour","middleName":"","lastName":"Kamal","suffix":""},{"id":252748885,"identity":"e1fee11e-79d7-4ae0-80e6-b2753eba17fe","order_by":5,"name":"Lamees Ammar","email":"","orcid":"","institution":"Institute of Endemic Diseases, University of Khartoum","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lamees","middleName":"","lastName":"Ammar","suffix":""},{"id":252748886,"identity":"c07d2008-0e8d-4941-8bad-aedc8e38c1da","order_by":6,"name":"Tomader A. 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Suleiman","email":"","orcid":"","institution":"Institute of Endemic Diseases, University of Khartoum","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Suleiman","middleName":"H.","lastName":"Suleiman","suffix":""},{"id":252748891,"identity":"77c904a1-0465-494b-83e7-e7a0aea04960","order_by":9,"name":"Muntasir Ibrahim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDACZh4GBsYGCQZ+ZiCHhyQtks1Ea2EAa2FgMDhArBbddt5jH37usIg2Ps587MEbBpt8eQcCWswO8yXP7D0jkbvtMFu64RyGNMuNBwhq4TFm4G0DaeExk+ZhOGxg2ECEFsa/QC2bm/m/Ea+FGWTLBmYeNrAWeQI6wH5hlgVqmXGYzUxyjkGagQFBLefPHmZ821aX299/+JnEmwobA3lCDkMDBtAIIg2QassoGAWjYBQMfwAAmo443k2VTR4AAAAASUVORK5CYII=","orcid":"","institution":"Institute of Endemic Diseases, University of Khartoum","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Muntasir","middleName":"","lastName":"Ibrahim","suffix":""}],"badges":[],"createdAt":"2023-11-21 09:14:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3643167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3643167/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47306744,"identity":"f0be8de0-e34a-4cdf-bb12-07231277e3bb","added_by":"auto","created_at":"2023-11-29 16:07:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":300174,"visible":true,"origin":"","legend":"\u003cp\u003emi-RNA analysis Flow chart\u003c/p\u003e","description":"","filename":"Figure1.002.png","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/7b607af6b0b24b2802653e38.png"},{"id":47306746,"identity":"6d240044-0556-4458-b1af-db1f254defd1","added_by":"auto","created_at":"2023-11-29 16:07:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1205474,"visible":true,"origin":"","legend":"\u003cp\u003eEpstein–Barr virus (EBV) latent gene expression in colorectal cancer (CRC). \u003cem\u003eIn situ\u003c/em\u003e hybridization to the abundant EBV-encoded RNA (EBER) transcripts is the standard approach for detecting EBV infection in cells and tissues. \u003cstrong\u003e(a)\u003c/strong\u003eThe tumor cells are spindle shaped with elongated dark nuclei and tapering cytoplasm. The tumor resembles Adenocarcinoma of the CRC (H\u0026amp;E×40). \u003cstrong\u003e(b) \u003c/strong\u003eEBV positive cells, the black arrows show type 3 tumor cells (×40).\u003c/p\u003e","description":"","filename":"Figure2.002.png","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/4f65a1f5b464f93997d3b2c4.png"},{"id":47306745,"identity":"e1a05dc1-a4ed-4d10-95a8-a971ddbced23","added_by":"auto","created_at":"2023-11-29 16:07:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165597,"visible":true,"origin":"","legend":"\u003cp\u003eThe bulk of colorectal cancer associated SNP. \u003cstrong\u003eA.\u003c/strong\u003e Line chart shows the total SNP proportion between cases and controls. \u003cstrong\u003eB.\u003c/strong\u003eProportion of total combined SNPs C-to-T and G-to-A transition mutations detected by whole exome sequencing which is Cytidine Deaminase Signature of 3` UTR region.\u003c/p\u003e","description":"","filename":"Figure3.002.png","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/065bf935c046a0ca91bc61e9.png"},{"id":47306748,"identity":"f8d7ec6f-8d0f-4321-8a2a-39acb56272b6","added_by":"auto","created_at":"2023-11-29 16:07:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":495376,"visible":true,"origin":"","legend":"\u003cp\u003eThe\u003cstrong\u003e \u003c/strong\u003eProtein- Protein interaction network for SNPs of CRC cases and controls. By using Cytoscape software highlighting the role of these somatic changes in the formation of cancer circuitries as compared to non-cancerous tissues.\u003c/p\u003e","description":"","filename":"Figure4.003.png","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/ab5810bf4cc915743e81f330.png"},{"id":47308573,"identity":"14fc651f-f5c9-4124-b9cd-34d854fbabdc","added_by":"auto","created_at":"2023-11-29 16:15:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1187528,"visible":true,"origin":"","legend":"\u003cp\u003emi-RNA, colorectal actionable genes and EBV associated genes network. Green dots on the right for mi-RNA associated with colorectal cancer. The red dots on the left are for mi-RNA associated with EBV.\u003c/p\u003e","description":"","filename":"Figure5.002.png","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/94ea80141adc6ab075e29ec2.png"},{"id":47306749,"identity":"ab8c147c-62f1-4fb2-9b59-40bb026a7547","added_by":"auto","created_at":"2023-11-29 16:07:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":144449,"visible":true,"origin":"","legend":"\u003cp\u003eExpression Log of miRNA 29 and 130 in colorectal cancers patients using\u0026nbsp;\u0026nbsp; Quantitative rt-PCR. The 2−\u003csup\u003eΔΔCt\u003c/sup\u003e method was used for the analysis and the U6 snRNA was selected as the endogenous reference gene for normalization.\u003c/p\u003e","description":"","filename":"Figure6.001.png","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/f8f32dff6f793ed755f1718a.png"},{"id":49213643,"identity":"8f267a15-8118-4f7d-9311-a3aa263460f0","added_by":"auto","created_at":"2024-01-05 09:37:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2945005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/a6b32de9-1e4b-4129-b9c4-38ee045748fa.pdf"},{"id":47308575,"identity":"78fc472d-3e0b-48d6-a123-51621afb92d0","added_by":"auto","created_at":"2023-11-29 16:15:06","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14244,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/589c65190b815f8a1cfe3c2f.docx"},{"id":47306752,"identity":"16c2c5bb-d321-41d4-9527-217b6dc1776b","added_by":"auto","created_at":"2023-11-29 16:07:06","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13774,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/1f08d0e1b7122ed927eacd53.docx"},{"id":47306751,"identity":"b4582026-943e-4a0b-9ed3-028cf08bb681","added_by":"auto","created_at":"2023-11-29 16:07:06","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":14355,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/bdb77dde3c376013051567a8.docx"},{"id":47306753,"identity":"9eb2b460-3dd3-4421-8b03-4bbbd6a1418c","added_by":"auto","created_at":"2023-11-29 16:07:06","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":17920,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3643167/v1/c1b9e32d9ec82f4f4140739c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"EBV-associated epigenetic signature in a colorectal cancer multicase family.","fulltext":[{"header":"Background","content":"\u003cp\u003eEpstein Bar Virus (EBV) was the first carcinogenic virus detected in human cancer cells in 1964 in African Burkitt\u0026apos;s lymphoma (1). The prevalence of EBV is endemic worldwide, with more than 90% of the population infected, the majority asymptomatic or mild infection (2). EBV accounts for approximately 1.5% of all cancers globally (2).\u003c/p\u003e\n\u003cp\u003eEBV is associated with many human cancers originating from B lymphocytes and epithelial and mesenchymal cells (2,3). EBV-associated neoplasms affect both immune-competent hosts and immunocompromised patients. The development of an EBV-associated neoplasm is an outcome of environmental factors and genetic susceptibility to viral infection that is allied with immune deregulation (3).\u003c/p\u003e\n\u003cp\u003eEBV infection affects several cells signaling pathways that induce alterations in host methylation profiles, leading to extensive methylation of both human and viral genomes, resulting in the abnormal expression of genes involved in cytokine regulation, cytoskeleton formation, cell proliferation, and cell adhesion, which promote viral persistence and propagation (4-7). EBV infection is associated with many elevated transcription factors levels by an epigenetic mechanism that remains active even after a complete loss of the viral genomes (8). Therefore, EBV infection can induce \u0026apos;hit-and-run\u0026apos; mutagenesis in a cell, a process that, in theory, could contribute to carcinogenesis, tumor progression, or even metastasis. Higher mutation rates were found specifically in CRC metastatic lesions when compared to corresponding primary tumors, some with evidence of EBV-derived sequences [9,10].\u003c/p\u003e\n\u003cp\u003eThe current paper is a continuation of previously published work where these colorectal samples are hypermutated (11). In the literature, hypermutated cancers may be associated with AID/APOBEC enzymes. During the dynamic host‒pathogen equilibrium of EBV and its human host, the virus survives and thrives within infected individuals who respond by a mechanism of innate immunity, such as single-strand DNA editing by Apolipoprotein B Editing Catalytic subunits proteins 3 (APOBEC3s), which is a robust and well-conserved system of innate immunity that mutates and inactivates viral genomes [12, 13]. APOBEC3 cytidine deaminases can edit single-stranded viral genomes such as EBV by inducing cytidine-to-uracil mutations in viral DNA [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to activation of RNA-editing enzymes, EBV exploits host miRNAs to escape the immune system. EBNA2 is a viral protein that is expressed during type III latency and upregulates miR-21, which subsequently downregulates myeloid differentiation factor 88 and IL-1 receptor-associated kinase 1 (15). The miR-17-92 cluster, which is essential for immune cell differentiation, is highly expressed in EBV-positive tumors, such as NPC (16) and DLBCL (17). High expression of miR-17-92 in B cells, T cells, NK cells, macrophages, and dendritic cells is known to inhibit cellular differentiation and function (18).In EBV-infected B lymphocytes, viral LMP1 activates NF-\u0026kappa;B signaling and host miR-155. However, miR-155 attenuates NF-\u0026kappa;B signaling to stabilize persistent infection (19).\u003c/p\u003e\n\u003cp\u003eHere, we extend our analysis of a CRC family where evidence of nonrandom mutagenesis was observed initially (11) to unravel the molecular basis of these patterns, including the possible role of editing enzymes and other epigenetic factors. The dual role of AOPBEC enzymes with viruses makes the consideration of this family in carcinogenesis all more interesting. Viruses are a major etiological agent for several tumors in Sudan, where the family under investigation comes from\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eSample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole exome sequencing (WES) was carried out for two cases and three controls from the same family as described previously (11). Additionally, six biopsies from nonfamilial CRC cases were collected to confirm the in-silico work at the laboratory. A histopathology test to confirm CRC was conducted on all biopsies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;A-EBV detection in colorectal cancer tissues:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe combined the three available methods for EBV detection in colorectal cancer as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1-3-Next Generation Sequencing:\u0026nbsp;\u003c/strong\u003eThe main idea is to detect the EBV sequences in the familial colorectal Exome samples. Firstly, alignment of the fastq files with hg19 reference genome using bowtie2. After obtaining the BAM files, we extracted the unmapped read to human genome and aligned them with EBV reference genome using blat.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-In situ hybridization (ISH):\u0026nbsp;\u003c/strong\u003eISH was carried outfor the available biopsy materials of CRC patients from the same family with an EBV peptide nucleic acid (PNA) probe/fluorescein (Dako, Denmark) and detected by a PNA ISH detection kit. Negative and positive controls were supplied by the manufacturer. A nasopharyngeal carcinoma type III biopsy served as an additional positive control. A case was positive if the nucleus of the tumor cell stained dark blue or black (20,21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3-DNA amplification using PCR\u003c/strong\u003e: Six biopsies from nonfamilial CRC cases were collected to confirm the presence of EBV DNA. Specific regions of the viral genome were amplified by using two primers (Epstein Barr Encoded RNA (EBER) gene and Latent Membrane Protein-1 (LMP-1) gene). Primer designs were described previously (22,23). DNA from EBV-positive nasopharyngeal carcinoma (NPC) was used as a positive control, and nuclease-free water to replace DNA was used as a negative control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB-APOBEC family analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1-Evaluation of colorectal cancer SNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn WES, SNPs were queried whether they were in known CRC genes or cancer hit genes or had a high impact, and functional analysis was performed according to ACMG guidelines (24). The proportion of (G/A- C/T) transitions was calculated in all SNPs. Networks of all SNPs were analysed using Cytoscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-APOBEC\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egene\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SNP prioritization:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the WES samples, SNPs in APOBEC family genes were analysed according to ACMG guidelines by\u0026nbsp;VEP (25), SNP-nexus (26) and Varsome (27).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3-APOBEC3b expression Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the strong association of APOBEC3B with hypermutated colorectal cancer, APOBEC3B expression analysis was carried out among the six biopsies from sporadic CRC using qRT‒PCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-miRNA analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Human miRNA analysis was carried out using different bioinformatic tools (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1-Prediction of 3` UTR miRNA dysfunction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that 80% of the miRNAs are in the 3` UTR variants, all 3`UTR regions of the WES were analysed. Moderate- and high-impact SNPs in the 3\u0026rsquo;UTR and CRC-related genes were selected according to ACMG guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-Identification of SNP-related miRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePolymiRTS (Polymorphism in microRNA Target Site database) was used to predict and prioritize miRNAs related to genes with significant SNPs. (21)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3-Functional prediction and network analysis of related miRNAs:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the selected miRNAs, biological pathways were analysed with the following tools: miRNet for network analysis and the relation between disease and miRNA (28). DIANA miRPath (29) was used to investigate the combinatorial effect of microRNAs, mutated SNPs, and biological pathways. Moreover, the Co-expression Meta-analysis of miRNA Targets (CoMeTa) tool (30) was used to rank miRNA targets according to their degree of co-expression, identify miRNA regulatory networks, and predict biological function. Finally, the tool for annotations of miRNAs (TAM) was used to investigate families, clusters, functions, associated diseases, and tissues. (31)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD-Expression analysis of miRNA:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003emiRNA130 and miRNA-29 were selected due to their strong association with CRC and EBV upon the insilico analysis. miRNA was verified using qRT‒PCR from six CRC patients and their matched normal tissues and blood. RNA was isolated using the EasyBlue isolation regent (iNTRon., Korea) cat no. 17061.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEBV detection:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTheEBV-2 sequence was detected in the available CRC WES which was supported by ISH. The confirmation of EBV DNA presence in the six unrelated cases by positive PCR (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPOBEC family analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRC gene SNP analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe focus here on mutation analysis intending to understand tumorigenesis from the angle of somatic changes, i.e., the incidental changes that set-in action cancer functional phenotypes. We started with an initial input of 43186-44886 total SNPs in CRC cases and 43832-44967 in controls. First, we identified the SNPs that are in cancer-hit genes. Then, we prioritized them according to the ACMG guidelines and chose pathogenic and likely pathogenic ones. (\u003cstrong\u003eSupplementary table\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eUpon analysis of filtered SNPs, the total read of variants in tumor sample exomes was twofold higher than that in controls (Fisher exact test P = 0.001) (\u003cstrong\u003eFigure 3a\u003c/strong\u003e). Editing signatures based on analysis of the transition/transversion ratio for novel SNPs show an excess of C/T, T/C, G/A, and A/G transitions (89%) in the exonic parts in tumor tissues (P=0.0001), while the 3\u0026apos; UTR shows a more uniform distribution of classes (\u003cstrong\u003eFigure 3b\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eCancer patients\u0026apos; networks could be distinguished from the control\u0026apos;s network by their network topology of the novel damaged SNVs in visualization (\u003cstrong\u003eFigure 4\u003c/strong\u003e). Interestingly, this figure highlights the role of these somatic changes in the formation of cancer circuitries when compared to noncancerous tissue. It also shows the complexity of the cancer network that should be considered to understand the disease pathogenesis. Not only are the proteins of centrality in the control disproportionately few, but the top scores in centrality analysis in CRC patients were cancer-associated proteins, while the top proteins in control samples were not, perhaps accounting for the tumor\u0026apos;s capacity to execute multiple functions and phenotypes, including phenotypes known as hallmarks of cancer (\u003cstrong\u003eFigure 4\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eAlthough both tumors displayed adequately complex networks, there were shared and unique circuitries indicating the unique features of each tumor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPOBEC gene variant analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total number of SNPs that occurred in APOBEC family genes among cases and controls was 124 SNPs, 52 of which were nonsynonymous SNPs, while the other 72 were synonymous. From the total number of SNPs, 70 were exonic, 37 were intronic and 17 were in the 3\u0026rsquo;UTR (Supplementary Table 1). Only one exonic nonsynonymous SNP (rs17000556) was found in A3A in the control sample \u0026ldquo;26\u0026rdquo;. (\u003cstrong\u003eSupplementary table 2\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe total number of SNPs in A3G in all samples was 10; two of them were intronic, while the other 8 were exonic. Of those 10 SNPs, 3 were nonsynonymous. These nonsynonymous SNPs were found in the control samples. Two of the 3 A3G SNPs (rs8177832) and (rs17496046) were nonsynonymous in the control samples, while the rs5757465 SNP was synonymous but shared between cases and controls. All of the SNPs were found to be benign. (\u003cstrong\u003eSupplementary table )\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPOBEC3b expression analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe APOBEC3B gene expression was approximately sixfold higher in cancer tissues than in noncancerous tissues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emiRNA analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2700 genes with 3`UTR dysfunction were found in the WES. Only 7 genes had moderate and high impact SNPs at the 3\u0026apos;UTR. (\u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e). Eighteen of the colorectal cancer actionable genes had 3\u0026apos;UTR dysfunction (\u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe Polymerit database showed 107 miRNAs related to colorectal actionable genes and 37 miRNAs associated with the selected genes with high and moderate impact 3`UTR dysfunction. (\u003cstrong\u003eSupplementary table 3 and table 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe Mi-Net tool showed that hsa-miR-214-p3, hsa-miR -484, hsa-miR-766-3p, and hsa-miR -130-2p are associated with colorectal cancer. In addition, hsa-miR-17-3p and hsa-miR-26 are associated with EBV (\u003cstrong\u003eFigure 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003ePrediction based on the miRNA regulatory effect on damaged SNPs: The results showed that 30% of these genes were controlled by hsa-miR-29a-3p.\u003c/p\u003e\n\u003cp\u003ePathway analysis using the DIANA tool: Different binding sites were controlled by hsa-miR-29a-3p and hsa-miR-130-2p. Our list of SNPs and hsa-miR-29a-3p was significantly associated with CRC (p= 0.0018)\u003c/p\u003e\n\u003cp\u003eThe most significant biological functions for miRNA130a using the COMETA tool were cell migration (p= 0.0001), negative regulation of transcription from the RNA polymerase II promoter (p= 0.0003), negative regulation of cell growth (p= 0.0004), and covalent chromatin modification (p= 0.0005). Interestingly, a significant correlation between miRNA29 and the apoptosis pathway was predicted (p = 0.002, FDR: 0.032).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression analysis of miRNAs:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression levels of hsa-miR-130-2p and hsa-miR-29a-3p in tissue samples were 6.5 and 3.71 higher, respectively, than those in normal controls. Expression profiles in the matched blood samples showed a lower level of expression in hsa-miR-130-2p (0.72) but not in hsa-miR-29a-3p (2.14) (\u003cstrong\u003eFigure 6\u003c/strong\u003e). Some similarity in the expression pattern was observed between hsa-miR-29a-3p and hsa-miR-130-2p.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe association of EBV with cancer among Sudanese patients has been established in several cancers, especially epithelial-origin cancers, including esophageal cancer (26), gastric carcinoma (32), breast cancer (22), and nasopharyngeal carcinoma (23,33). Here, we investigated the possible epigenetic mechanisms by which EBV can induce carcinogenesis in CRC. We were able to confirm the presence of EBV in our samples by different methods of detection to overcome every method limitations (34). Although the association between EBV and CRC is still debatable (34,35), some research has hypothesized that EBV can induce CRC (36-39). In situ hybridization has the advantage of discerning the virus location, whether in the B lymphocytes\u0026mdash;its native location\u0026mdash;versus the epithelial tissue where carcinogenesis takes place. Interestingly, especially within the intestine, it was reported that EBV-derived molecules can transmit from B lymphocytes to epithelial cells via microvesicles [40]. These microvesicles can contain different EBV-derived molecules, such as LMP1, one of the major EBV-related oncogenes, or noncoding RNAs (ncRNAs; EBERs) (41,42).\u003c/p\u003e\n\u003cp\u003eFor EBV to exert its oncogenic effects, this occurs through manipulating the host epigenetic mechanisms by the virus, which includes the methylation machinery (4) and editing enzymes, namely, the cytidine aminase/AID family of proteins that evolved originally as a well-conserved system of innate immunity that mutates and inactivates viral genomes and endogenous retroelements (43,13).\u003c/p\u003e\n\u003cp\u003eIn the current report, we investigate the possibility of editing in cancer tissues through members of the apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC) family of enzymes by comparing the percentage of A\u0026ndash;G and C\u0026ndash;T transitions,\u0026nbsp;which turned out to be significant (11). This is added recent genomic observation that APOBEC signatures were enriched in some CRC primary tumor and corresponding metastasis samples [9] and\u0026nbsp;is supportive of the association between EBV and APOBEC3, since APOBEC3 enzymes,\u0026nbsp;among\u0026nbsp;other activities, have specific functions in the defense against viral infections [9,44,45]. Recently, APOBEC3 proteins linked viral infections to cancer development. For example, in breast cancer, APOBEC3B mRNA was\u0026nbsp;overexpressed\u0026nbsp;in normal breast epithelial cells transfected with HPV,\u0026nbsp;which\u0026nbsp;caused a significant increase in \u0026gamma;-H2AX foci formation and DNA breaks, which were blocked by the knockdown of both HPV and APOBEC3B (46).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;AID is an activation deamination enzyme that leads to antibody diversity (47). APOBEC3 comprises eleven members in \u003cem\u003eHomo sapiens\u003c/em\u003e enzymes\u0026rdquo; and has a role in cancer development. APOBEC3-mediated deamination in the TCW motif (W = A or T) was found in multiple tumors, such as colorectal cancer (46,48-53). APOBEC3G in humans, such as mice, is associated with CRC with liver metastasis (54). Marouf and his colleagues showed that 4 SNPs in the APOBEC3B gene and 1 SNP in the APOBEC3A gene were associated with increased breast cancer risk and/or clinical outcome (55).\u003c/p\u003e\n\u003cp\u003eCancer was viewed for quite some time as a random mutagenesis process. This view is progressively recessing, with mounting evidence of a highly regulated process of organizational nature where several effector molecules are prominently involved in APOBEC-mediated DNA deamination as a potential cause of cancer-associated somatic mutations (56,57), as well as other endogenous regulators such as miRNA that are largely responsible for the specificity of the various tumors and their mutagenic capabilities, which are more likely to spark a tumorigenesis process (11).\u003c/p\u003e\n\u003cp\u003eThe ability to initiate hallmark aspects from cell division to metastasis is intriguing. To view carcinogenesis as a complex procedure that engenders a group of phenotypic cellular changes that bring into the limelight molecules such as the APOBEC family of proteins, as the complexity of the carcinogenesis process insinuates a role of molecules that can control and regulate such disparate functions, this was attested by the network topology of the three cases versus the controls where the connections were concatenated in a more complex fashion implying a potential highjack by the tumors of cellular mechanism through an EBV/APOBEC-based mechanism. Although both tumors displayed adequately complex networks, there were common and unique circuitries indicating the unique features of each tumor. Not only are the proteins of centrality in the control disproportionately few, but the top scores in centrality analysis in CRC patients were always cancer associated proteins, while the top proteins in control samples were not, perhaps accounting for the tumor capacity to execute multiple functions and phenotypes, including phenotypes known as hallmarks of cancer. Of those genes of centrality that appeared in our network associated with EBV, CRC, and miRNA, as shown in Figure 5, are PIK3R2 and PIK3CG. The former gene is involved in tumor metastasis and increased expression in melanoma, breast, and colon cancer, as well as in lung squamous cell carcinoma (LUSC) (58-60), which is also involved in EBV-infected cells. PIK3CG overexpression also promotes CRC tumorigenesis and progression (61).\u003c/p\u003e\n\u003cp\u003eHow do these genomic changes resulting in cancer hallmarks and unique features of epigenetic organization take place in the body of cancerous patients? Human cells are under continuous genetic and epigenetic changes along their life span resulting from endogenous and external environmental onslaughts. Cancer is driven by the accumulation of genetic mutations (somatic + germline mutations). Somatic mutations are generated from mutational processes of exogenous and endogenous exposures, DNA enzymatic modifications, and failure of DNA repair [62-64]. ssDNA-specific AID/APOBEC cytidine deaminases are among the most prominent endogenous enzymatic mutagens (9). Previous studies have identified and confirmed more than 50 distinct signatures of single-base substitution (SBS) derived from the analysis of whole-genome and whole-exome sequences (WES) of multiple cancer types [44,62, 65\u0026ndash;69]. Mutational processes result in different mutation types with characteristic combinations of mutation types constituting different mutational signatures [62,65]. Transition of C\u0026gt;T is the most common mutation in human cells and is more common in many types of cancer (70).\u003c/p\u003e\n\u003cp\u003eMutational signatures reflect the activity of mutational processes that have been active throughout a person\u0026rsquo;s life [63]. The identified SBS signatures reflect processes commonly found across cancer types and processes confined to a particular cancer type. For example, signature SBS2 and SBS13, both attributed to the enzymatic activity of the APOBEC family of cytidine deaminases, are present in multiple cancer types [44]. Clustering of these mutations was recently highlighted within breast, head/neck, and other cancers (71). These clusters have been named kataegis (57), which are linked to one or more of the nine active DNA cytosine deaminases encoded by the human genome (9,11,57,70-72).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Based on analysis of WES data in the 3\u0026rsquo; UTR regions using bioinformatics approaches, we first excluded the premise that genetic changes in the 3\u0026rsquo; UTR are driven by incidental mutation through the cytosine deaminase enzyme because of the lack of mutational signatures in the miRNA domains, which we have argued in favor of for exonic mutations (11). The synchronous expression profiles and upregulation of mi-RNA130 and mi-RNA29 are in accordance with previous studies (73-77).\u003c/p\u003e\n\u003cp\u003emiR-29a-3p and miR-130a-5p are used as circulatory markers for some cancers (74,78). They are known to control many important cellular and biological functions, many of which are cancer-associated pathways, i.e., enhancement of cell proliferation and migration. Inhibitors of these miRNAs were found to affect cell survival in CRC cell lines (79).\u003c/p\u003e\n\u003cp\u003eInterestingly, miRNA 29 and encoded miR-29 seed (miR-BART3) have been shown to share several mRNA targets with miR-29 (80). EBV is an established transforming virus in human B cells, so the importance of viral mimics of cellular miRNAs in viral tumorigenesis (81) and of viral analogs to miR-29 merits additional investigation. LMP-1 induces miR-29b, which results in miR-29b-mediated downregulation of T-cell leukemia gene 1 (TCL1), a protein with roles in cell survival and proliferation (82).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe present through computational approaches and molecular testing, evidence of EBV etiology in a CRC multi-case extended family. The oncogenicity of the virus is associated with epigenetic regulation mainly through APOBEC enzymatic activity, resulting in unique mutational patterns and features of tumor organization, as evident in the molecular networks of both dysfunctional genes and miRNAs. In addition, this report helps further our understanding of the complexity of cancer pathogenesis and presents cancer as a genuine multigenic process.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCRC: Colorectal cancer\u003c/p\u003e\n\u003cp\u003eEBV: Epstein Bar Virus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAID/APOBEC: Apolipoprotein B Editing Catalytic subunits proteins.\u003c/p\u003e\n\u003cp\u003eNPC: Nasopharyngeal carcinoma\u003c/p\u003e\n\u003cp\u003eSBS:single-base substitution\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research project approved by the Ethical Committee of the Institute of Endemic Diseases, University of Khartoum.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Written informed consent from all participants have been obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was partly funded by the University of Khartoum and the Ministry of Higher Education and Scientific Research, Khartoum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR H, re-analyzed the data, wrote the manuscript.\u003c/p\u003e\n\u003cp\u003eOY, performed the laboratory experiments and part of the in-silico work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR I analyzed the APOBEC family.\u003c/p\u003e\n\u003cp\u003eR Y, Performed part of the bioinformatics and in -silico analysis.\u003c/p\u003e\n\u003cp\u003eB H, performed mi-RNA analysis.\u003c/p\u003e\n\u003cp\u003eLA, analysed the EBV sequence data.\u003c/p\u003e\n\u003cp\u003eT I, performed the PCR for APOBEC\u003c/p\u003e\n\u003cp\u003eA S, Supported the experiments and revised the manuscript.\u003c/p\u003e\n\u003cp\u003eSS,provided the patients.\u003c/p\u003e\n\u003cp\u003eM I, conceived of the study, supervised the work and revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMuch gratitude also goes to the colorectal cancer families and patients for their kind collaboration in the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEpstein MA, Achong BG, Barr YM.\u0026nbsp;Virus particles in cultured lymphoblasts from Burkitt's lymphoma.\u0026nbsp;1964;\u0026nbsp;1:702\u0026ndash;3. 10.1016/S0140-6736(64)91524-7\u003c/li\u003e\n\u003cli\u003eKieff, E. and Rickinson, A.B. 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It has been shown recently to act through various epigenetic mechanisms, including DNA hypermethylation, EBV-related miRNA, and RNA editing enzymes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWhole exome sequencing of an extended multi-case colorectal cancer family revealed a potential oncogenic viral etiology. Investigation of such putative involvement in the same family was carried out by interrogating exome sequences of the tumor tissue for the presence of EBV signatures, in addition to immune histochemistry analysis and PCR of the LMP and EBER genes. Due to previously encountered strong signals for the involvement of APOBEC3b as an RNA editing enzyme, quantitative PCR was performed to quantify APOBEC3b. Various bioinformatics tools have also been used to detect virus-related miRNAs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe EBV 2 sequence was retrieved from patient tumor sequences and detected by PCR and immunohistochemistry in the tumor samples. APOBEC3b was found to be sixfold higher in the sample than in controls, which explains the high C/T transition occurrence among tumor sequences. Bioinformatic analysis revealed that has-miR-29 b and has-miR-130 were among the top related miRNAs to EBV and colorectal cancer which was further supported by expression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThese results, in addition to expanding the list of EBV-related cancers, highlight potential epigenetic mechanisms that might help explain the oncogenic functionality of the virus and the ontology of tumor complexity.\u003c/p\u003e","manuscriptTitle":"EBV-associated epigenetic signature in a colorectal cancer multicase family.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-29 16:07:01","doi":"10.21203/rs.3.rs-3643167/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55fbc801-35da-451c-8bc8-2f3072b83ec8","owner":[],"postedDate":"November 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-05T09:29:45+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-29 16:07:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3643167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3643167","identity":"rs-3643167","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}