Epigenetic modification and risk of Hepatocellular carcinoma in high incidence region of Northeast India

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Despite its prevalence, the role of epigenetic changes, especially DNA methylation, in HCC development in this region remains underexplored. Aberrant DNA methylation patterns offer significant potential as biomarkers for diagnosing and monitoring individuals at high risk. This study focuses on understanding the contribution of DNA methylation to HCC pathogenesis in two states of Northeast India, aiming to provide insights into disease mechanisms and improve early detection strategies in this high-risk population. A population-based case-control study was conducted in Arunachal and Sikkim, two states of North-eastern India, involving a total of 164 participants of HCC (73 histologically-confirmed cases, 91 age and sex-matched controls), with blood samples collected and analyzed for methylation patterns in four key tumor suppressor and DNA repair genes: p16, APC, GSTP1, and MGMT. The findings reveal that methylation was observed in a subset of liver cancer patients, with the highest prevalence in the p16 gene (5 cases) followed by APC, MGMT, and GSTP1. Epidemiological analysis highlighted significant associations between HCC risk and factors such as male predominance, lower literacy levels, alcohol use, and chronic hepatitis B (HBV) or hepatitis C (HCV) infections. This study emphasizes the critical role of epigenetic modifications in HCC pathogenesis and suggests that DNA methylation in tumor suppressor and DNA repair genes could serve as potential biomarkers for early detection and risk assessment of HCC in Northeast India. Biological sciences/Cancer Biological sciences/Molecular biology Health sciences/Diseases Health sciences/Gastroenterology Health sciences/Medical research Health sciences/Oncology Carcinoma DNA methylation Epigenetic modifications Tumor suppressor genes HBV/HCV infection Biomarkers for liver cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Liver cancer is one of the deadliest cancers worldwide, posing a significant challenge due to its high incidence and mortality rates. In 2020, the World Health Organization (WHO) and the International Agency for Research on Cancer reported that liver cancer accounted for 4.7% of all cancer cases globally, ranking as the 6th most commonly diagnosed cancer. Even more alarming, it was the third leading cause of cancer-related deaths, highlighting the need for improved prevention, diagnosis, and treatment options [ 1 ]. Hepatocellular carcinoma (HCC) is the most common type of liver cancer, responsible for 75–85% of cases [ 2 ]. It often develops as a result of chronic liver damage caused by a combination of environmental and genetic factors. Chronic infections with Hepatitis B virus (HBV) or Hepatitis C virus (HCV) remain the primary risk factors, accounting for around 80% of HCC cases worldwide [ 3 ]. Other contributors include aflatoxin exposure, excessive alcohol use, obesity, non-alcoholic steatohepatitis (NASH), and genetic conditions like hereditary hemochromatosis. The prevalence of risk factors varies across regions. In East Asia and sub-Saharan Africa, chronic HBV infections are the dominant cause of HCC due to high rates of vertical transmission. In contrast, HCV is more prevalent in Western countries and Japan, where it remains a leading driver of liver cancer cases [ 4 ]. Emerging trends also show a growing burden of liver cancer associated with metabolic disorders like obesity and diabetes, especially in developed countries, driven by the rise of Non-alcoholic Fatty Liver Disease (NAFLD) [ 5 ]. Treatment options for advanced HCC remain limited, and late-stage diagnosis often leaves patients with poor prognoses. Sorafenib, a tyrosine kinase inhibitor, has been the standard treatment for advanced cases, providing a modest survival benefit of 3–5 months [ 6 ]. Recent advancements, however, are offering hope. Immunotherapy, particularly the combination of atezolizumab and bevacizumab, has shown superior efficacy compared to sorafenib, significantly improving progression-free survival and overall survival in patients with unresectable HCC [ 7 ]. Despite these advancements, the 5-year survival rate for liver cancer remains dismally low, under 20% globally [ 1 ]. This highlights the pressing need for early detection strategies, particularly in high-risk populations, and continued research into innovative therapies and preventative measures. Several epigenetic studies, which examine heritable changes in gene activity without affecting the DNA sequence, may provide a significant contribution to our understanding of the mechanisms underlying the development and progression of HCC and, consequently, may identify new biomarkers for its diagnosis. DNA methylation, histone modifications, non- coding RNAs, and other factors are involved in epigenetics [ 8 ]. DNA methylation is a critical epigenetic alteration vital for controlling transcription and regulating gene expression. It is well-established that epigenetic regulation in tumor cells is important for the development of HCC [ 9 , 10 ]. Methylation of cytosine nucleotides (5-MeC) is the most common epigenetic change in mammalian DNA [ 11 ]. Although 70–80% of CpG sites in human DNA are methylated, which is more frequently observed at repetitive DNA sites or in regions of low CpG density, contrarily in 1–2% of the genome in healthy individuals, represents a completely unmethylated CpG island [ 12 ]. HBV and HCV infections can lead to the inactivation or suppression of the p16 gene, accelerating the progression of liver cancer. HBV X protein and HCV core protein have been shown to disrupt p16 function, promoting cell cycle progression and cellular proliferation. As an important epigenetic regulator, the viral protein HBx activates the DNA methyltransferase family and promotes the hypermethylation of a particular tumor suppressor gene (GSTP1, CDKN2B, and RASSF1A). Transcriptional silencing and loss of protein expression are often associated with CpG island methylation. p16INK4a, p15INK4b, p14ARF, GSTP1, APC, MGMT, hMLH1, SOCS-1, E-cadherin, and RASSF1A are a few genes that are silenced by this mechanism [ 13 ]. One of the most frequently altered tumor suppressor genes is p16. Most aberrant promoter methylation in HCC is responsible for the loss of p16, which is a cyclin-dependent kinase inhibitor that disrupts the cell cycle and interrupts cell proliferation. In non-viral liver cancer, inactivation of p16 due to mutations, deletions, or promoter hypermethylation can similarly lead to unchecked cell proliferation. The absence of viral factors means the carcinogenic process is more directly linked to genetic and epigenetic alterations of p16. Influence of HBV and HCV infections are also observed Adenomatous Polyposis Coli (APC). The Wnt/β-catenin signaling pathway, which is regulated in part by APC, can be aberrantly activated in HBV/HCV-associated liver cancers. Viral proteins may indirectly influence APC function, contributing to the pathogenesis of liver cancer through dysregulation of cell growth and apoptosis. Mutations or loss of APC in liver cancer not related to HBV or HCV can similarly lead to activation of Wnt/β-catenin signaling, promoting oncogenesis. The specific impact of APC mutations may, however, be more pronounced in the context of genetic predispositions and environmental factors other than viral infections. The influence of HBV/HCV infections on GSTP1 (Glutathione S-Transferase Pi 1) can be understood in this manner: Oxidative stress is a hallmark of chronic hepatitis infection, which can be exacerbated by impaired GSTP1 function. HBV and HCV increase oxidative stress and inflammation, and the reduced detoxification capability due to GSTP1 silencing can lead to higher DNA damage rates and cancer progression. In liver cancers not associated with viral infections, GSTP1's role in detoxifying carcinogens remains critical. Environmental toxins and metabolic byproducts can cause DNA damage if not properly detoxified, highlighting the importance of GSTP1 in protecting against carcinogenesis. The HBV/HCV infections have significant impact on MGMT (O-6-Methylguanine-DNA Methyltransferase). The viral promotion of a pro-oxidant state can increase the formation of alkylated DNA adducts. MGMT's role in repairing these lesions is crucial for preventing mutagenesis. In HBV or HCV infections, the increased oxidative DNA damage coupled with impaired MGMT function can synergistically elevate cancer risk. The importance of MGMT in repairing DNA damage remains critical in the absence of viral infections. Environmental and endogenous alkylating agents pose a risk for initiating liver cancer, and the efficiency of MGMT-mediated repair mechanisms is a key factor in preventing malignancy. According to the Population-based Cancer Registry (PBCR) Report, 2021 [ 14 ], the northeastern states including Arunachal Pradesh and Sikkim have reported the highest incidence of cancer [ 15 ]. Although the prevalence of HCC is becoming more well-recognized in the states of Arunachal Pradesh and Sikkim, little is known about epigenetic alterations and the risk of liver cancer in this area. These aberrant methylation patterns can be a great way to diagnose and monitor people who are at high risk for developing HCC. However, the significance of epigenetic alteration is still not fully understood. Thus, the objective of this study is to analyze the methylation profile of the tumor suppressor genes (APC and p16) and DNA repair genes (MGMT and GSTP1) and to establish the relationship between methylation status and HBV/HCV infection risk associated with liver cancer patients. Results Altogether 222 blood samples (germline DNA from WBCs) were collected for the study, including 130 samples (55 cases and 75 controls) from Arunachal Pradesh and 92 samples (46 cases and 46 controls) from Sikkim. About 62.4% of total participants were male and 37.6% were female (Fig. 1 ). The average age of liver cancer cases (61.6 years for hepatitis-negative and 50.6 years for hepatitis-positive cases) was significantly higher compared to controls (31.8 years for hepatitis-positive and 51.4 years for hepatitis-negative controls) (Fig. 2 ). Demographic analysis revealed that a major section of liver cancer patients were illiterate (63.6% for hepatitis-negative and 75.0% for hepatitis-positive cases), married (83.1% for hepatitis-negative and 83.3% for hepatitis-positive cases), and self-employed/employed (59.7% for hepatitis-negative and 75.0% for hepatitis-positive cases). Regarding HBV and HCV infection status, 22.77% (n = 23) of cases and 22.27% (n = 33) of controls were HBsAg positive, whereas 3.96% (n = 4) of cases and 8.26% (n = 10) of controls were HCV reactive. Additionally, 2.97% (n = 3) of cases and 3.3% (n = 4) of control samples tested positive for both HBsAg and HCV (Fig. 3 ). When comparing liver cancer cases with and without HBV/HCV infection, patients without HBV/HCV infection had an average age of 61.6 years, whereas those with HBV/HCV infection were younger, with an average age of 50.6 years (p-value = 0.014). Liver cancer patients were predominantly male, married, illiterate, and self- employed/employed, as shown in Table 2 . Table 1 Primer Sequences, Annealing Temperatures, and Product Sizes for Methylation-Specific PCR of p16, APC, MGMT, and GSTP1 Genes Gene Primer Type Forward Primer (5ʹ-3ʹ) Reverse Primer (5ʹ-3ʹ) Annealing Temp (°C) Product Size (bp) p16 M TTATTAGAGGGTGGGGCGGATCGC GACCCCGAACCGCGACCGTAA 61°C 150 bp U TTATTAGAGGGTGGGGTGGATTGT CAACCCCAAACCACAACCATAA 60°C 151 bp APC M GAACCAAAACGCTCCCCAT TTATATGTCGGTTACGTGCGTTTATAT 59°C 74 bp U AAACCAAAACACTCCCCATTC AGTTATATGTTGGTTATGTGTGTTTAT 59°C 76 bp MGMT M TTTCGACGTTCGTAGGTTTTCGC GCACTCTTCCGAAAACGAAACG 59°C 81 bp U TTTGTGTTTTGATGTTTGTAGGTTTTTGT AACTCCACACTCTTCCAAAAACAAAACA 59°C 93 bp GSTP1 M TTCGGGGTGTAGCGGTCGTC GCCCCAATACTAAATCACGACG 60°C 89 bp U GATGTTTGGGGTGTAGTGGTTGTT CCACCCCAATACTAAATCACAACA 60°C 95 bp Table 2 Demographic characteristics of liver cancer patients and controls corresponding to the groups Demographic Characteristics GROUP A GROUP B GROUP C GROUP D Cases (n = 77) (Hepatitis Negative) Cases (n = 24) (Hepatitis Positive) Control (n = 39) (HBV/HCV Positive) Control (n = 82) (HBV/HCV Negative) Average age (in years) 61.6 (± 13.1) 50.6 (± 12.3) 31.8 (± 13.3) 51.4 (± 19.3) Sex Male Female 46 (59.7) 31 (40.2) 17 (70.8) 07 (29.2) 22 (56.4) 17 (43.6) 51 (62.3) 31 (37.8) Educational Status* Illiterate Literate 49 (63.6) 28 (36.3) 18 (75.0) 06 (25.0) 09 (23.1) 30(76.9) 18(21.9) 64 (78.0) Marital Status* Unmarried Married Ever Married 05 (6.5) 64 (83.1) 08 (10.4) 0 20 (83.3) 04 (16.7) 17 (43.6) 20 (51.3) 02 (5.1) 17 (20.7) 62 (75.6) 03 (3.6) Occupational Status Unemployed/Student Employed/Self Employed House wives 14 (18.2) 46 (59.7) 17 (22.1) 04 (25.0) 20 (75.0) 0 24 (61.5) 05 (12.8) 10 (25.6) 18 (21.9) 47 (57.3) 17 (20.7) *p value < 0.05 in the chi square test *Here, Hepatitis refers to HBV and HCV infection Cancer staging analysis revealed that among the 77 liver cancer patients without HBV/HCV infection, the distribution was: Stage I (n = 15, 19.48%), Stage II (n = 15, 19.48%), Stage IIIA (n = 19, 24.67%), Stage IIIB (n = 11, 14.28%), Stage IIIC (n = 1, 1.29%), Stage IVA (n = 2, 2.59%), and Stage IVB (n = 14, 18.18%). In contrast, among the 24 liver cancer patients with infections associated with HBV/HCV, majority were in Stage IIIA (n = 10, 41.7%) and Stage IIIB (n = 9, 37.5%), while fewer were in Stage I (n = 3, 12.5%) and Stage II (n = 2, 8.3%) (Table 3 ). These findings indicate that liver cancer patients without HBV/HCV infection were more possibly to present with advanced cancer stages in contrast to those with HBV/HCV infection. Table 3 Staging of liver cancer patients Cancer staging Group A (n = 77) (Liver cancer patients without Hepatitis infection) Group B (n = 24) (Liver cancer patients with Hepatitis infection) Stage I 15 (19.48%) 3 (12.5) Stage II 15 (19.48%) 2 (8.3) Stage IIIA 19 (24.67%) 10 (41.7) Stage IIIB 11 (14.28) 9 (37.5) Stage IIIC 1 (1.29%) 0 Stage IVA 2 (2.59%) 0 Stage IVB 14 (18.18%) 0 * Percentage in parentheses *Here, Hepatitis refers to HBV and HCV inhection • DNA Hypermethylation of p16, APC, GSTP1, and MGMT genes in HCC Cases and Controls DNA hypermethylation patterns of p16, APC, GSTP1, and MGMT genes were analyzed in 104 HCC cases and 104 matched controls. Hence, there are four groups: Group A: Hepatitis Negative Cases, Group B: Hepatitis Positive Cases and Group C: Hepatitis Negative Controls, Group D: Hepatitis Positive Controls. Here, Hepatitis refers to HBV/HCV infection. Among Hepatitis-positive HCC cases, hypermethylation of p16, APC, and GSTP1 was observed in 2 cases each (4.0%), while MGMT hypermethylation was also detected in 2 cases (4.0%). In Hepatitis-negative HCC cases, p16 and APC were hypermethylated in 2 cases each (8.3%), GSTP1 in 2 cases (8.3%), and MGMT in 1 case (4.1%). In contrast, no hypermethylation of p16, APC, GSTP1, or MGMT was observed in Hepatitis-positive or Hepatitis-negative control subjects. Overall, hypermethylation of p16, APC, GSTP1, and MGMT was significantly more frequent in HCC cases compared to controls (Table 4 , Figs. 3 – 6 ), irrespective of Hepatitis status, highlighting the potential role of these epigenetic alterations in hepatocarcinogenesis. Table 4 Methylation profile of tumor suppressor gene and DNA repair gene Gene Total no. of Liver cancer patient tested Total no. of controls tested Methylation found in liver cancer cases Methylation found in controls Hepatitis Positive Cases Hepatitis Negative Cases Hepatitis Positive Controls Hepatitis Negative Controls p16 104 104 2 (4.0% ) 2 (8.3%) 0 0 APC 104 104 2 (4.0%) 2 (8.3%) 0 0 GSTP1 104 104 2 (4.0%) 2 (8.3%) 0 0 MGMT 104 104 2 (4.0%) 1 (4.1%) 0 0 Discussion The results of this study demonstrate that hypermethylation of tumor suppressor genes p16, APC, GSTP1, and MGMT is significantly associated with hepatocellular carcinoma (HCC) cases compared to controls, irrespective of HBV infection status. Notably, no hypermethylation of these genes was observed in control subjects, suggesting that these epigenetic alterations are closely linked to the pathogenesis of HCC. Among HBV-positive HCC cases, the observed hypermethylation frequencies of p16, APC, GSTP1, and MGMT were relatively low (4.0% for each gene). Similarly, in HBV- negative HCC cases, hypermethylation frequencies for p16, APC, and GSTP1 were slightly higher at 8.3%, while MGMT hypermethylation was observed in 4.1% of cases. These findings suggest that the hypermethylation of these genes may occur independently of HBV infection, although the contribution of HBV-mediated mechanisms to epigenetic dysregulation cannot be excluded. HBV proteins, such as HBx, are known to activate DNA methyltransferases, potentially contributing to the silencing of tumor suppressor genes, but this process may not uniformly affect all genes analyzed [ 16 ]. The absence of hypermethylation in the control group, regardless of HBV status, indicates the role of these epigenetic changes in HCC development. Tumor suppressor gene silencing through promoter hypermethylation has been well-documented as a hallmark of carcinogenesis, leading to the loss of critical regulatory functions in cell cycle control, DNA repair, and detoxification processes. Specifically, p16 hypermethylation disrupts cell cycle regulation, while APC inactivation directs to abnormal activation of Wnt/β-catenin signaling pathway. Similarly, GSTP1 silencing compromises cellular detoxification, increasing oxidative stress, and MGMT hypermethylation impairs DNA repair, leading to genomic instability [ 17 – 20 ]. The slightly higher hypermethylation rates in HBV-negative cases for genes such as p16 and APC suggest that non-viral factors, including environmental exposures or genetic predispositions, may also play a role in driving these epigenetic alterations. Risk factors such as alcohol consumption, aflatoxin exposure, and dietary habits, which are prevalent in northeastern India, could contribute to the observed patterns [ 21 ]. This study highlights the potential utility of p16, APC, GSTP1, and MGMT hypermethylation as biomarkers for HCC. Their absence in controls indicates their specificity for cancerous tissue, making them promising candidates for diagnostic purposes. However, the relatively low frequency of hypermethylation observed in this cohort suggests that these markers might need to be combined with other molecular or clinical indicators to improve sensitivity. While a relatively small proportion of individuals in this study (4–8%) may directly benefit from epigenetic therapy targeting hypermethylation, the identification of such patients highlights the importance of molecular profiling in precision medicine. Future research should focus on expanding the cohort, identifying additional genes involved, and developing targeted therapies for patients with similar molecular alterations. This research study has compared epigenetic profiles between healthy and diseased tissues of Liver Cancer and helped in identifying cancer-driving alterations. This understanding is pivotal for the identification of biomarkers that facilitate early HCC detection, which is crucial for effective treatment and patient prognosis. Epigenetically silenced genes, in particular, may serve as indicators of early-stage HCC, providing valuable insights for timely intervention. Additionally, understanding the epigenetic landscape paves the way for the development of targeted therapies, including personalized medicine strategies aimed at reversing gene silencing. Such therapies hold promise for combination treatments integrating antiviral and epigenetic approaches. Exploring how HBV and HCV influence host cell epigenetic reprogramming also broadens our understanding of the impact of chronic viral infections on the genome. Methods Epidemiological data, including socio-demographic profiles, selected occupational exposures, personal and family cancer histories, smoking, chewing, alcohol consumption behaviors, and dietary habits, were collected using a pre-designed questionnaire. Trained social workers/investigators conducted face-to-face interviews with participants after obtaining informed consent. All experimental protocols were approved by the Institutional Animal Ethical Committee (IAEC), ICMR-RMRC, Dibrugarh, Assam, India. The detailed epidemiological data have been previously published [21]. For this study, demographic features including age, sex, educational background, marital status, occupation are discussed, as the primary objective was to analyze the methylation profiles of selected genes and examine their association with HBV/HCV infection risk in liver cancer patients. For sample collection, 3–4 mL of blood was drawn from both cases and controls into EDTA vials and the samples were kept at −80°C for until use. HBV and HCV infections were determined using HBsAg ELISA kit for quantitative detection of HBsAg in serum or plasma. Genomic DNA extraction from blood samples was carried out via spin column method (QIAamp Blood DNA Mini Kit). Analysis on methylation status of DNA was performed using methylation-specific PCR (MSP) after bisulfite modification of the DNA extracted (EpiTect Fast DNA Bisulfite Kit). Modified DNA obtained was kept at −20°C until further use. The methylation status of p16, APC, GSTP1, and MGMT genes was confirmed via agarose gel electrophoresis (2%), as detailed in Figures 3–6. Methylation-specific PCR The DNA methylation patterns in CpG islands of genes – p16, APC, GSTP1, MGMT promoter regions were analyzed using MSP. Each DNA sample underwent bisulphite treatment to differentiate methylated from unmethylated sequences. Two separate PCRs were conducted for each sample: one using M primers (for methylated DNA) and the other using U primers (for unmethylated DNA). Table 1 displays the details of primer sequences, product size, and annealing temperatures used. Gel electrophoresis The PCR amplified products were separated on 2% agarose gels stained with ethidium bromide and electrophoresis run was performed for 40 minutes at 100 V. DNA molecular weight markers (100 bp for p16 and 50 bp for APC, MGMT, and GSTP1) were included in each gel to determine product size. Bands were visualized under a UV transilluminator and photographed for analysis. Statistical analysis All collected data were validated, entered into a database, and analyzed using SPSS v.20. Descriptive statistics was performed to summarize the demographic features of cases and controls. Two-sided p-values (<0.05) was applied for determination of statistical significance. Abbreviations Hepatocellular Carcinoma (HCC); Population-Based Cancer Registry (PBCR); Adenomatous Polyposis Coli (APC); GSTP1 (Glutathione S-Transferase Pi 1); MGMT (O-6-Methylguanine-DNA Methyltransferase); Hepatitis B (HBV); Hepatitis C (HCV); World Health Organization (WHO); Non-alcoholic Steatohepatitis (NASH); Non- alcoholic Fatty Liver Disease (NAFLD); Ethylenediaminetetraacetic acid (EDTA). Declarations Acknowledgements: General: The work was funded by the Department of Health Research (DHR), Govt. of India for which the authors are grateful. The authors additionally thank all of the project's research team members and other project stakeholders for their contributions to the study. Funding Statement: The work was funded by the Department of Health Research (DHR), Govt. of India. Conflict of Interest: The authors declare no conflict of interests Ethical Declaration: All procedure were conducted in adherence to ethical guidelines. Informed consent was obtained from all participants. Authors Contribution: KRD and KN contributed to the study design.SKP conducted all statistical analyses and prepared the statistical tables and figures in the manuscript. The manuscript preparation was supervised under the guidance of KRD. ST and CDB helped in sample collection and data acquisition from the Arunachal and Sikkim sites of the study. PB and DB wrote and edited the manuscript. The manuscript was reviewed by AD. 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Lou, C. et al. Aberrant methylation of multiple genes and its clinical implication in hepatocellular carcinoma. Chin. J. Oncol. 30 (11), 831–836 (2008). Phukan, R. K. et al. Association of processed food, synergistic effect of alcohol and HBV with hepatocellular carcinoma in a high incidence region of India. Cancer Epidemiol. 53 , 35–41 (2018). Additional Declarations No competing interests reported. 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. We do this by developing innovative software and high quality services for the global research community. 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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-6373578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453379558,"identity":"d612ef5a-2796-4f0a-9885-c1bee52916b5","order_by":0,"name":"Kangjam Rekha Devi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACCQYGZmYgBiMQkAMRBx6QosUYrCWBKC1QkNgAIvFpkZx9+LBxQY21nMFxBrZPN/fYpc8PO/wQaIudnG4Ddi3SfGnJyTOOpRsbHGZgnp3zLDl34+00A6CWZGOzA9i1yPHwGB/mYTucOLMZ6MKcAwdyN85OAGk5kLgNpxb+z4d5/h2uh2lJN5yd/gGvFmkeHuZk3rbDCfzMEC0J8tI5+G2R7GEzNubtSzfsZ2ZsBmpJNtwgnVNwIMEAt18kzjA/lub5Zi3Pxn/4MFCLnbz87PTNHz5U2Mnh0oIEGBvAlAFYpQFB5UhAvoEU1aNgFIyCUTASAAD+cFcnHOwglQAAAABJRU5ErkJggg==","orcid":"","institution":"ICMR-Regional Medical Research Centre, Dibrugarh","correspondingAuthor":true,"prefix":"","firstName":"Kangjam","middleName":"Rekha","lastName":"Devi","suffix":""},{"id":453379559,"identity":"420c6ac1-7c42-4b38-b95c-03bc23b7da60","order_by":1,"name":"Kanwar Narain","email":"","orcid":"","institution":"ICMR-Regional Medical Research Centre, Dibrugarh","correspondingAuthor":false,"prefix":"","firstName":"Kanwar","middleName":"","lastName":"Narain","suffix":""},{"id":453379560,"identity":"ce9e1ba6-3a65-41a3-a9b4-ed421d0d6445","order_by":2,"name":"Priyanka Borah","email":"","orcid":"","institution":"ICMR-Regional Medical Research Centre, Dibrugarh","correspondingAuthor":false,"prefix":"","firstName":"Priyanka","middleName":"","lastName":"Borah","suffix":""},{"id":453379561,"identity":"61dc5029-7c06-49f4-b4e0-591be6c9facc","order_by":3,"name":"Deepsikha Bhowmik","email":"","orcid":"","institution":"ICMR-Regional Medical Research Centre, Dibrugarh","correspondingAuthor":false,"prefix":"","firstName":"Deepsikha","middleName":"","lastName":"Bhowmik","suffix":""},{"id":453379563,"identity":"ce6d3a31-1ea8-4296-8632-606461636e44","order_by":4,"name":"Archana Deka","email":"","orcid":"","institution":"ICMR-Regional Medical Research Centre, Dibrugarh","correspondingAuthor":false,"prefix":"","firstName":"Archana","middleName":"","lastName":"Deka","suffix":""},{"id":453379565,"identity":"69babf90-adfc-4177-95f3-b41c94526d35","order_by":5,"name":"Sanjiv K. Phukan","email":"","orcid":"","institution":"ICMR-Regional Medical Research Centre, Dibrugarh","correspondingAuthor":false,"prefix":"","firstName":"Sanjiv","middleName":"K.","lastName":"Phukan","suffix":""},{"id":453379567,"identity":"424940ca-e815-477e-82e8-1dc9434f19ad","order_by":6,"name":"Sopai Tawsik","email":"","orcid":"","institution":"Tomo Riba Institute of Health and Medical Science, Naharlagun, Arunachal Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Sopai","middleName":"","lastName":"Tawsik","suffix":""},{"id":453379569,"identity":"ed5848a2-f9de-4ccb-8248-c3bd522966d5","order_by":7,"name":"Chewang D. Bhutia","email":"","orcid":"","institution":"STNM Hospital, Sikkim","correspondingAuthor":false,"prefix":"","firstName":"Chewang","middleName":"D.","lastName":"Bhutia","suffix":""}],"badges":[],"createdAt":"2025-04-04 06:08:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6373578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6373578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82358676,"identity":"42206dc2-f84a-4ba7-9920-a70fdb295ded","added_by":"auto","created_at":"2025-05-09 11:25:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7747,"visible":true,"origin":"","legend":"\u003cp\u003eSex distribution of the study subjects\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6373578/v1/814f15317200ae7bff110196.png"},{"id":82356147,"identity":"341ee658-63e4-4a47-9060-6bc354829c8b","added_by":"auto","created_at":"2025-05-09 11:17:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11276,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of HBsAg and HCV among liver cancer patients\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6373578/v1/d2bbaa656d7f537c76fc755d.png"},{"id":82356149,"identity":"84103013-017e-451d-b268-f7863e1c0d70","added_by":"auto","created_at":"2025-05-09 11:17:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136790,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of gel image of DNA bands showing methylation of p16 gene\u003c/p\u003e\n\u003cp\u003e(M: Primers for amplification of methylated DNA; U: Primers for amplification of unmethylated DNA)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6373578/v1/2449d4e278d58ff8a40f9ef3.png"},{"id":82356145,"identity":"ba0ae4cb-d08f-4a32-b19e-ea3428f839fd","added_by":"auto","created_at":"2025-05-09 11:17:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101181,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of gel image of DNA bands showing methylation of APC gene\u003c/p\u003e\n\u003cp\u003e(M: Primers for amplification of methylated DNA; U: Primers for amplification of unmethylated DNA)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6373578/v1/b6868865bdafc7f62f88c97c.png"},{"id":82356158,"identity":"0c55a5f4-e6ed-46e5-b906-7e2a6155bba8","added_by":"auto","created_at":"2025-05-09 11:17:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":284641,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of gel image of DNA bands showing methylation of GSTP1 gene\u003c/p\u003e\n\u003cp\u003e(M: Primers for amplification of methylated DNA; U: Primers for amplification of unmethylated DNA)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6373578/v1/f1c19741c8da1cdad6336260.png"},{"id":82359800,"identity":"bc044043-a2fb-4e25-a447-ec6397b3e930","added_by":"auto","created_at":"2025-05-09 11:33:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":106428,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of gel image of DNA bands showing methylation of MGMT gene\u003c/p\u003e\n\u003cp\u003e(M: Primers for amplification of methylated DNA; U: Primers for amplification of unmethylated DNA)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6373578/v1/a10c8248e803b0310e353e59.png"},{"id":88496166,"identity":"c9229b18-8a53-401c-90bd-223ac1014477","added_by":"auto","created_at":"2025-08-07 06:01:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1592622,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6373578/v1/15f9f5ea-c6a1-486c-ba1e-ee08c2a3f1ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epigenetic modification and risk of Hepatocellular carcinoma in high incidence region of Northeast India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer is one of the deadliest cancers worldwide, posing a significant challenge due to its high incidence and mortality rates. In 2020, the World Health Organization (WHO) and the International Agency for Research on Cancer reported that liver cancer accounted for 4.7% of all cancer cases globally, ranking as the 6th most commonly diagnosed cancer. Even more alarming, it was the third leading cause of cancer-related deaths, highlighting the need for improved prevention, diagnosis, and treatment options [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHepatocellular carcinoma (HCC) is the most common type of liver cancer, responsible for 75\u0026ndash;85% of cases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It often develops as a result of chronic liver damage caused by a combination of environmental and genetic factors. Chronic infections with Hepatitis B virus (HBV) or Hepatitis C virus (HCV) remain the primary risk factors, accounting for around 80% of HCC cases worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Other contributors include aflatoxin exposure, excessive alcohol use, obesity, non-alcoholic steatohepatitis (NASH), and genetic conditions like hereditary hemochromatosis.\u003c/p\u003e \u003cp\u003eThe prevalence of risk factors varies across regions. In East Asia and sub-Saharan Africa, chronic HBV infections are the dominant cause of HCC due to high rates of vertical\u003c/p\u003e \u003cp\u003etransmission. In contrast, HCV is more prevalent in Western countries and Japan, where it remains a leading driver of liver cancer cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Emerging trends also show a growing burden of liver cancer associated with metabolic disorders like obesity and diabetes, especially in developed countries, driven by the rise of Non-alcoholic Fatty Liver Disease (NAFLD) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTreatment options for advanced HCC remain limited, and late-stage diagnosis often leaves patients with poor prognoses. Sorafenib, a tyrosine kinase inhibitor, has been the standard treatment for advanced cases, providing a modest survival benefit of 3\u0026ndash;5 months [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent advancements, however, are offering hope. Immunotherapy, particularly the combination of atezolizumab and bevacizumab, has shown superior efficacy compared to sorafenib, significantly improving progression-free survival and overall survival in patients with unresectable HCC [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite these advancements, the 5-year survival rate for liver cancer remains dismally low, under 20% globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This highlights the pressing need for early detection strategies, particularly in high-risk populations, and continued research into innovative therapies and preventative measures.\u003c/p\u003e \u003cp\u003eSeveral epigenetic studies, which examine heritable changes in gene activity without affecting the DNA sequence, may provide a significant contribution to our understanding of the mechanisms underlying the development and progression of HCC and, consequently, may identify new biomarkers for its diagnosis. DNA methylation, histone modifications, non- coding RNAs, and other factors are involved in epigenetics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. DNA methylation is a critical epigenetic alteration vital for controlling transcription and regulating gene expression. It is well-established that epigenetic regulation in tumor cells is important for the development of HCC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Methylation of cytosine nucleotides (5-MeC) is the most common epigenetic change in mammalian DNA [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although 70\u0026ndash;80% of CpG sites in human DNA are methylated, which is more frequently observed at repetitive DNA sites or in regions of low CpG density, contrarily in 1\u0026ndash;2% of the genome in healthy individuals, represents a completely unmethylated CpG island [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. HBV and HCV infections can lead to the inactivation or suppression of the p16 gene, accelerating the progression of liver cancer. HBV X protein and HCV core protein have been shown to disrupt p16 function, promoting cell cycle progression and cellular proliferation. As an important epigenetic regulator, the viral protein HBx activates the DNA methyltransferase family and promotes the hypermethylation of a particular tumor suppressor gene (GSTP1, CDKN2B, and RASSF1A). Transcriptional silencing and loss of protein expression are often associated with CpG island methylation. p16INK4a, p15INK4b, p14ARF, GSTP1, APC, MGMT, hMLH1, SOCS-1, E-cadherin, and RASSF1A are a few genes that are silenced by this mechanism [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. One of the most frequently altered tumor suppressor genes is p16. Most aberrant promoter methylation in HCC is responsible for the loss of p16, which is a cyclin-dependent kinase inhibitor that disrupts the cell cycle and interrupts cell proliferation. In non-viral liver cancer, inactivation of p16 due to mutations, deletions, or promoter hypermethylation can similarly lead to unchecked cell proliferation. The absence of viral factors means the carcinogenic process is more directly linked to genetic and epigenetic alterations of p16. Influence of HBV and HCV infections are also observed Adenomatous Polyposis Coli (APC). The Wnt/β-catenin signaling pathway, which is regulated in part by APC, can be aberrantly activated in HBV/HCV-associated liver cancers. Viral proteins may indirectly influence APC function, contributing to the pathogenesis of liver cancer through dysregulation of cell growth and apoptosis. Mutations or loss of APC in liver cancer not related to HBV or HCV can similarly lead to activation of Wnt/β-catenin signaling, promoting oncogenesis. The specific impact of APC mutations may, however, be more pronounced in the context of genetic predispositions and environmental factors other than viral infections. The influence of HBV/HCV infections on GSTP1 (Glutathione S-Transferase Pi 1) can be understood in this manner: Oxidative stress is a hallmark of chronic hepatitis infection, which\u003c/p\u003e \u003cp\u003ecan be exacerbated by impaired GSTP1 function. HBV and HCV increase oxidative stress and inflammation, and the reduced detoxification capability due to GSTP1 silencing can lead to higher DNA damage rates and cancer progression. In liver cancers not associated with viral infections, GSTP1's role in detoxifying carcinogens remains critical. Environmental toxins and metabolic byproducts can cause DNA damage if not properly detoxified, highlighting the importance of GSTP1 in protecting against carcinogenesis. The HBV/HCV infections have significant impact on MGMT (O-6-Methylguanine-DNA Methyltransferase). The viral promotion of a pro-oxidant state can increase the formation of alkylated DNA adducts. MGMT's role in repairing these lesions is crucial for preventing mutagenesis. In HBV or HCV infections, the increased oxidative DNA damage coupled with impaired MGMT function can synergistically elevate cancer risk. The importance of MGMT in repairing DNA damage remains critical in the absence of viral infections. Environmental and endogenous alkylating agents pose a risk for initiating liver cancer, and the efficiency of MGMT-mediated repair mechanisms is a key factor in preventing malignancy.\u003c/p\u003e \u003cp\u003eAccording to the Population-based Cancer Registry (PBCR) Report, 2021 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the northeastern states including Arunachal Pradesh and Sikkim have reported the highest incidence of cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although the prevalence of HCC is becoming more well-recognized in the states of Arunachal Pradesh and Sikkim, little is known about epigenetic alterations and the risk of liver cancer in this area. These aberrant methylation patterns can be a great way to diagnose and monitor people who are at high risk for developing HCC. However, the significance of epigenetic alteration is still not fully understood. Thus, the objective of this study is to analyze the methylation profile of the tumor suppressor genes (APC and p16) and DNA repair genes (MGMT and GSTP1) and to establish the relationship between methylation status and HBV/HCV infection risk associated with liver cancer patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAltogether 222 blood samples (germline DNA from WBCs) were collected for the study, including 130 samples (55 cases and 75 controls) from Arunachal Pradesh and 92 samples (46 cases and 46 controls) from Sikkim. About 62.4% of total participants were male and 37.6% were female (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average age of liver cancer cases (61.6 years for hepatitis-negative and 50.6 years for hepatitis-positive cases) was significantly higher compared to controls (31.8 years for hepatitis-positive and 51.4 years for hepatitis-negative controls) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Demographic analysis revealed that a major section of liver cancer patients were illiterate (63.6% for hepatitis-negative and 75.0% for hepatitis-positive cases), married (83.1% for hepatitis-negative and 83.3% for hepatitis-positive cases), and self-employed/employed (59.7% for hepatitis-negative and 75.0% for hepatitis-positive cases). Regarding HBV and HCV infection status, 22.77% (n\u0026thinsp;=\u0026thinsp;23) of cases and 22.27% (n\u0026thinsp;=\u0026thinsp;33) of controls were HBsAg positive, whereas 3.96% (n\u0026thinsp;=\u0026thinsp;4) of cases and 8.26% (n\u0026thinsp;=\u0026thinsp;10) of controls were HCV reactive. Additionally, 2.97% (n\u0026thinsp;=\u0026thinsp;3) of cases and 3.3% (n\u0026thinsp;=\u0026thinsp;4) of control samples tested positive for both HBsAg and HCV (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When comparing liver cancer cases with and without HBV/HCV infection, patients without HBV/HCV infection had an average age of 61.6 years, whereas those with HBV/HCV infection were younger, with an average age of 50.6 years (p-value\u0026thinsp;=\u0026thinsp;0.014). Liver cancer patients were predominantly male, married, illiterate, and self- employed/employed, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer Sequences, Annealing Temperatures, and Product Sizes for Methylation-Specific PCR of p16, APC, MGMT, and GSTP1 Genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForward Primer (5ʹ-3ʹ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReverse Primer (5ʹ-3ʹ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnealing Temp (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProduct Size (bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTATTAGAGGGTGGGGCGGATCGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGACCCCGAACCGCGACCGTAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTATTAGAGGGTGGGGTGGATTGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCAACCCCAAACCACAACCATAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAACCAAAACGCTCCCCAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTTATATGTCGGTTACGTGCGTTTATAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAACCAAAACACTCCCCATTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAGTTATATGTTGGTTATGTGTGTTTAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMGMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTTCGACGTTCGTAGGTTTTCGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCACTCTTCCGAAAACGAAACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTTGTGTTTTGATGTTTGTAGGTTTTTGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAACTCCACACTCTTCCAAAAACAAAACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGSTP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCGGGGTGTAGCGGTCGTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCCCCAATACTAAATCACGACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATGTTTGGGGTGTAGTGGTTGTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCACCCCAATACTAAATCACAACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of liver cancer patients and controls corresponding to the groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDemographic Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGROUP A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGROUP B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGROUP C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGROUP D\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003cp\u003e(Hepatitis Negative)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003cp\u003e(Hepatitis Positive)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;39) (HBV/HCV\u003c/p\u003e \u003cp\u003ePositive)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;82) (HBV/HCV\u003c/p\u003e \u003cp\u003eNegative)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage age (in years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.6 (\u0026plusmn;\u0026thinsp;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.6 (\u0026plusmn;\u0026thinsp;12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.8 (\u0026plusmn;\u0026thinsp;13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.4 (\u0026plusmn;\u0026thinsp;19.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex Male\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (59.7)\u003c/p\u003e \u003cp\u003e31 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (70.8)\u003c/p\u003e \u003cp\u003e07 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (56.4)\u003c/p\u003e \u003cp\u003e17 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (62.3)\u003c/p\u003e \u003cp\u003e31 (37.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Status* Illiterate\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eLiterate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (63.6)\u003c/p\u003e \u003cp\u003e28 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (75.0)\u003c/p\u003e \u003cp\u003e06 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09 (23.1)\u003c/p\u003e \u003cp\u003e30(76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(21.9)\u003c/p\u003e \u003cp\u003e64 (78.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status* Unmarried Married\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eEver Married\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e05 (6.5)\u003c/p\u003e \u003cp\u003e64 (83.1)\u003c/p\u003e \u003cp\u003e08 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e20 (83.3)\u003c/p\u003e \u003cp\u003e04 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (43.6)\u003c/p\u003e \u003cp\u003e20 (51.3)\u003c/p\u003e \u003cp\u003e02 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (20.7)\u003c/p\u003e \u003cp\u003e62 (75.6)\u003c/p\u003e \u003cp\u003e03 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupational Status Unemployed/Student Employed/Self Employed\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eHouse wives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (18.2)\u003c/p\u003e \u003cp\u003e46 (59.7)\u003c/p\u003e \u003cp\u003e17 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e04 (25.0)\u003c/p\u003e \u003cp\u003e20 (75.0)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (61.5)\u003c/p\u003e \u003cp\u003e05 (12.8)\u003c/p\u003e \u003cp\u003e10 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (21.9)\u003c/p\u003e \u003cp\u003e47 (57.3)\u003c/p\u003e \u003cp\u003e17 (20.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e*p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the chi square test *Here, Hepatitis refers to HBV and HCV infection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCancer staging analysis revealed that among the 77 liver cancer patients without HBV/HCV infection, the distribution was: Stage I (n\u0026thinsp;=\u0026thinsp;15, 19.48%), Stage II (n\u0026thinsp;=\u0026thinsp;15, 19.48%), Stage IIIA (n\u0026thinsp;=\u0026thinsp;19, 24.67%), Stage IIIB (n\u0026thinsp;=\u0026thinsp;11, 14.28%), Stage IIIC (n\u0026thinsp;=\u0026thinsp;1, 1.29%), Stage IVA (n\u0026thinsp;=\u0026thinsp;2, 2.59%),\u003c/p\u003e \u003cp\u003eand Stage IVB (n\u0026thinsp;=\u0026thinsp;14, 18.18%). In contrast, among the 24 liver cancer patients with infections\u003c/p\u003e \u003cp\u003eassociated with HBV/HCV, majority were in Stage IIIA (n\u0026thinsp;=\u0026thinsp;10, 41.7%) and Stage IIIB (n\u0026thinsp;=\u0026thinsp;9, 37.5%), while fewer were in Stage I (n\u0026thinsp;=\u0026thinsp;3, 12.5%) and Stage II (n\u0026thinsp;=\u0026thinsp;2, 8.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings indicate that liver cancer patients without HBV/HCV infection were more possibly to present with advanced cancer stages in contrast to those with HBV/HCV infection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStaging of liver cancer patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer staging\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A (n\u0026thinsp;=\u0026thinsp;77) (Liver cancer patients\u003c/p\u003e \u003cp\u003ewithout Hepatitis infection)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup B (n\u0026thinsp;=\u0026thinsp;24) (Liver cancer patients\u003c/p\u003e \u003cp\u003ewith Hepatitis infection)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (19.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (19.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IIIA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (24.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (41.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IIIB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (14.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (37.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IIIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IVA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage IVB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (18.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* Percentage in parentheses *Here, Hepatitis refers to HBV and HCV inhection\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; DNA Hypermethylation of p16, APC, GSTP1, and MGMT genes in HCC Cases and Controls\u003c/h2\u003e \u003cp\u003eDNA hypermethylation patterns of p16, APC, GSTP1, and MGMT genes were analyzed in 104 HCC cases and 104 matched controls. Hence, there are four groups: Group A: Hepatitis Negative Cases, Group B: Hepatitis Positive Cases and Group C: Hepatitis Negative Controls, Group D: Hepatitis Positive Controls. Here, Hepatitis refers to HBV/HCV infection. Among Hepatitis-positive HCC cases, hypermethylation of p16, APC, and GSTP1 was observed in 2 cases each (4.0%), while MGMT hypermethylation was also detected in 2 cases (4.0%). In Hepatitis-negative HCC cases, p16 and APC were hypermethylated in 2 cases each (8.3%), GSTP1 in 2 cases (8.3%), and MGMT in 1 case (4.1%). In contrast, no hypermethylation of p16, APC, GSTP1, or MGMT was observed in Hepatitis-positive or Hepatitis-negative control subjects. Overall, hypermethylation of p16, APC, GSTP1, and MGMT was significantly more frequent in HCC cases compared to controls (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), irrespective of Hepatitis status, highlighting the potential role of these epigenetic alterations in hepatocarcinogenesis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethylation profile of tumor suppressor gene and DNA repair gene\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal no. of Liver cancer patient tested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal no. of controls tested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMethylation found in liver cancer cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMethylation found in controls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHepatitis\u003c/p\u003e \u003cp\u003ePositive Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHepatitis\u003c/p\u003e \u003cp\u003eNegative Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHepatitis\u003c/p\u003e \u003cp\u003ePositive Controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHepatitis\u003c/p\u003e \u003cp\u003eNegative Controls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.0% )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGSTP1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMGMT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study demonstrate that hypermethylation of tumor suppressor genes p16, APC, GSTP1, and MGMT is significantly associated with hepatocellular carcinoma (HCC) cases compared to controls, irrespective of HBV infection status. Notably, no hypermethylation of these genes was observed in control subjects, suggesting that these epigenetic alterations are closely linked to the pathogenesis of HCC.\u003c/p\u003e \u003cp\u003eAmong HBV-positive HCC cases, the observed hypermethylation frequencies of p16, APC, GSTP1, and MGMT were relatively low (4.0% for each gene). Similarly, in HBV- negative HCC cases, hypermethylation frequencies for p16, APC, and GSTP1 were slightly higher at 8.3%, while MGMT hypermethylation was observed in 4.1% of cases. These findings suggest that the hypermethylation of these genes may occur independently of HBV infection, although the contribution of HBV-mediated mechanisms to epigenetic dysregulation cannot be excluded. HBV proteins, such as HBx, are known to activate DNA methyltransferases, potentially contributing to the silencing of tumor suppressor genes, but this process may not uniformly affect all genes analyzed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe absence of hypermethylation in the control group, regardless of HBV status, indicates the role of these epigenetic changes in HCC development. Tumor suppressor gene silencing through promoter hypermethylation has been well-documented as a hallmark of carcinogenesis, leading to the loss of critical regulatory functions in cell cycle control, DNA repair, and detoxification processes. Specifically, p16 hypermethylation disrupts cell cycle regulation, while APC inactivation directs to abnormal activation of Wnt/β-catenin signaling pathway. Similarly, GSTP1 silencing compromises cellular detoxification, increasing oxidative stress, and MGMT hypermethylation impairs DNA repair, leading to genomic instability [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe slightly higher hypermethylation rates in HBV-negative cases for genes such as p16 and APC suggest that non-viral factors, including environmental exposures or genetic predispositions, may also play a role in driving these epigenetic alterations. Risk factors such\u003c/p\u003e \u003cp\u003eas alcohol consumption, aflatoxin exposure, and dietary habits, which are prevalent in northeastern India, could contribute to the observed patterns [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study highlights the potential utility of p16, APC, GSTP1, and MGMT hypermethylation as biomarkers for HCC. Their absence in controls indicates their specificity for cancerous tissue, making them promising candidates for diagnostic purposes. However, the relatively low frequency of hypermethylation observed in this cohort suggests that these markers might need to be combined with other molecular or clinical indicators to improve sensitivity. While a relatively small proportion of individuals in this study (4\u0026ndash;8%) may directly benefit from epigenetic therapy targeting hypermethylation, the identification of such patients highlights the importance of molecular profiling in precision medicine. Future research should focus on expanding the cohort, identifying additional genes involved, and developing targeted therapies for patients with similar molecular alterations.\u003c/p\u003e \u003cp\u003eThis research study has compared epigenetic profiles between healthy and diseased tissues of Liver Cancer and helped in identifying cancer-driving alterations. This understanding is pivotal for the identification of biomarkers that facilitate early HCC detection, which is crucial for effective treatment and patient prognosis. Epigenetically silenced genes, in particular, may serve as indicators of early-stage HCC, providing valuable insights for timely intervention. Additionally, understanding the epigenetic landscape paves the way for the development of targeted therapies, including personalized medicine strategies aimed at reversing gene silencing. Such therapies hold promise for combination treatments integrating antiviral and epigenetic approaches. Exploring how HBV and HCV influence host cell epigenetic reprogramming also broadens our understanding of the impact of chronic viral infections on the genome.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eEpidemiological\u0026nbsp;data,\u0026nbsp;including\u0026nbsp;socio-demographic\u0026nbsp;profiles,\u0026nbsp;selected\u0026nbsp;occupational\u0026nbsp;exposures, personal and family cancer histories, smoking, chewing, alcohol consumption behaviors, and dietary habits, were collected using a pre-designed questionnaire. Trained social workers/investigators conducted face-to-face interviews with participants after obtaining informed\u0026nbsp;consent. All experimental protocols were approved by \u0026nbsp;the Institutional\u0026nbsp;Animal\u0026nbsp;Ethical\u0026nbsp;Committee (IAEC),\u0026nbsp;ICMR-RMRC,\u0026nbsp;Dibrugarh,\u0026nbsp;Assam,\u0026nbsp;India.\u0026nbsp;The\u0026nbsp;detailed\u0026nbsp;epidemiological\u0026nbsp;data\u0026nbsp;have\u0026nbsp;been previously\u0026nbsp;published\u0026nbsp;[21].\u0026nbsp;For\u0026nbsp;this\u0026nbsp;study,\u0026nbsp;demographic\u0026nbsp;features\u0026nbsp;including\u0026nbsp;age,\u0026nbsp;sex,\u0026nbsp;educational background, marital status, occupation are discussed, as the primary objective was to analyze the methylation profiles of selected genes and examine their association with HBV/HCV infection risk in liver cancer patients.\u003c/p\u003e\n\u003cp\u003eFor sample collection, 3\u0026ndash;4 mL of blood was drawn from both cases and controls into EDTA vials and the samples were kept at \u0026minus;80\u0026deg;C for until use. HBV and HCV infections were determined using HBsAg ELISA\u0026nbsp;kit for quantitative detection of HBsAg in serum or plasma. Genomic DNA extraction from blood samples was carried out via spin column method (QIAamp\u0026nbsp;Blood\u0026nbsp;DNA\u0026nbsp;Mini\u0026nbsp;Kit).\u0026nbsp;Analysis\u0026nbsp;on\u0026nbsp;methylation\u0026nbsp;status\u0026nbsp;of\u0026nbsp;DNA\u0026nbsp;was\u0026nbsp;performed\u0026nbsp;using methylation-specific PCR (MSP) after bisulfite modification of the DNA extracted (EpiTect Fast DNA Bisulfite Kit). Modified DNA obtained was kept at \u0026minus;20\u0026deg;C until further use. The methylation status of p16, APC, GSTP1, and MGMT genes was confirmed via agarose gel electrophoresis (2%), as detailed in \u003cstrong\u003eFigures 3\u0026ndash;6.\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003ch3\u003eMethylation-specific\u0026nbsp;PCR\u003c/h3\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe DNA methylation patterns in CpG islands of genes \u0026ndash; p16, APC, GSTP1, MGMT promoter regions were analyzed using MSP. Each DNA sample underwent bisulphite treatment to\u003c/p\u003e\n\u003cp\u003edifferentiate methylated from unmethylated sequences. Two separate PCRs were conducted for each sample: one using M primers (for methylated DNA) and the other using U primers (for unmethylated DNA). \u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003edisplays the details of primer sequences, product size, and annealing temperatures used.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003ch3\u003eGel electrophoresis\u003c/h3\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe\u0026nbsp;PCR\u0026nbsp;amplified\u0026nbsp;products\u0026nbsp;were\u0026nbsp;separated\u0026nbsp;on\u0026nbsp;2%\u0026nbsp;agarose\u0026nbsp;gels\u0026nbsp;stained\u0026nbsp;with\u0026nbsp;ethidium\u0026nbsp;bromide and electrophoresis run was performed for 40 minutes at 100 V. DNA molecular weight markers (100 bp for p16 and 50 bp for\u0026nbsp;APC, MGMT, and GSTP1) were included in each gel to determine product size. Bands were visualized under a UV transilluminator and photographed for analysis.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003ch3\u003eStatistical\u0026nbsp;analysis\u003c/h3\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll collected data were validated, entered into a database, and analyzed using SPSS v.20. Descriptive statistics was performed to summarize the demographic features of cases and controls. Two-sided p-values (\u0026lt;0.05) was applied for determination of statistical significance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHepatocellular Carcinoma (HCC); Population-Based Cancer Registry (PBCR); Adenomatous Polyposis Coli (APC); GSTP1 (Glutathione S-Transferase Pi 1); MGMT (O-6-Methylguanine-DNA Methyltransferase); Hepatitis B (HBV); Hepatitis C (HCV); World Health Organization (WHO); Non-alcoholic Steatohepatitis (NASH); Non- alcoholic Fatty Liver Disease (NAFLD); Ethylenediaminetetraacetic acid (EDTA).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral:\u0026nbsp;\u003c/strong\u003eThe\u0026nbsp;work\u0026nbsp;was funded\u0026nbsp;by\u0026nbsp;the\u0026nbsp;Department\u0026nbsp;of\u0026nbsp;Health\u0026nbsp;Research\u0026nbsp;(DHR),\u0026nbsp;Govt.\u0026nbsp;of\u0026nbsp;India for which the authors are grateful. The authors additionally thank all of the project\u0026apos;s research team members and other project stakeholders for their contributions to the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eThe work was funded by the Department of Health Research (DHR), Govt. of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict\u0026nbsp;of\u0026nbsp;Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interests\u003c/p\u003e\n\u003ch3\u003eEthical Declaration:\u003c/h3\u003e\n\u003col\u003e\n \u003cli\u003eAll\u0026nbsp;procedure\u0026nbsp;were\u0026nbsp;conducted in adherence\u0026nbsp;to ethical guidelines.\u003c/li\u003e\n \u003cli\u003eInformed\u0026nbsp;consent\u0026nbsp;was obtained\u0026nbsp;from\u0026nbsp;all\u0026nbsp;participants.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution:\u0026nbsp;\u003c/strong\u003eKRD and KN contributed to the study design.SKP conducted all statistical analyses and prepared the statistical tables and figures in the manuscript. The manuscript preparation was supervised under the guidance of KRD. ST and CDB helped in sample collection and data acquisition from the\u0026nbsp;Arunachal and Sikkim sites of the study. PB and DB wrote and edited the manuscript. The manuscript was reviewed by AD.\u003c/p\u003e\n\u003ch3\u003eData\u0026nbsp;Availability:\u003c/h3\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy\u0026nbsp;Registration:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide. \u003cem\u003eCancer J. Clin.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e (3), 209\u0026ndash;249 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillanueva, A. 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(2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLou, C. et al. Aberrant methylation of multiple genes and its clinical implication in hepatocellular carcinoma. \u003cem\u003eChin. J. Oncol.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (11), 831\u0026ndash;836 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhukan, R. K. et al. Association of processed food, synergistic effect of alcohol and HBV with hepatocellular carcinoma in a high incidence region of India. \u003cem\u003eCancer Epidemiol.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 35\u0026ndash;41 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Carcinoma, DNA methylation, Epigenetic modifications, Tumor suppressor genes, HBV/HCV infection, Biomarkers for liver cancer","lastPublishedDoi":"10.21203/rs.3.rs-6373578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6373578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Hepatocellular carcinoma (HCC) is a leading liver cancer globally, with Northeast India reporting particularly high incidence rates according to Population-Based Cancer Registry (PBCR) data. Despite its prevalence, the role of epigenetic changes, especially DNA methylation, in HCC development in this region remains underexplored. Aberrant DNA methylation patterns offer significant potential as biomarkers for diagnosing and monitoring individuals at high risk. This study focuses on understanding the contribution of DNA methylation to HCC pathogenesis in two states of Northeast India, aiming to provide insights into disease mechanisms and improve early detection strategies in this high-risk population. A population-based case-control study was conducted in Arunachal and Sikkim, two states of North-eastern India, involving a total of 164 participants of HCC (73 histologically-confirmed cases, 91 age and sex-matched controls), with blood samples collected and analyzed for methylation patterns in four key tumor suppressor and DNA repair genes: p16, APC, GSTP1, and MGMT. The findings reveal that methylation was observed in a subset of liver cancer patients, with the highest prevalence in the p16 gene (5 cases) followed by APC, MGMT, and GSTP1. Epidemiological analysis highlighted significant associations between HCC risk and factors such as male predominance, lower literacy levels, alcohol use, and chronic hepatitis B (HBV) or hepatitis C (HCV) infections. This study emphasizes the critical role of epigenetic modifications in HCC pathogenesis and suggests that DNA methylation in tumor suppressor and DNA repair genes could serve as potential biomarkers for early detection and risk assessment of HCC in Northeast India.\u003c/p\u003e","manuscriptTitle":"Epigenetic modification and risk of Hepatocellular carcinoma in high incidence region of Northeast India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:17:03","doi":"10.21203/rs.3.rs-6373578/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":"e4fe7a9c-b01a-4bc4-8c99-fec6c3640b81","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48215481,"name":"Biological sciences/Cancer"},{"id":48215482,"name":"Biological sciences/Molecular biology"},{"id":48215483,"name":"Health sciences/Diseases"},{"id":48215484,"name":"Health sciences/Gastroenterology"},{"id":48215485,"name":"Health sciences/Medical research"},{"id":48215486,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-08-07T05:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 11:17:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6373578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6373578","identity":"rs-6373578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}