Exploring the Evolutionary Mechanism of Hepatitis B and Gastric Cancer Based on Mendelian Randomization Combined with Bioinformatics Analysis

Q: How does the study assess causality between chronic HBV infection and gastric cancer?

It applies two-sample Mendelian randomization using genetic variants as instrumental variables to infer a causal relationship between chronic HBV infection and gastric cancer.

Q: What MR finding supports a causal link?

The MR analysis shows a positive causal association, with IVW OR = 1.165 (95% CI 1.085–1.250, P < .001), and results remain robust in sensitivity analyses.

Q: Which biological pathways and key genes are implicated by the bioinformatics analysis?

Differential expression and WGCNA identify five key targets enriched in Toll-like receptor signaling and PI3K-Akt signaling, with CXCL9 and COL6A2 highlighted as hub genes.

Sep 21, 2026

Upload by Olivia Brown

Chronic hepatitis B virus infection is associated with extrahepatic cancers, yet causal mechanisms remain uncertain. This observational study evaluates causality and evolutionary mechanisms between chronic HBV infection and gastric cancer using two-sample Mendelian randomization (MR) and bioinformatics. Five genetic instruments were selected from 1371 cases and 2938 controls of East Asian descent, with outcome GWAS from the Japanese Biobank. MR indicates a positive causal effect (IVW OR 1.165, 95% CI 1.085–1.250, P < .001) validated by sensitivity analysis.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

Abbreviations

Keywords

Introduction

Background and rationale

® Observational Study Exploring the evolutionary mechanism of hepatitis B and gastric cancer based on Mendelian randomization combined with bioinformatics analysis Huilian Cai, MDa , Tianjian Huang, MDa , Bohui Zheng, MDa , Xianqiong Zhu, BDa , Lisi Zhou, MDa , Jiayu Wu, BDa , Ying Xu, BDa , Shulan Huang, MDb , Yuxuan Huang, BDc , Tian Liu, MDd ,* | Abstract
Chronic hepatitis B virus infection (HBV) infection appears to be associated with extrahepatic cancers. This study aims to evaluate the causality and evolutionary mechanism of chronic HBV infection and gastric cancer through Mendelian randomization (MR) analysis and bioinformatics analysis. We conducted 2-sample MR to investigate the causal relationship between chronic HBV infection and gastric cancer. We identified 5 independent genetic variants closely associated with exposure (chronic HBV infection) as instrumental variables in a sample of 1371 cases and 2938 controls of East Asian descent in Korea. The genome wide association study (GWAS) data for the outcome variable came from the Japanese Biobank. Bioinformatics analysis was used to explore the evolutionary mechanism of chronic HBV infection and gastric cancer. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify key targets that are commonly associated with both diseases, and their biological functions were investigated. Multiple machine-learning models were employed to select hub genes. The MR analysis showed a positive causal relationship between chronic HBV infection and gastric cancer (IVW: OR = 1. 165, 95% CI = 1.085–1.250, P < .001), and the result was robust in sensitivity analysis. According to the bioinformatics analysis, the 5 key targets were mainly enriched in Toll-like receptor signaling and PI3K-Akt signaling. Two hub genes, CXCL9 and COL6A2, were identified, and a high-performing predictive model was constructed. Chronic HBV infection is positively associated with gastric cancer, and the evolutionary mechanism may be related to Toll-like receptor signaling. Prospective studies are still needed to confirm these findings. | | --- | | Abbreviations: AUC = area under the curve, DEGs = differentially expressed genes, GWAS = genome wide association study, HBV = hepatitis B virus infection, IVW = inverse variance weighted, KEGG = Kyoto encyclopedia of genes and genomes, MR = Mendelian randomization, RF = random forest, SNP = single nucleotide polymorphism, SVM = support vector machine, TLRs = toll-like receptors, WGCNA = weighted gene co-expression network analysis. | | Keywords: causal inference, extrahepatic cancers, hepatitis B, Mendelian randomization | 1. Introduction As research on the “inflammation-cancer” transformation deepens, the impact of hepatitis B virus infection (HBV) on cancer has attracted increasing attention. It is estimated that approximately 360 million people worldwide are infected with HBV. [1,2] Previous studies have indicated that chronic HBV infection is a risk factor for hepatocellular carcinoma[3] and is associated with extrahepatic cancers such as pancreatic, lung, gastric, and thyroid. [4,5] However, the results above are based on observational studies with limited evidence, and the causal relationship between chronic HBV infection and extrahepatic cancer remains uncertain. Moreover, these studies may be influenced by confounding factors. Therefore, further research using alternative methods is needed to explore these associations. The Mendelian randomization (MR) analysis improves causal inference by utilizing genetic variation as an instrumental variable to infer the causal relationship between The authors have no funding and conflicts of interest to disclose. All data generated or analyzed during this study are included in this published article [and its supplementary information files]. The authors are accountable for all aspects of the work in ensuring that questions related to the a

FAQs

How does the study assess causality between chronic HBV infection and gastric cancer?

It applies two-sample Mendelian randomization using genetic variants as instrumental variables to infer a causal relationship between chronic HBV infection and gastric cancer.

What MR finding supports a causal link?

The MR analysis shows a positive causal association, with IVW OR = 1.165 (95% CI 1.085–1.250, P < .001), and results remain robust in sensitivity analyses.

Which biological pathways and key genes are implicated by the bioinformatics analysis?

Differential expression and WGCNA identify five key targets enriched in Toll-like receptor signaling and PI3K-Akt signaling, with CXCL9 and COL6A2 highlighted as hub genes.

Sep 21, 2026

Upload by Olivia Brown