International journal of genomics · 2026
Integrative Mendelian Randomization and Pathomics Analysis Using Expression Quantitative Trait Loci and Genome-Wide Association Study Data Identifies Mismatch Repair Genes as Prognostic Biomarkers in Gastric Adenocarcinoma.
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Zhang Yingqiao, Li Dan, Jin Yuyao, Zhao Wenjuan, Guo Pengyu, Wang Ziqi, Zhu Xinyu, Ma Zhenqi, Sui Lin, Liang Yanmeng, Liu Yang, Zhang Xiushi
Аннотация
BACKGROUND: Mismatch repair (MMR) genes are implicated in stomach adenocarcinoma (STAD). This study assessed their causal role in STAD, prognostic value, and developed a histopathology-based model to predict MutS homolog 2 (MSH2) expression.
METHODS: Using data from the IEU OpenGWAS database, five Mendelian randomization (MR) models evaluated causal links between MMR genes and gastric cancer (GC). Prognostic relevance was assessed via survival analysis. A random forest model using TCGA hematoxylin and eosin-stained images was trained to predict MSH2 expression. Biological insights were explored via pathomics score, gene set enrichment analysis (GSEA), immune infiltration, and tumor mutational burden (TMB).
RESULTS: MR analysis identified MLH1 and PMS2 as risk genes, while MSH2 had a protective effect. Cox regression confirmed MSH2 as an independent protective factor (HR = 0.690, 95% CI: 0.487-0.977, p < 0.05). The pathomics model predicted MSH2 expression with an AUC of 0.811. A comparison of high- and low-survival-probability (SP) groups showed differentially expressed genes, including SFRP4. The high-SP group had elevated TMB and TP53 mutation frequency.
CONCLUSION: MMR genes, especially MSH2, are critical in STAD development and prognosis. The image-based model effectively predicts MSH2 expression, supporting the integration of genomic and histopathologic data for personalized GC care.
METHODS: Using data from the IEU OpenGWAS database, five Mendelian randomization (MR) models evaluated causal links between MMR genes and gastric cancer (GC). Prognostic relevance was assessed via survival analysis. A random forest model using TCGA hematoxylin and eosin-stained images was trained to predict MSH2 expression. Biological insights were explored via pathomics score, gene set enrichment analysis (GSEA), immune infiltration, and tumor mutational burden (TMB).
RESULTS: MR analysis identified MLH1 and PMS2 as risk genes, while MSH2 had a protective effect. Cox regression confirmed MSH2 as an independent protective factor (HR = 0.690, 95% CI: 0.487-0.977, p < 0.05). The pathomics model predicted MSH2 expression with an AUC of 0.811. A comparison of high- and low-survival-probability (SP) groups showed differentially expressed genes, including SFRP4. The high-SP group had elevated TMB and TP53 mutation frequency.
CONCLUSION: MMR genes, especially MSH2, are critical in STAD development and prognosis. The image-based model effectively predicts MSH2 expression, supporting the integration of genomic and histopathologic data for personalized GC care.