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Updated: Jun 1, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
Published on: March 1, 2024
Mendelian Randomization and Transcriptome Data Analysis Reveal Bidirectional Causal Relationships and Mechanisms
Junyang Ma1,2, Yuan Gao3, Shufu Hou1
1Laboratory of Metabolism and Gastrointestinal Tumor, Shandong Provincial QianFoShan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
Introduction:
Gastric cancer (GC) is the fifth most common cancer globally, and the relationship between type 2 diabetes mellitus (T2DM) and cancer risk remains controversial.
Methods:
We performed Mendelian randomization (MR) analysis using publicly available GWAS data to assess the causal relationship between T2DM and GC, validated by heterogeneity and pleiotropy analyses. Transcriptomic data from TCGA and GEO were analyzed to identify common differentially expressed genes (DEGs). Weighted gene coexpression network analysis (WGCNA) was used to construct a prognostic risk model. Drug sensitivity and immune infiltration were evaluated using GDSC and ImmuCellAI, respectively. Additionally, gene mutation analysis was conducted using TCGA data.
Results:
The Mendelian randomization analysis revealed a causal relationship between T2DM and GC at the genetic level. Specifically, the causal effect of T2DM on GC was estimated with an odds ratio (OR) of 1.32 (95% CI: 1.12-1.56), while the reverse causal effect of GC on T2DM was estimated at an OR of 0.78 (95% CI: 0.67-0.91). Sensitivity analyses, including Cochran's Q test and the leave-one-out test, confirmed the robustness of these findings. We constructed a prognostic risk score consisting of three T2DM- related genes (CST2, PSAPL1, and C4orf48) based on transcriptome data analysis. Patients with high-risk scores exhibited significantly worse overall survival (OS) (p < 0.05). Cox regression analysis further confirmed the independent predictive value of the risk score for GC prognosis. Our predictive model demonstrated good performance, with an AUC of 0.786 in the training set and 0.757 in the validation set. Gene enrichment analysis indicated that the genes shared between T2DM and GC were associated with inflammatory response, immune response, and metabolic pathways. Tumor immune microenvironment analysis suggested that immune evasion mechanisms may play a key role in developing GC in patients with coexisting T2DM.
Conclusion:
T2DM is associated with reduced GC risk. The risk score and model may help guide GC prognosis and management.
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