Identification of Venous Thromboembolism Driver Genes Based on Integrative Bioinformatics and Machine Learning

Abstract Background: Venous thromboembolism (VTE) is a major cause of morbidity and mortality globally, accompanied with a variety of underlying diseases, and it often doesn't get enough attention for early diagnosis and treatment. VTE is associated with many risk factors, however, the genetic basis of venous thromboembolism remains poorly understood. Methods: We analyzed whole-blood gene expression profiles from 185 VTE patients and 93 normal controls obtained from the GEO database. Differential expression analysis was conducted to identify differentially expressed genes (DEGs), followed by weighted gene co-expression network analysis (WGCNA) to identify key genes through intersection analysis. Subsequently, enrichment analysis was performed on gene modules associated with key genes. Central genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) and random forest algorithms. Additionally, immune cell infiltration was analyzed using CIBERSORT, and pathway functions of key genes were assessed through Gene Set Enrichment Analysis (GSEA). Results: A total of 102 DEGs were identified in VTE. Using weighted gene co-expression network analysis (WGCNA), these DEGs were clustered into 14 modules. Among them, the black module showed significant correlation with VTE (cor=0.48, p<0.0001). Machine learning techniques identified four key genes, namely TXNL1 (AUC=0.840), CD3G (AUC=0.786), MRPL15 (AUC=0.775), and MCTS1 (AUC=0.748), demonstrating high diagnostic efficacy for predicting VTE. These four hub genes were significantly associated with pathways such as TRANSLATION, SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE, and EUKARYOTIC TRANSLATION INITIATION, providing valuable insights into the potential mechanisms underlying VTE. Immunoinfiltration analysis using ssGSTEA revealed significant positive correlations between Activated CD8 T cell, Gamma delta T cell, and hub genes, suggesting the potential of these hub genes and immune cell populations for targeted immunotherapy in VTE patients, thereby opening new avenues for therapeutic intervention. Conclusion: TXNL1, CD3G, MRPL15, and MCTS1 have emerged as promising candidate driver genes for VTE. These central genes hold the potential to provide valuable insights into the molecular diagnosis and treatment of VTE.