生信分析胃癌化疗耐药嘌呤代谢关键基因及预后关系

Bioinformatics Analysis of Purine Metabolism Key Genes in Gastric Cancer Chemotherapy Resistance and Prognosis

摘要: 目的：探讨胃癌化疗耐药形成机制中嘌呤代谢通路关键基因的作用及其调控机制，并评估其临床预测价值。方法：通过生物信息学分析筛选化疗耐药相关差异基因，结合功能富集分析确定嘌呤代谢通路的关键基因，并验证其表达水平及与患者预后的关系。同时，探索其可能的调控机制，并构建基于关键基因的预后模型。结果：化疗耐药组中嘌呤代谢通路活性显著增强，IMPDH2高表达且与胃癌患者较差预后相关。进一步分析提示c-Myc可能作为IMPDH2的上游转录因子，而KRAS通过MAPK通路上调c-Myc，推测存在c-Myc-IMPDH2-KRAS闭环调控机制。基于IMPDH2及相关基因构建的预后模型，能够有效预测胃癌患者的5年生存率和无病生存率。结论：本研究发现IMPDH2在胃癌化疗耐药中起关键作用，并推测其通过c-Myc-IMPDH2-KRAS闭环机制促进嘌呤代谢重编程及化疗耐药。构建的预后模型具有良好的预测能力，为胃癌精准治疗和个体化管理提供了新思路。

Abstract: Objective: To explore the role and regulatory mechanisms of key genes in the purine metabolism pathway involved in gastric cancer chemotherapy resistance and to evaluate their clinical prognostic value. Methods: Bioinformatics analysis was used to identify chemotherapy resistance-related differentially expressed genes. Functional enrichment analysis was performed to identify key genes in the purine metabolism pathway, followed by validation of their expression levels and association with patient prognosis. Potential regulatory mechanisms were explored, and a prognostic model based on the key genes was constructed. Results: The purine metabolism pathway was significantly upregulated in the chemotherapy-resistant group, with IMPDH2 highly expressed and associated with poor prognosis in gastric cancer patients. Further analysis suggested that c-Myc may act as the upstream transcription factor of IMPDH2, while KRAS may regulate c-Myc via the MAPK pathway, indicating the existence of a c-Myc-IMPDH2-KRAS feedback regulatory loop. A prognostic model based on IMPDH2 and related genes effectively predicted the 5-year overall survival and disease-free survival rates of gastric cancer patients. Conclusion: This study identified IMPDH2 as a key player in gastric cancer chemotherapy resistance and proposed that it may promote purine metabolism reprogramming and chemotherapy resistance via the c-Myc-IMPDH2-KRAS feedback loop. The constructed prognostic model demonstrated good predictive power, offering new insights for precision therapy and personalized management of gastric cancer.