基于生物信息学分析构建γ-氨基丁酸相关模型评估胰腺癌的预后和免疫治疗反应OA

Objective To develop and validate a comprehensive model based on gamma-aminobutyric acid(GABA)-related genes to characterize the tumor microenvironment(TME),clinical outcomes and response to immunotherapy in pancreatic ductal adenocarcinoma(PDAC).Methods Differential gene expression analysis was performed using the R"Limma"package on data from The Cancer Genome Atlas(TCGA)PDAC cohort and normal pancreatic tissue data from the Genotype-Tissue Expression(GTEx)project.Differentially expressed genes were cross-referenced with a known GABA-related gene set to identify GABA-related genes dysregulated in PDAC.Functional enrichment analysis was conducted using the"clusterProfiler"package.Key prognosis-associated genes were identified via LASSO regression,and a multigene prognostic signature was constructed based on their expression levels and regression coefficients.A nomogram was established to determine independent prognostic factors.The immune cell infiltration within the TME was evaluated using multiple algorithms,including ESTIMATE and CIBERSORT,while the response to immunotherapy was assessed using the tumor immune dysfunction and exclusion(TIDE)algorithm.Results In the TCGA cohort,we established an 11-GABA-related gene prognostic signature that effectively stratified PDAC patients into high-and low-risk groups.Patients in the high-risk group had a significantly shorter overall survival and exhibited decreased immune cell infiltration in the TME.Furthermore,in the IMvigor210 immunotherapy cohort,the signature score was correlated with patient responsiveness to immune checkpoint blockade(ICB)therapy.Conclusion This bioinformatics-based study constructed a GABA-related gene signature that effectively predicts patient prognosis in PDAC.It also reveals its association with an immunosuppressive TME and resistance to ICB therapy,providing a novel potential biomarker for personalized treatment strategies.