基于内质网应激相关基因的卵巢癌预后模型构建及其在免疫治疗预测中的应用OA

Objective:To construct a prognostic model for ovarian cancer based on endoplasmic reticulum stress-related genes(ERSRGs)and systematically evaluate its ability to predict patient survival outcomes and response to immunotherapy.Methods:Transcriptomic data and clinical information of ovarian cancer patients were integrated,and machine learning algo-rithms were applied to identify ERSRGs associated with prognosis.A risk score model was developed based on the selected key genes,and its predictive performance and robustness were assessed in independent training,validation,and overall cohorts.Sin-gle-cell transcriptomic data were further utilized to explore the expression patterns and potential regulatory mechanisms of these key genes within immune cells.Results:9 key ERSRGs(ERBB2,NHLRC1,CREB3L4,CALR3,MAPK13,OSBPL3,HSD11B2,SLC4A11,GJB1)were identified(P<0.05).The constructed model effectively stratified patients into high-and low-risk groups,demonstrating a 5-year AUC of 0.663 in the overall cohort.The high-risk group exhibited characteristics of T cell dysfunction and immune escape(P<0.05).Single-cell RNA sequencing analysis revealed specific expression of these genes in certain immune cell subtypes and tumor cells,suggesting their potential role in modulating the immune microenvironment.Conclusion:A novel prognostic model based on nine ERSRGs was successfully constructed and validated for ovarian cancer,demonstrating robust performance in predicting both prognosis and immunotherapy response.This model provides a theoretical foundation and a potential clinical tool for individualized treatment strategies,showing promising translational value.