A Risk Model Based on the Tumor Microenvironment to Predict Survival and Immunotherapy Efficacy for Ovarian Cancer

Abstract

Abstract (1) Background: Based on the interactions between immune components in the tumor microenvironment and ovarian cancer (OC) cells, immunotherapies have been demonstrated to be effective in dramatically increasing survival rates. This study aimed to identify landmark genes, construct a prognostic risk model, and explore its relevance to immunotherapy efficacy; (2) Methods: A risk model were built based on the immune- and stromal-related genes, which were extracted from the OC gene expression data of “The Cancer Genome Atlas” (TCGA) database. Survival analysis and receiver operating characteristic (ROC) analysis was then conducted through the model`s riskscore pattern, which was established depending on the TCGA training cohort and verified based on the internally TCGA cohort and externally “Gene Expression Omnibus” (GEO) datasets. Finally, the immune-related characteristics and prognostic values of this model were evaluated; (3) Results: The prognostic risk model of OC exhibited excellent performance in predicting the survival rates in the TCGA and GEO database. This model, significantly associated with 17 functional immune cells, 17 immune checkpoint, PD-1, several immune pathways, may improve immunotherapy efficacy of OC; (4) Conclusions: As a potential prognostic marker, the risk model may offer personalized immunotherapy protocols for OC and provide a theoretical foundation for new immunotherapy combinations.