A risk model based on the tumor microenvironment to predict survival and immunotherapy efficacy for ovarian cancer

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, develop a prognostic risk model, and explore its relevance to the efficacy of immunotherapy.

A risk model was 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 were then conducted through the model's risk score pattern, which was established depending on the TCGA training cohort and verified based on the internal TCGA cohort and external "Gene Expression Omnibus" (GEO) datasets. Furthermore, the immune-related characteristics and prognostic values of the risk model were evaluated.

The prognostic risk model for ovarian cancer demonstrated excellent performance in predicting survival rates, as validated in both the TCGA and GEO databases. The model showed significant associations with 17 functional immune cells, 17 immune checkpoints, PD-1, and several immune pathways, suggesting its potential to enhance the efficacy of immunotherapy in OC.

The risk model developed in this study has the potential to serve as a prognostic marker for OC, enabling the development of personalized immunotherapy protocols and providing a theoretical basis for novel combinations of immunotherapeutic approaches.