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

Abstract

Background:Ovarian cancer(OC) is the most lethal carcinoma among all gynecological malignancies.Based on the continued understanding of interaction relationships between immune components in tumor microenvironment (TME) and OC cells,immunotherapies have demonstrated to be dramatically effective in increasing survival rates.This research aims to construct a risk model and identify landmark genes which are of vital importance to improve the prognosis of OC. Methods:The gene expression data of 379 OC patients were extracted from The Cancer Genome Atlas(TCGA) database,and immune and stromal related genes were analyzed by difference analysis and weighted correlation network analysis (WGCNA).Subsequently,univariate and multivariate Cox regression analyses were used to build an risk model for OC.Kaplan-Meier survival curves were then taken to depict survival difference between high- and low-risk subgroups.Meanwhile,receiver operating characteristic (ROC) curves and the nomograms were applied to assess the accuracy and validity of the model.Finally,differentiation of immune cells,immune checkpoint molecules,and biological functions between high- and low-risk categories were identified to predict immunotherapy efficacy for OC.Results:GIMAP7,HTRA4,CCL5,ICOS,CD40LG,CD3G,VSIG4,CD2,ANKRD22 were obtained to construct the prognosis risk model;high risk score was an prognosis factor of poor survival rate of OC patients.Further ROC curve and nomogram analyses showed that this model exhibited excellent performance in predicting the 1-,3-,and 5-year survival rate.We obtained 17 functional immune cells,17 immune checkpoint molecules,several immune-related reactions,and GO items which might change the prognosis of OC patients.Conclusions:The 9-genes prognostic model may play critical roles as potential prognostic markers and offer personalized immunotherapy protocols for OC and may provide a theoretical foundation for new immunotherapy combinations.