A Novel Immune-Related Prognostic Model Based on Chemosensitivity Predicts the Outcome of Ovarian Cancer

Abstract

Abstract Background: Ovarian cancer (OC) is the most lethal gynecological tumor. Chemotherapy resistance is a significant factor in the treatment and prognosis of ovarian cancer (OC). Compelling evidence indicates that changes in tumor immune microenvironmental are crucial to chemotherapy responses. Here, we aimed to construct an immune-related gene pairs classifier base on the chemosensitivity status of OC. Methods: Gene expression and clinical data collected from The Cancer Genome Atlas (TCGA) were to screen immune- and chemosensitivity-related genes. By univariate analysis and least absolute shrinkage and selection operator (LASSO) cox analysis, gene pairs associated with prognosis were identified from the intersection of the two parts of the genes. The prognostic signature was constructed by multivariate analysis. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were applied to evaluate the predictive validity of the risk model in independent Gene Expression Omnibus (GEO) datasets. A nomogram containing the risk signature and clinical characters was constructed. Besides, we appraised the forecasting capability of prognostic signature in clinicopathological features, immune landscape, gene mutation, the efficacy of immunotherapy, and drug sensitivity. The potential molecular mechanism of the signature was investigated by gene set enrichment analysis (GSEA). Results: The prognostic signature consisting of eleven chemosensitivity- and immune-related gene pairs was constructed in our study. The risk score, age, and chemosensitivity could be independent predictors for overall survival (OS). Nomogram and ROC curves demonstrate the importance of risk score and provide personal mortality risk prediction at different time points. The calibration plot shows the reliability of the nomogram. In addition, patients in the high-risk group had a lower IC50 for several common agents. In terms of the immune microenvironment, we found that B cells memory, T cells CD4 memory activated, and dendritic cells activated higher degree of infiltration in the low-risk group whereas it was the opposite for T cells CD4 naive, T cells CD4 memory resting, and M2 macrophages. Patients with high-risk scores had elevated expression of immune checkpoint genes, speculating that these patients may be more suitable for immunotherapy. Function analysis also confirmed our findings that the risk model may provide new targets for precision immunotherapy. Conclusions: Our study developed a chemosensitivity- and immune-related prognostic model to predict the prognosis of ovarian cancer patients, providing new sights in optimizing patient selection to improve future outcomes.