Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer.

Ovarian cancer is the most lethal heterogeneous disease among gynecological tumors with a poor prognosis. Necroptosis, the most studied way of death in recent years, is different from apoptosis and pyroptosis. It is a kind of regulated programmed cell death and has been shown to be closely related to a variety of tumors. However, the expression and prognosis of necroptosis-related genes in ovarian cancer are still unclear. Our study therefore firstly identified the expression profiles of necroptosis-related genes in normal and ovarian cancer tissues. Next, based on differentially expressed necroptosis-related genes, we clustered ovarian cancer patients into two subtypes and performed survival analysis. Subsequently, we constructed a risk model consisting of 5 genes by LASSO regression analysis based on the differentially expressed genes in the two subtypes, and confirmed the strong prognostic ability of the model and its potential as an independent risk factor survival analysis and independent risk factor analysis. Based on this risk model, patients were divided into high and low risk groups. By exploring differentially expressed genes, enrichment functions and tumor immune microenvironment in patients in high and low risk groups, the results showed that patients in the low risk group were significantly enriched in immune signaling pathways. Besides, immune cells content, immune function activity was significantly better than the high-risk group. Eventually, we also investigated the sensitivity of patients with different risk groups to ICB immunotherapy and chemotherapy drugs. In conclusion, the risk model could effectively predict the survival and prognosis of patients, and explore the tumor microenvironment status of ovarian cancer patients to a certain extent, and provide promising and novel molecular markers for clinical diagnosis, individualized treatment and immunotherapy of patients.