Construction of an Immunity and Ferroptosis-Related Risk Score Model to Predict Ovarian Cancer Clinical Outcomes and Immune Microenvironment

Abstract

Background: Ovarian cancer (OV) is a severe and common gynecological disease. Ferroptosis can regulate the progression and invasion of tumors. The immune system is a decisive factor in cancer. The present study aimed to use gene expression data to establish an immunity and ferroptosis-related risk score model as a prognostic biomarker to predict clinical outcomes and the immune microenvironment of OV. Methods: Common gene expression data were searched from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Immunity-related genes and ferroptosis-related genes were searched and downloaded from the ImmPort and FerrDb databases, followed by the analysis of the overall survival of patients with OV and the identification of genes. Subsequently, the status of the infiltration of immune cells and the association between immune checkpoints and risk score were assessed. Results: A total of 10 prognostic genes (C5AR1, GZMB, IGF2R, ISG20, PPP3CA, STAT1, TRIM27, TSHR, RB1, and EGFR) were included in the immunity and ferroptosisrelated risk score model. The high-risk group had a higher infiltration of immune cells. The risk score, an independent prognostic feature of OV was negatively associated with each immune checkpoint. The risk score may thus help to predict the response to immunotherapy. Conclusions: The immunity and ferroptosis-related risk score model is an independent prognostic factor for OV. The established risk score may help to predict the response of patients to immunotherapy.