Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Ovarian Cancer

Abstract

Background: Ovarian cancer (OV) is the most common gynecological cancer and is a major cause of cancer-related death among women worldwide. Immunotherapy has recently been of great interest, and it has been proven to be an effective treatment strategy. For this reason, the work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. Methods: The Gene ExpressionOmnibus (GEO) and the Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients, whose samples were divided into training, validation, and testing sets. The InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, were used to build an IRGP signature. The functional enrichment analysis was performed to investigate the biological activities of IRGPs. Then, the correlation between risk scores and clinicopathological parameters was further analyzed. Finally, we compared our signature and four existing signatures for OV. Results: We first identified a 17-IRGP signature significantly associated with survival based on LASSO regression model. The average area under the curve (AUC) values of the training, validation, and all