Development of a multi-gene-based immune prognostic signature in ovarian Cancer

Abstract Background Various components of the immune system play a critical role in prognosis and treatment response ovarian cancer (OC). Immunotherapy has been recognized as hallmark but effect is contradictional. Reliable gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored develop an individualized prediction signature. Methods This study gene expression profiles patients with from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed genes transcription (TFs) were identified using collected ImmPort dataset TFs Cistoma database. Survival associated terms overall survival. signature was developed based on survival LASSO (Least absolute shrinkage selection operator) Cox regression analysis. Further, we performed network analysis uncover potential regulators immune-related help computational biology. Results signature, weighted combination 21 genes, moderately AUC 0.746, 0.735, 0.749 1, 3, 5 year survival, respectively. Network uncovered genes. Intriguingly, reflected cells landscape infiltration some cell subtypes. Conclusions We first constructed clinical significance, which showed promising predictive value surveillance, OC patients.