Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response

Background: Programmed cell death (PCD) plays a critical role in regulating tumor metastasis; however, its underlying mechanisms in ovarian cancer (OV) remain unclear.
Methods: To identify molecular subtypes of OV, unsupervised clustering was performed based on the expression profiles of prognosis-related PCD genes from The Cancer Genome Atlas (TCGA-OV). Prognostic PCD-related genes were selected using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. A set of prognostic signature genes was determined according to the minimum Akaike Information Criterion (AIC). A Risk Score model for OV prognosis was constructed using gene expression data and multivariate Cox regression coefficients. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance and clinical utility of the Risk Score. The model’s robustness was validated using RNA-Seq data from the Gene Expression Omnibus (GSE32062) and the International Cancer Genome Consortium (ICGC-AU) datasets through Kaplan-Meier and ROC analyses. Pathway characteristics were assessed using gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA). Additionally, the Risk Score was evaluated in relation to chemotherapy sensitivity and immunotherapy responsiveness.
Results: A 9-gene Risk Score model was established through Cox and LASSO Cox regression analyses. Patients in the low-risk group exhibited significantly better prognosis and heightened immune activity. Conversely, the high-risk group showed increased PI3K pathway activation. Drug sensitivity analysis indicated that high-risk patients may benefit more from PI3K inhibitors, such as Taselisib and Pictilisib. Moreover, patients in the low-risk group demonstrated better responses to immunotherapy.
Conclusion: The 9-gene PCD-based Risk Score model shows strong potential as a prognostic tool for OV. It may guide personalized treatment by predicting prognosis, immune microenvironment activity, and responses to chemotherapy and immunotherapy. This study also lays the groundwork for further exploration of the PCD mechanisms in ovarian cancer.