Metabolism-Related Programmed Cell Death: Unveiling Prognostic Biomarkers, Immune Checkpoints, and Therapeutic Strategies in Ovarian Cancer
Ovarian cancer (OC), the gynecologic malignancy with the poorest prognosis, is driven by metabolic reprogramming and dysregulated programmed cell death (PCD). However, their interplay and prognostic significance remain inadequately understood. Transcriptomic data from OC patients and healthy controls (TCGA and GTEx) were analyzed to identify differentially expressed genes (DEGs) intersecting with metabolism-related (MRGs) and PCD-related genes (PCDRGs). Prognostic genes were determined using univariate Cox regression, LASSO, multivariate Cox regression, and stepwise analyses. Consensus clustering revealed enrichment differences, while a risk model and nomogram were developed for outcome prediction. Associations between prognostic genes, immune microenvironment, and drug sensitivity were also assessed. A total of 166 candidate genes were identified, with PLA2G2D, LPCAT3, ARG1, PLA2G4A, and EXOSC3 emerging as significant prognostic markers. The risk model demonstrated marked survival differences, while the nomogram showed robust calibration for survival prediction. Differential immune cell infiltration was observed between risk groups. Additionally, Sinularin and Fulvestrant exhibited variable sensitivity, validated through molecular docking models. Metabolism-related PCD genes were identified as pivotal prognostic markers in OC, providing critical insights for prognostic evaluation and targeted therapy development.