Evaluation and expression-level verification of the prognostic value of palmitoylation-related genes in ovarian cancer

Abstract

Ovarian cancer (OC) remains the most lethal malignancy within the spectrum of gynecological cancers globally. While protein S-palmitoylation has been extensively implicated in tumor progression, its specific functional contributions and molecular mechanisms in the context of OC pathogenesis remain to be fully elucidated. This article aims to explore the prognostic effect associated with palmitoylation in OC. In this study, palmitoylation-related genes (PRGs) were defined as genes encoding enzymes directly involved in the palmitoylation/depalmitoylation process, as well as genes whose functions, subcellular localization, or signaling are regulated by this modification. Based on this definition, PRGs comprising enzymes and regulated substrates, were identified from public transcriptomic databases. By intersecting ovarian cancer (OC)-associated and palmitoylation-linked differentially expressed genes (DEGs), candidate targets were pinpointed. A prognostic risk model was then constructed using LASSO and Cox regression analyses on the TCGA-OV cohort (N = 378) and validated in the GSE51088 cohort (N = 152). This model was integrated into a predictive nomogram and further characterized through pathway enrichment, immune infiltration, checkpoint analysis, drug screening, and mutation profiling. Finally, identified markers were validated via RT-qPCR in clinical samples. Through intersecting DEGs1 and DEGs2, we obtained 24 candidate biomarkers. Four PRGs (HSPG2, BRD4, RARRES1, and SCGB1D2) were identified to construct a prognostic risk model. The risk score, alongside ethnicity and tumor stage, served as an independent prognostic indicator, integrated into a robust nomogram. Mechanistically, high-risk cohorts were characterized by dysregulated ribosome and translation initiation pathways, altered infiltration of seven immune cell types, and significant variations in seven checkpoints (e.g., CTLA4, CD274). Additionally, the model predicted sensitivities for 131 drugs and captured a high TP53 mutation rate. RT-qPCR validation confirmed the upregulation of HSPG2, SCGB1D2, and BRD4, and the downregulation of RARRES1 in OC tissues, showing high consistency with bioinformatic predictions (P < 0.05). This study identified HSPG2, BRD4, RARRES1, and SCGB1D2, which served as prognostic markers reflecting the palmitoylation-related biological landscape in OC that could lay the foundation for innovative therapeutic strategies.