Abstract

Background Ovarian cancer (OV) is the leading cause of mortality among gynecological malignancies, often diagnosed at an advanced stage and prone to recurrence after treatment. In order to improve the prognosis, there is an urgent clinical need to identify novel strategies for early intervention and prognosis prediction. Sphingolipids are both important components of cell membranes and closely related to cell signaling. Key enzymes and intermediates of sphingolipid metabolism have critical roles in regulating biological processes such as proliferation and apoptosis of cancer cells, and some of the anticancer drugs targeting sphingolipid metabolism have already entered into clinical trials. However, the prognostic value of sphingolipid metabolism-related genes (SRGs) in OV remains unclear. This study aims to systematically evaluate the prognostic significance of SRGs in OV and construct a prognostic risk model to improve survival prediction. Methods In this study, we integrated transcriptomic profiles and corresponding clinical data of OV patients from the Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/) databases. Through univariate and multivariate Cox regression analyses, we identified five SRGs to construct a prognostic signature of OV. Based on the signature-derived risk scores, all samples were stratified into high- and low-risk groups. To further evaluate the signature’s clinical utility, we comprehensively assessed its associations with immune microenvironment, immunotherapy response and chemotherapy sensitivity. Finally, in vitro experiments were performed to validate the functional role of the key gene CERK in the model. Results Patients stratified according to risk scores exhibited statistically significant differences in survival outcomes.