Identification and validation of a sphingolipid metabolism-related prognostic signature for predicting prognosis and immune microenvironment-related characteristics in ovarian cancer.

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, Results: Patients stratified according to risk scores exhibited statistically significant differences in survival outcomes. The robustness and predictive accuracy of this signature were consistently validated in both internal and external cohorts. Comprehensive analysis of the immune microenvironment and immunotherapy response revealed that patients in the low-risk group were more likely to derive clinical benefit from immunotherapy. Moreover, drug sensitivity analysis indicated that the low-risk group was more responsive to olaparib, whereas the high-risk group showed increased sensitivity to Topotecan. Critically, functional experiments demonstrated that CERK knockout in this model significantly suppressed proliferation, migration, and invasion capacities in SKOV3 and ES-2 cell lines. Conclusions: We developed a sphingolipid metabolism-related prognostic signature (SRPS), which demonstrated robust performance in predicting survival outcomes and guiding personalized therapeutic strategies.