Identification of heme metabolism-related biomarkers in ovarian cancer and construction of a prognostic model via bulk RNA-Seq analysis

Background: Ovarian cancer (OC) remains a lethal gynecologic malignancy. While treatment advances have improved survival, drug resistance and recurrence persist. Heme metabolism dysregulation is implicated in OC, but its mechanisms are unclear. This study aimed to identify heme metabolism-related biomarkers in OC. Methods: Differentially expressed genes (DEGs) were intersected with heme metabolism-related genes (HMRGs) to obtain candidate genes. Univariate Cox regression, proportional hazards (PH) assumption tests, and least absolute shrinkage and selection operator (LASSO) regression were employed to select biomarkers. A prognostic model was constructed and validated. Pathway enrichment analyses were performed using gene set enrichment analysis (GSEA). Immune cell infiltration was assessed via single-sample gene set enrichment analysis (ssGSEA). Results: Integration of DEGs with HMRGs yielded 101 candidate genes. A total of 4 biomarkers (EPB42, SLC7A11, HMBS, and GLRX5) were identified via univariate Cox regression, PH assumption tests, and LASSO regression analysis. The prognostic model demonstrated reliable predictive efficacy in both training and validation sets. GSEA revealed that the pathways enriched in the down-regulated gene set were mainly the mitochondrial electron transport chain, mitochondrial oxidative phosphorylation system (OXPHOS), and the mitochondrial gene module. Immune infiltration analysis identified 3 differentially infiltrated cell types. Conclusion: Overall,EPB42, SLC7A11, HMBS, and GLRX5 were identified as biomarkers associated with heme metabolism in OC. This finding provides new evidence for the clinical diagnosis and treatment of OC.