Exploration of the prognostic value and mechanisms of hemoglobin metabolism-related genes in ovarian cancer based on transcriptome sequencing

Background: Ovarian cancer (OC) has a complex pathogenesis, and reliable prognostic tools remain limited. The contribution of heme metabolism-related genes (HMRGs) to disease progression and the tumor immune microenvironment is not fully understood. Materials and Methods: Public OC datasets and literature-derived HMRGs were integrated. Differential expression and Cox regression analyses were used to identify prognostic genes and construct a risk model. Patients were stratified into high-risk and low-risk groups, followed by analyses of immune infiltration, somatic mutations, pathway activity, drug sensitivity, and molecular docking. Results: First, five OC-associated prognostic genes (CCDC28A, CD163, DCAF10, MARK3, UCP2) were identified, and the constructed prognostic model showed good predictive efficacy for OC. Second, 22 immune cell types exhibited significant compositional differences between risk groups. Somatic mutation analysis revealed TP53 as the dominant mutation in both groups, with CD163 and MARK3 showing higher mutation frequencies in the HRG. Gene enrichment analysis indicated downregulation of pathways such as the hematopoietic cell lineage, suggesting potential impairment in local immune cell generation and maturation. Drug sensitivity analysis showed systematic chemotherapy response differences between risk groups, while molecular docking confirmed that proteins encoded by core genes such as CD163 serve as potential drug targets, with flunisolide displaying strong binding affinity to the CD163 protein (binding energy: -9.5 kcal/mol). The prognostic model exhibited robust predictive performance (AUC > 0.6). Conclusion: The five-gene HMRG signature may support prognostic stratification and help identify potential therapeutic targets in OC.