Development of molecular subtype and prognostic model related to metabolism-related genes in high-grade serous ovarian cancer: A study based on ten cohorts

Background: High-grade serous ovarian cancer (HGSOC) is a subtype of ovarian cancer with poor survival. At present, there are no good prognostic markers to guide treatment. Tumor metabolism plays a vital role in HGSOC; however, current knowledge on this topic is incomplete. Method We downloaded ten ovarian cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases and identified 1525 platinum-treated HGSOC samples. Metabolic-related genes were identified in the Molecular Signatures Database, and consensus clustering was used to identify HGSOC metabolic subtypes. We used the least absolute shrinkage and the selection operator to construct a metabolic-related prognostic model. Finally, we analyzed the potential biological characteristics of high- and low-risk groups by calculating the degree of immune cell infiltration and enrichment analysis. Results We identified two metabolic subtypes (clusters 1 and 2) in the cohort of TCGA-OV. Cluster 2 was poor, primarily enriched in carbohydrate processes, and cluster 1 was enriched primarily in respiratory processes. We constructed a 16-metabolic gene-related model that successfully predicted the overall survival rate in TCGA-OV, GPL96-OV, GPL570-OV, and GPL6480-OV cohorts. The model was suitable for predicting progression-free survival. Patients at high risk were prone to infiltration with activated mast cells, while patients at low risk were prone to infiltration of T cell follicular helper cells. Pathway enrichment analysis showed significant DNA replication enrichment in the low-risk group. Conclusion We performed a comprehensive analysis of metabolic genes that will aid the study of the metabolic mechanisms of ovarian cancer. Identifying metabolic gene-related subtypes and generating a prognostic model will help predict the risk of HGSOC and identify appropriate treatments to prolong survival and improve quality of life in HGSOC patients.