Molecular Subtypes of Ovarian Cancer Based on Lipid Metabolism and Glycolysis Reveals Potential Therapeutic Targets

Background: Ovarian cancer (OC) is one of the most lethal gynecological malignant neoplasms. The aim of this study was to use high-throughput sequencing data to investigate the molecular and clinical characteristics of OC subtypes related to lipid metabolism and glycolysis, thus providing a theoretical basis for clinical decision-making. Methods: Molecular data and clinicopathological characteristics of OC patients were extracted from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), and the Gene Expression Omnibus (GEO). Following analysis of genes involved in lipid metabolism and glycolysis, OC was classified into subtypes by unsupervised clustering. The molecular features and clinical outcomes of these subtypes were then evaluated. Results: OC patients were divided into five subtypes based on the analysis of nine genes of interest. Amongst these, patients in subtype D had longer overall survival and more benign clinical features. Subtypes B and E had shorter overall- and progression-free survival, respectively. Both the B and E subtypes were closely related to lipid metabolism and to the glycolytic process. Subtype D was positively correlated with the infiltration of CD8(+) T cells, CD4(+) T cells, and macrophages, all of which play essential anti-tumor roles. Several risk models for selected subtypes were also constructed based on the expression of select genes. Conclusions: The present work revealed that irregular metabolism in OC tissues was an indicator of poor clinical outcome and altered homeostasis in cancer-related pathways. Moreover, aberrant gene expression signatures associated with lipid metabolism and glycolysis were also correlated with an immunosuppressive tumor microenvironment. Based on lipid metabolism and glycolysis, we have therefore identified several OC molecular subtypes that may prove useful for the development of potential therapeutic targets.