Tissue-Based Metabolomic Profiling of Endometrial Cancer and Hyperplasia

Background: Endometrial cancer (EC) is the sixth most common cancer among women globally, with an estimated 420,000 new cases diagnosed annually. Methods: This study comprised patients with endometrial cancer (EC) (n = 17), hyperplasia (HY) (n = 17), and controls (CO) (n = 20). Tissue was collected from the endometrium of all 54 patients, including patients with HY, EC, and CO, who underwent total hysterectomy. EC and HY diagnoses were confirmed based on histological examination. Untargeted metabolomics profiling was conducted using LC-HRMS. The partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were used for univariate and multivariate statistical analysis. The fitness of the model (R2Y) and predictive ability (Q2) were used to create OPLS-DA models. ROC analysis was carried out, followed by network analysis using Ingenuity Pathway Analysis. Results: The top metabolites that can discriminate EC and HY from CO were identified. This revealed a decrease in the levels of the lipid species, specifically phosphatidic acid (PA) (PA (14:1/14:0), PA(10:0/17:0), PA(18:1-O(12,13)/12:0)), PG(a-13:0/a-13:0), ganglioside GA1 (d18:1/18:1), PS(14:1/14:0), TG(20:0/18:4/14:1), and CDP-DG(PGF2alpha/18:2), while the levels of 3-Dehydro-L-gulonate, Uridine diphosphate-N-acetylglucosamine, ganglioside GT2 (d18:1/14:0), gamma-glutamyl glutamic acid and oxidized glutathione were increased in cases of EC and HY as compared to CO. Bioinformatics analysis, specifically using Ingenuity Pathway Analysis (IPA), revealed distinct pathway enrichments for EC and HY. For EC, the most highly scored pathways were associated with cell-to-cell signaling and interaction, skeletal and muscular system development and function, and small-molecule biochemistry. In contrast, HY cases showed the highest scoring pathways related to inflammatory disease, inflammatory response, and organismal injury and abnormalities. Conclusions: Developing sensitive biomarkers could improve diagnosis and guide treatment decisions, particularly in identifying which patients with HY may safely avoid hysterectomy and be managed with hormonal therapy.