Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis.

Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957–0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610–0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901–0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics. Synopsis: Endometrial cancer (EC) diagnostic suffers from a lack of non-invasive, specific and sensitive tools. Machine learning of high-performance serum metabolic fingerprints (SMFs) was used to identify a metabolic biomarker panel for differentiation diagnosis of EC vs. Non-EC. An SMFs database of 191 EC and 204 Non-EC subjects was built via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). A metabolic biomarker panel for differentiation diagnosis of EC was identified, with an AUC of 0.901–0.902 and an accuracy of 82.8–83.1%. The metabolite biomarker functions on EC cell behavior were evaluated in vitro (including proliferation, colony formation, migration, and apoptosis). Endometrial cancer (EC) diagnostic suffers from a lack of non-invasive, specific and sensitive tools. Machine learning of high-performance serum metabolic fingerprints (SMFs) was used to identify a metabolic biomarker panel for differentiation diagnosis of EC vs. Non-EC. [ABSTRACT FROM AUTHOR]