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Methods: This single-center observational study included 62 patients with EC and 70 controls with benign uterine conditions who underwent surgery between June 2012 and February 2020. Preoperative serum levels of classic androgens, 11-oxyandrogens, glucocorticoids and mineralocorticoids were measured using liquid chromatography\u2013tandem mass spectrometry (LC-MS\/MS). Machine learning was used to assess their diagnostic and prognostic value alone and combined with clinical parameters and tumor biomarkers. Results: Patients with EC had significantly higher serum levels of classic androgens (androstenedione, testosterone), 11-oxyandrogens (11\u03b2-hydroxy-androstenedione, 11\u03b2-hydroxy-testosterone) and glucocorticoids (17\u03b1-hydroxy-progesterone, 11-deoxycortisol) compared to controls. While individual steroids had limited diagnostic value, a multivariate model including classic androgens, CA-125, HE4, BMI and parity achieved an AUC 0.87, 79.1% sensitivity and 74.7% specificity in distinguishing EC from benign uterine condition. This model outperformed our previously published model based on CA-125, HE4 and BMI (AUC: 0.81, p &lt; 0.0001). Prognostically, HE4 was the strongest marker for lymphovascular space invasion (LVSI) (AUC: 0.79) and deep myometrial invasion (MI) (AUC: 0.71). Among steroids, androstenedione was the most predictive of LVSI (AUC: 0.67), while 11\u03b2-hydroxy-testosterone was the strongest predictor of deep MI (AUC: 0.64). Conclusions: Patients with EC exhibit distinct steroid hormone profiles. While steroids alone offer modest diagnostic and prognostic value, integrating them into multivariate models improves diagnostic accuracy.<\/jats:p>","DOI":"10.3390\/cancers17101679","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T05:11:57Z","timestamp":1747372317000},"page":"1679","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Steroid Profiling and Machine Learning Reveal Androgens as Candidate Biomarkers for Endometrial Cancer Diagnosis: A Case-Control Study"],"prefix":"10.3390","volume":"17","author":[{"given":"Marija","family":"Gjorgoska","sequence":"first","affiliation":[{"name":"Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angela E.","family":"Taylor","sequence":"additional","affiliation":[{"name":"Institute of Metabolism and Systems Research, University of Birmingham, Birmingham B15 2TT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9698-2130","authenticated-orcid":false,"given":"\u0160pela","family":"Smrkolj","sequence":"additional","affiliation":[{"name":"Department of Gynecology and Obstetrics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia"},{"name":"Division of Gynecology and Obstetrics, University Medical Centre, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3453-4081","authenticated-orcid":false,"given":"Tea Lani\u0161nik","family":"Ri\u017ener","sequence":"additional","affiliation":[{"name":"Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. 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