{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T23:21:00Z","timestamp":1785799260628,"version":"3.56.0"},"reference-count":67,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Endometrial carcinoma (EC) is one of the most common gynecologic malignancies, with increasing global morbidity and mortality rates. Curcumol, a sesquiterpenoid hemicrystalline compound, exhibits notable pharmacological effects, including anticancer, anti-inflammatory, and antiviral properties. This study aims to explore the molecular mechanisms through which curcumol exerts its effects in the treatment of EC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Network pharmacology, data mining and machine learning were used to integrate curcumol and EC targets. R and online databases were applied to screen core targets. The core targets were verified by molecular docking, molecular dynamics simulation, ceRNA network regulation, clinical sample staining, and immunoinfiltration analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Progesterone Receptor (PGR) and Ribosomal protein S6 kinase (RPS6KA1) were identified as two core targets in the cancer risk prognostic model. Survival analysis indicated that high expression of PGR and RPS6KA1 is associated with prolonged survival in patients with EC. The HPA validation confirmed the low expression of PGR and high expression of RPS6KA1 in EC tissues. Molecular docking and simulation confirmed strong binding affinities between curcumol and the PGR and RPS6KA1 targets. Myc-associated zinc finger protein (MAZ) was a regulator of both PGR and RPS6KA1. Additionally, KCNQ1OT1 and chr22-38_28785274\u201329006793.1 were found to jointly regulate PGR and RPS6KA1 through various miRNAs, contributing to the pathogenesis of EC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Through multi-omics analysis, we conclude that curcumol exerts its anticancer effects primarily through the core targets PGR and RPS6KA1 in the treatment of EC.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2025.1565959","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T05:28:02Z","timestamp":1751520482000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-omics analysis of the anti-cancer effects of curcumol in endometrial carcinoma"],"prefix":"10.3389","volume":"16","author":[{"given":"Jumin","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haozhen","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zean","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shukai","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youyi","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqiong","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"104967","DOI":"10.1016\/j.compbiomed.2021.104967","article-title":"De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations","volume":"139","author":"Arshia","year":"2021","journal-title":"Comput. 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