{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["23(1)"],"submitter":["Han X"],"pubmed_abstract":["<h4>Background</h4>Oral squamous cell carcinoma (OSCC) represents a highly malignant form of cancer characterized by molecular heterogeneity and unsatisfactory treatment outcomes, with approximately 50% of patients experiencing local recurrence and distant metastasis following therapy. Given that metastasis is the most critical determinant of OSCC prognosis, enhancing the precision of clinical interventions and identifying therapeutic targets are of paramount importance. In view of this, this study is the first to develop a machine-learning-based prognostic model integrating epithelial-mesenchymal transition (EMT), anoikis, and basement membrane remodeling genes.<h4>Methods</h4>We systematically evaluated 78 algorithm and parameter combinations to identify a robust prognostic model, strati"],"journal":["Journal of translational medicine"],"pagination":["1344"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12645686"],"repository":["biostudies-literature"],"pubmed_title":["Development of a cancer metastasis-associated risk model via multi-machine-learning algorithms for prognostic risk evaluation and clinical application in oral squamous cell carcinoma."],"pmcid":["PMC12645686"],"pubmed_authors":["Zhang G","Jiao M","Feng X","Wang D","Sun T","Han X","Yue M","Jiang S","Wang H","Dai Y","Jia J","Hou M","Yun R","An Y"],"additional_accession":[]},"is_claimable":false,"name":"Development of a cancer metastasis-associated risk model via multi-machine-learning algorithms for prognostic risk evaluation and clinical application in oral squamous cell carcinoma.","description":"<h4>Background</h4>Oral squamous cell carcinoma (OSCC) represents a highly malignant form of cancer characterized by molecular heterogeneity and unsatisfactory treatment outcomes, with approximately 50% of patients experiencing local recurrence and distant metastasis following therapy. Given that metastasis is the most critical determinant of OSCC prognosis, enhancing the precision of clinical interventions and identifying therapeutic targets are of paramount importance. In view of this, this study is the first to develop a machine-learning-based prognostic model integrating epithelial-mesenchymal transition (EMT), anoikis, and basement membrane remodeling genes.<h4>Methods</h4>We systematically evaluated 78 algorithm and parameter combinations to identify a robust prognostic model, strati","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T18:56:32.932Z","creation":"2026-05-20T03:13:59.82Z"},"accession":"S-EPMC12645686","cross_references":{"pubmed":["41286896"],"doi":["10.1186/s12967-025-07336-y"]}}