{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15"],"submitter":["Hou L"],"pubmed_abstract":["<h4>Purpose</h4>Regulation of inducers and transcription factor families influence epithelial-mesenchymal transition (EMT), a contributing factor to breast cancer invasion and progression.<h4>Methods</h4>Molecular subtypes were classified based on EMT-related mRNAs using ConsensusClusterPlus package. Differences in tumor immune microenvironment and prognosis were assessed among subtypes. Based on EMT genes, a gene signature for prognosis was built using TCGA training set by performing multivariate and univariate Cox regression analyses. Prediction accuracy of the signature was validated by receiver operating characteristic (ROC) curves and overall survival analysis on internal and external datasets. By conducting univariate and multivariate Cox regression analyses, the risk signature as an"],"journal":["International journal of general medicine"],"pagination":["3497-3515"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8979091"],"repository":["biostudies-literature"],"pubmed_title":["Epithelial-Mesenchymal Transition-Based Gene Signature and Distinct Molecular Subtypes for Predicting Clinical Outcomes in Breast Cancer."],"pmcid":["PMC8979091"],"pubmed_authors":["Hou L","Li X","Zhao S","Hou S","Yin L"],"additional_accession":[]},"is_claimable":false,"name":"Epithelial-Mesenchymal Transition-Based Gene Signature and Distinct Molecular Subtypes for Predicting Clinical Outcomes in Breast Cancer.","description":"<h4>Purpose</h4>Regulation of inducers and transcription factor families influence epithelial-mesenchymal transition (EMT), a contributing factor to breast cancer invasion and progression.<h4>Methods</h4>Molecular subtypes were classified based on EMT-related mRNAs using ConsensusClusterPlus package. Differences in tumor immune microenvironment and prognosis were assessed among subtypes. Based on EMT genes, a gene signature for prognosis was built using TCGA training set by performing multivariate and univariate Cox regression analyses. Prediction accuracy of the signature was validated by receiver operating characteristic (ROC) curves and overall survival analysis on internal and external datasets. By conducting univariate and multivariate Cox regression analyses, the risk signature as an","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T13:27:29.469Z","creation":"2025-04-04T13:27:29.469Z"},"accession":"S-EPMC8979091","cross_references":{"pubmed":["35386860"],"doi":["10.2147/IJGM.S343885"]}}