{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhu H"],"funding":["Nanjing Drum Tower Hospital,China","National Natural Science Foundation of China"],"pagination":["145"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12909457"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["26(1)"],"pubmed_abstract":["Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease, with unknown pathogenesis and no effective treatment. Identifying the key molecular of IPF in underlying mechanisms is critical for developing targeted therapies. Differentially expressed genes (DEGs) were identified based on GSE53845 data from the Gene Expression Omnibus (GEO) database and the Limma R package, followed by gene set enrichment analysis (GSEA). The key module genes selected by Weighted Gene Co-expression Network Analysis (WGCNA) were integrated with the DEGs. The hub genes were screened using three machine-learning algorithms, with further performance validated through Receiver Operating Characteristic (ROC) curves and nomogram models. In addition, validation was performed using external validation "],"journal":["Clinical and experimental medicine"],"pubmed_title":["Identification and validation of the protective gene BCAT2 related to amino acid metabolism in idiopathic pulmonary fibrosis."],"pmcid":["PMC12909457"],"funding_grant_id":["2022-LCYJ-MS-11","82570105, 82070064, 81670059 and 81200049"],"pubmed_authors":["Cao M","Zhang J","Chen M","Huang X","Zhu H","Li Q","Jiang C","Hou S","Cui S","Ye X"],"additional_accession":[]},"is_claimable":false,"name":"Identification and validation of the protective gene BCAT2 related to amino acid metabolism in idiopathic pulmonary fibrosis.","description":"Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease, with unknown pathogenesis and no effective treatment. Identifying the key molecular of IPF in underlying mechanisms is critical for developing targeted therapies. Differentially expressed genes (DEGs) were identified based on GSE53845 data from the Gene Expression Omnibus (GEO) database and the Limma R package, followed by gene set enrichment analysis (GSEA). The key module genes selected by Weighted Gene Co-expression Network Analysis (WGCNA) were integrated with the DEGs. The hub genes were screened using three machine-learning algorithms, with further performance validated through Receiver Operating Characteristic (ROC) curves and nomogram models. In addition, validation was performed using external validation ","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-16T07:11:59.929Z","creation":"2026-07-09T10:43:17.036Z"},"accession":"S-EPMC12909457","cross_references":{"pubmed":["41665823"],"doi":["10.1007/s10238-026-02053-2"]}}