{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhuang J"],"funding":["Henan Medical Science and Technology Research Project"],"pagination":["8495923"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8983176"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["2022"],"pubmed_abstract":["<h4>Background</h4>We planned to uncover the cancer stemness-related genes (SRGs) in prostate cancer (PCa) and its underlying mechanism in PCa metastasis.<h4>Methods</h4>We acquired the RNA-seq data of 406 patients with PCa from the TCGA database. Based on the mRNA stemness index (mRNAsi) calculated by one-class logistic regression (OCLR) algorithm, SRGs in PCa were extracted by WGCNA. Univariate and multivariate regression analyses were applied to uncover OS-associated SRGs. Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Pearson's correlation analysis were performed to discover the possible mechanism of PCa metastasis. The significantly correlated transcription factors of OS-associated SRGs were also identified by Pearson's correlation analysis. ChIP-seq was "],"journal":["Disease markers"],"pubmed_title":["Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Prostate Cancer."],"pmcid":["PMC8983176"],"funding_grant_id":["201602031","81702659","81501203","201940306","81772856","2017YQ054"],"pubmed_authors":["Meng T","Lin R","Zhang J","Yan P","Wei C","Fan M","Huang R","Li M","Xian S","Zhou Z","Yin H","Liu Y","Li Z","Huang Z","Wang S","Zhang X","Zhu X","Zhuang J"],"additional_accession":[]},"is_claimable":false,"name":"Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Prostate Cancer.","description":"<h4>Background</h4>We planned to uncover the cancer stemness-related genes (SRGs) in prostate cancer (PCa) and its underlying mechanism in PCa metastasis.<h4>Methods</h4>We acquired the RNA-seq data of 406 patients with PCa from the TCGA database. Based on the mRNA stemness index (mRNAsi) calculated by one-class logistic regression (OCLR) algorithm, SRGs in PCa were extracted by WGCNA. Univariate and multivariate regression analyses were applied to uncover OS-associated SRGs. Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Pearson's correlation analysis were performed to discover the possible mechanism of PCa metastasis. The significantly correlated transcription factors of OS-associated SRGs were also identified by Pearson's correlation analysis. ChIP-seq was ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-05T15:53:10.488Z","creation":"2025-04-05T15:53:10.488Z"},"accession":"S-EPMC8983176","cross_references":{"pubmed":["35392496"],"doi":["10.1155/2022/8495923"]}}