{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["45"],"submitter":["Shen HY"],"pubmed_abstract":["<h4>Background</h4>Tumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has not been fully elucidated.<h4>Methods</h4>A series of bioinformatic analyses and machine learning strategies were performed to construct APM-related gene signatures to guide personalized treatment for BRCA patients. A single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF) algorithm was used to divide the cohort into differe"],"journal":["Neoplasia (New York, N.Y.)"],"pagination":["100942"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10587768"],"repository":["biostudies-literature"],"pubmed_title":["Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients."],"pmcid":["PMC10587768"],"pubmed_authors":["Fang Z","Zhu Z","Liang MX","Zhang J","Xu HP","Tang JH","Chen WQ","Xu JL","Xu D","Shen HY"],"additional_accession":[]},"is_claimable":false,"name":"Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients.","description":"<h4>Background</h4>Tumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has not been fully elucidated.<h4>Methods</h4>A series of bioinformatic analyses and machine learning strategies were performed to construct APM-related gene signatures to guide personalized treatment for BRCA patients. A single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF) algorithm was used to divide the cohort into differe","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Nov","modification":"2026-05-28T11:20:27.499Z","creation":"2025-04-06T02:05:55.295Z"},"accession":"S-EPMC10587768","cross_references":{"pubmed":["37839160"],"doi":["10.1016/j.neo.2023.100942"]}}