{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yuan Q"],"funding":["the Haiyan Science Foundation","the China Primary Health Care Foundation","the Wu Jie Ping Medical Foundation","the Intra-Hospital Project of 900TH Hospital of The Joint Logistics Support Force","China Health & Medical Development Foundation"],"pagination":["71"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12802299"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["<h4>Background</h4>Breast cancer heterogeneity complicates prognosis and treatment. Metabolic reprogramming, particularly lysine beta-hydroxybutyrylation (Kbhb) driven by ketone bodies, influences the tumor microenvironment. However, the impact of Kbhb on specific breast cancer subpopulations remains unclear. This study aims to identify Kbhb-affected tumor cell subsets and evaluate their prognostic potential.<h4>Methods</h4>We integrated multi-omics data from TCGA, GEO, single-cell RNA sequencing, and spatial transcriptomics. After identifying breast cancer subpopulations influenced by Kbhb-associated genes, we validated the functional role of key genes via molecular experiments. A machine learning-based prognostic model was developed using 101 algorithm combinations.<h4>Results</h4>We ide"],"journal":["Journal of translational medicine"],"pubmed_title":["Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer."],"pmcid":["PMC12802299"],"funding_grant_id":["No. 2023QN08","No. cphcf-2023-017","No. JJZD2021-02","No. chmdf2025-xrkt04-03","No. 320.6750.18215"],"pubmed_authors":["Ni L","Yuan Q","Sha Y","Ye R","Deng L","Han J","Yu H"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer.","description":"<h4>Background</h4>Breast cancer heterogeneity complicates prognosis and treatment. Metabolic reprogramming, particularly lysine beta-hydroxybutyrylation (Kbhb) driven by ketone bodies, influences the tumor microenvironment. However, the impact of Kbhb on specific breast cancer subpopulations remains unclear. This study aims to identify Kbhb-affected tumor cell subsets and evaluate their prognostic potential.<h4>Methods</h4>We integrated multi-omics data from TCGA, GEO, single-cell RNA sequencing, and spatial transcriptomics. After identifying breast cancer subpopulations influenced by Kbhb-associated genes, we validated the functional role of key genes via molecular experiments. A machine learning-based prognostic model was developed using 101 algorithm combinations.<h4>Results</h4>We ide","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-11T04:58:16.197Z","creation":"2026-06-11T03:08:24.304Z"},"accession":"S-EPMC12802299","cross_references":{"pubmed":["41382084"],"doi":["10.1186/s12967-025-07555-3"]}}