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10.1186/s12967-025-07555-3 http://scihub22266oqcxt.onion/10.1186/s12967-025-07555-3 41382084!?!41382084 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41382084&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       J+Transl+Med 2025 ; ? (?): ? Nephropedia Template TP {{tp|p=41382084|t=2025. Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer |pdf=|usr=}}{{41382084}} gab.com Text Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer JO: J Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=41382084 #FOAvip BACKGROUND: 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. METHODS: 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. RESULTS: We identified a tumor cell subset susceptible to Kbhb-related metabolic changes, significantly correlating with patient prognosis. SCGB2A2 overexpression reduced invasion, metastasis, and stemness. A prognostic score derived from Kbhb-affected cell markers accurately predicted patient outcomes and immunotherapy response. CONCLUSIONS: Kbhb influences breast cancer heterogeneity, with SCGB2A2 + neoplastic cells serving as valuable prognostic indicators. Targeting these cells may improve therapeutic outcomes. Our model also supports machine learning-guided drug discovery for metabolically vulnerable subpopulations. Twit Text FOAVip Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer ????J Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=41382084 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41382084 #FOAvip English Wikipedia <ref>{{Cite pmid|41382084}}</ref> Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer #MMPMID41382084 Yuan Q; Sha Y; Ye R; Yu H; Ni L; Han J; Deng L J Transl Med 2025[Dec]; ? (?): ? PMID41382084show ga BACKGROUND: 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. METHODS: 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. RESULTS: We identified a tumor cell subset susceptible to Kbhb-related metabolic changes, significantly correlating with patient prognosis. SCGB2A2 overexpression reduced invasion, metastasis, and stemness. A prognostic score derived from Kbhb-affected cell markers accurately predicted patient outcomes and immunotherapy response. CONCLUSIONS: Kbhb influences breast cancer heterogeneity, with SCGB2A2 + neoplastic cells serving as valuable prognostic indicators. Targeting these cells may improve therapeutic outcomes. Our model also supports machine learning-guided drug discovery for metabolically vulnerable subpopulations. ?Machine learning-based identification of kbhb-affected tumor cell subs(epmc).pdf DeepDyve Pubget Overpricing