ArticleJournal of translational medicine2025
Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Mitochondrial-Related Gene Signature for Diagnosis and Immune Microenvironment Modulation in Lung Cancer and Venous Thromboembolism.World journal of oncology · 2026Article
- Age-Associated Four-Gene Prognostic Signature in Breast Cancer.Cancer reports (Hoboken, N.J.) · 2026Article
- Integrated pan-cancer analysis reveals a cancer-associated fibroblast oxidative stress response signature predicting immunotherapy response and prognosis.Apoptosis : an international journal on programmed cell death · 2026Article
- Red blood cell distribution width-to-albumin ratio as a novel predictor for mortality in breast cancer patients admitted to ICU: a retrospective analysis using MIMIC-IV 3.1.BMC medical informatics and decision making · 2026Article
- Integrated bioinformatics, machine learning, and experimental validation identify a four-gene diagnostic signature for cervical cancer associated with PI3K/AKT signaling.Scientific reports · 2026Article
- Integrative analysis of the meibum microbiome in dry eye disease: from dysbiosis and diagnostic biomarkers to immunomodulation by Bradyrhizobium-derived outer membrane vesicles.Journal of translational medicine · 2026Article
- Integrative Machine-Learning Molecular Subtyping and Risk Scores of Circadian Rhythm-Related Prognostic Signatures in Colorectal Cancer.Journal of gastrointestinal cancer · 2026Article
- Habitat imaging based on DCE-MRI for differentiating luminal and non-luminal subtypes of breast cancer: a two-center study.Frontiers in oncology · 2026Article
- Artificial intelligence-based integration of imaging, exposome, and multi-omics data for immune-related biomarker discovery and precision prevention in breast cancer.Frontiers in immunology · 2026Review
- Purine Metabolism-Related Pathogenic Genes in Trigeminal Neuralgia: A Multiomics Mendelian Randomization Study.Pain research & management · 2026Article
- Salidroside-Based Nanomedicines for Triple-Negative Breast Cancer: From Molecular Mechanisms to Clinical Translation.Breast cancer (Dove Medical Press) · 2026Review
- Willingness to Undergo Corrective Surgery After Breast-Conserving Surgery in Chinese Patients with Breast Deformities: A Single-Center Study.Breast cancer (Dove Medical Press) · 2026Article
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7 authors.
Funding
Abstract
backgroundBreast 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.
methodsWe 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.
resultsWe 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.
conclusionsKbhb 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.
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