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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月11日

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  • Quan Yuan,
  • Yupeng Sha,
  • Rongjie Ye,
  • Hao Yu,
  • Lin Ni,
  • Jiguang Han,
  • Lin Deng

摘要

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.