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<p>Lactate accumulation is a central feature of tumor metabolic reprogramming, yet its spatial and cell-type-specific effects in cancer, such as lung adenocarcinoma (LUAD), remain poorly defined. We integrated single-cell transcriptomics, spatial transcriptomics, spatial metabolomics, and immunofluorescence with TCGA survival data and machine-learning models. High-lactate tumors exhibited increased epithelial and fibroblast abundances, whereas T/NK cells and monocytes/macrophages were enriched in low-lactate samples. Spatial metabolomics revealed cell-type–restricted lactate and pyruvate distributions, with endothelial cells showing minimal lactate accumulation. Endothelial subclusters in high-lactate tissues displayed angiogenic and stress-response signatures and were strongly associated with poor prognosis. Multiple machine-learning frameworks—including random forest, elastic-net regression, SVM, ANN, and decision-tree models—consistently identified endothelial and fibroblast programs as key determinants of high-lactate states and adverse clinical outcomes. Collectively, our multi-omics spatial profiling demonstrates that lactate reshapes the LUAD microenvironment by driving angiogenesis, immune suppression, and prognostic stratification, highlighting lactate-centered pathways as promising therapeutic targets.</p>

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Machine learning-enabled spatial multi-omics uncovers lactate-driven targets and tumor microenvironmental reprogramming in cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月30日

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  • Yingzheng Tan,
  • Wenliang Tan,
  • Yanchao Liang,
  • Yunzhu Long,
  • Shuanghua Chen,
  • Qihao Hu,
  • Yangjing Ou,
  • Jingli Fu,
  • Huan Chen,
  • Fangyuan Ren,
  • Jun Ye,
  • Qing Zhou,
  • Sheng Li,
  • Xiaojin He,
  • Qianqian Wang,
  • Yueming Shen,
  • Haiyuan Lu,
  • Daichao Wu,
  • Anbo Gao,
  • Xun Chen,
  • Yukun Li

摘要

Lactate accumulation is a central feature of tumor metabolic reprogramming, yet its spatial and cell-type-specific effects in cancer, such as lung adenocarcinoma (LUAD), remain poorly defined. We integrated single-cell transcriptomics, spatial transcriptomics, spatial metabolomics, and immunofluorescence with TCGA survival data and machine-learning models. High-lactate tumors exhibited increased epithelial and fibroblast abundances, whereas T/NK cells and monocytes/macrophages were enriched in low-lactate samples. Spatial metabolomics revealed cell-type–restricted lactate and pyruvate distributions, with endothelial cells showing minimal lactate accumulation. Endothelial subclusters in high-lactate tissues displayed angiogenic and stress-response signatures and were strongly associated with poor prognosis. Multiple machine-learning frameworks—including random forest, elastic-net regression, SVM, ANN, and decision-tree models—consistently identified endothelial and fibroblast programs as key determinants of high-lactate states and adverse clinical outcomes. Collectively, our multi-omics spatial profiling demonstrates that lactate reshapes the LUAD microenvironment by driving angiogenesis, immune suppression, and prognostic stratification, highlighting lactate-centered pathways as promising therapeutic targets.