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Background <p>Hepatocellular carcinoma (HCC) exhibits high aggressiveness and substantial molecular heterogeneity, yet precise and individualized therapeutic strategies remain limited. Comprehensive multi-omics integration and machine learning–driven modeling hold promise for refining molecular subtyping and improving prognostic prediction in HCC.</p> Methods <p>We developed a computational framework integrating multi-omics datasets from HCC patients. Ten clustering algorithms were combined to perform integrative multi-omics clustering and identify molecular subtypes. Ten machine learning algorithms were subsequently applied to construct a consensus prognostic signature. The clinical relevance of subtypes and risk groups was assessed through survival analysis, immunotherapy response prediction, and tumor immune microenvironment profiling. Single-cell RNA sequencing and spatial transcriptomics data were incorporated to determine the cellular origins and spatial expression patterns of key genes.</p> Results <p>Integrative multi-omics analysis identified four prognostically distinct cancer subtypes (CS1–CS4), with CS4 exhibiting the most favorable clinical outcomes. Five key genes were selected to build a robust prognostic model. Patients in the low-risk group demonstrated significantly better survival, an enhanced response to immunotherapy, and a higher probability of exhibiting a “hot tumor” phenotype. Conversely, the high-risk group showed poorer prognosis and reduced immunotherapy benefit. Single-cell and spatial transcriptomics analyses revealed that the key genes are predominantly enriched in malignant hepatocytes and display spatial patterns suggestive of tumor regional heterogeneity.</p> Conclusions <p>This study provides a refined molecular classification of HCC through integrative multi-omics analysis and establishes a machine learning–driven prognostic model with potential utility in early prognosis prediction and immunotherapy stratification. The dual-dimensional single-cell and spatial transcriptomic analyses further illuminate the cellular and spatial features associated with key prognostic genes. Prospective clinical validation is required to confirm the model’s predictive performance. </p>

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Multi-omics integration and machine learning define robust molecular subtypes and prognostic signatures in hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月21日

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  • Xiangyu Wang,
  • Biaobiao Yan,
  • Jianhua Yang,
  • Zhen Cheng,
  • Wenchao Song,
  • Yinfeng Yang,
  • Jinghui Wang

摘要

Background

Hepatocellular carcinoma (HCC) exhibits high aggressiveness and substantial molecular heterogeneity, yet precise and individualized therapeutic strategies remain limited. Comprehensive multi-omics integration and machine learning–driven modeling hold promise for refining molecular subtyping and improving prognostic prediction in HCC.

Methods

We developed a computational framework integrating multi-omics datasets from HCC patients. Ten clustering algorithms were combined to perform integrative multi-omics clustering and identify molecular subtypes. Ten machine learning algorithms were subsequently applied to construct a consensus prognostic signature. The clinical relevance of subtypes and risk groups was assessed through survival analysis, immunotherapy response prediction, and tumor immune microenvironment profiling. Single-cell RNA sequencing and spatial transcriptomics data were incorporated to determine the cellular origins and spatial expression patterns of key genes.

Results

Integrative multi-omics analysis identified four prognostically distinct cancer subtypes (CS1–CS4), with CS4 exhibiting the most favorable clinical outcomes. Five key genes were selected to build a robust prognostic model. Patients in the low-risk group demonstrated significantly better survival, an enhanced response to immunotherapy, and a higher probability of exhibiting a “hot tumor” phenotype. Conversely, the high-risk group showed poorer prognosis and reduced immunotherapy benefit. Single-cell and spatial transcriptomics analyses revealed that the key genes are predominantly enriched in malignant hepatocytes and display spatial patterns suggestive of tumor regional heterogeneity.

Conclusions

This study provides a refined molecular classification of HCC through integrative multi-omics analysis and establishes a machine learning–driven prognostic model with potential utility in early prognosis prediction and immunotherapy stratification. The dual-dimensional single-cell and spatial transcriptomic analyses further illuminate the cellular and spatial features associated with key prognostic genes. Prospective clinical validation is required to confirm the model’s predictive performance.