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<p>Hepatocellular carcinoma (HCC) is characterized by high heterogeneity and a complex tumor microenvironment, which challenges conventional prognostic approaches. We developed a machine learning (ML)-based radiomic prognostic model that integrates single-cell and bulk RNA sequencing to improve risk stratification in HCC patients. scRNA-seq analysis was performed, excluding cells with &lt; 200 or &gt; 7500 detected genes or &gt; 20% mitochondrial genes. Dimensionality reduction And clustering identified 2317 co-heterogeneous genes across six cell types. A nine-gene TME signature, based on the intersection with TCGA data, was used to stratify survival risk. We screened radiomic features strongly correlated with TME scores and developed a support vector machine model. Feature importance was assessed by SHAP analysis, and model performance was validated using Cox regression and nomogram analysis. Patients with higher TME risk scores had significantly reduced survival (HR: 2.13, 95% CI: 1.42–3.21, <i>p</i> &lt; 0.001). The SVM model, based on four selected radiomic features, achieved high prognostic accuracy (area under the curve (AUC) = 0.85; C-index = 0.78), and its predictions aligned with nomogram survival estimates. By integrating molecular and imaging data, this radiomic model shows promising prognostic performance and may provide a non-invasive framework for HCC patient stratification.</p>

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Noninvasive Multi-Omics Radiomic Model Integrating scRNA-seq and Bulk RNA-seq for Hepatocellular Carcinoma Prognosis

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Yiping Gao,
  • Yifan Miao,
  • Hongfa Cai,
  • Shuangqing Chen

摘要

Hepatocellular carcinoma (HCC) is characterized by high heterogeneity and a complex tumor microenvironment, which challenges conventional prognostic approaches. We developed a machine learning (ML)-based radiomic prognostic model that integrates single-cell and bulk RNA sequencing to improve risk stratification in HCC patients. scRNA-seq analysis was performed, excluding cells with < 200 or > 7500 detected genes or > 20% mitochondrial genes. Dimensionality reduction And clustering identified 2317 co-heterogeneous genes across six cell types. A nine-gene TME signature, based on the intersection with TCGA data, was used to stratify survival risk. We screened radiomic features strongly correlated with TME scores and developed a support vector machine model. Feature importance was assessed by SHAP analysis, and model performance was validated using Cox regression and nomogram analysis. Patients with higher TME risk scores had significantly reduced survival (HR: 2.13, 95% CI: 1.42–3.21, p < 0.001). The SVM model, based on four selected radiomic features, achieved high prognostic accuracy (area under the curve (AUC) = 0.85; C-index = 0.78), and its predictions aligned with nomogram survival estimates. By integrating molecular and imaging data, this radiomic model shows promising prognostic performance and may provide a non-invasive framework for HCC patient stratification.