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<p>Hepatocellular carcinoma (HCC) exhibits profound cellular heterogeneity, the understanding of which is critical for improving prognosis and therapy. Using single-cell RNA sequencing of 32,247 cells from human HCC samples, we characterized the tumor ecosystem and identified five malignant hepatocyte subpopulations with distinct molecular profiles and stage-specific enrichment. Among these, the S100A6⁺ C1 and S100A9⁺ C4 subpopulations were predominantly associated with advanced tumors and actively remodeled the tumor microenvironment through enhanced signaling pathways such as MDK and MIF. We further identified PGAM2 as a key transcriptional regulator in early-stage tumors, whose activity correlated with sialylation—a process linked to immune evasion. Based on these findings, we developed a prognostic model integrating PGAM2 and sialylation-related genes, which robustly stratified patients into high- and low-risk groups with significantly different survival outcomes, immune contextures, and predicted therapeutic responses. Functional experiments validated AGRN, a component of the signature, as a functional driver of HCC proliferation and invasion. Collectively, our results decode the cellular and molecular heterogeneity of HCC, provide a clinically relevant prognostic tool, and highlight potential targets for further investigation.</p><p></p>

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Machine learning-driven comprehensive profiling of tumor heterogeneity and sialylation in hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • Kaiqiang Tang,
  • Lu Han,
  • Junlin Li,
  • Kang Li

摘要

Hepatocellular carcinoma (HCC) exhibits profound cellular heterogeneity, the understanding of which is critical for improving prognosis and therapy. Using single-cell RNA sequencing of 32,247 cells from human HCC samples, we characterized the tumor ecosystem and identified five malignant hepatocyte subpopulations with distinct molecular profiles and stage-specific enrichment. Among these, the S100A6⁺ C1 and S100A9⁺ C4 subpopulations were predominantly associated with advanced tumors and actively remodeled the tumor microenvironment through enhanced signaling pathways such as MDK and MIF. We further identified PGAM2 as a key transcriptional regulator in early-stage tumors, whose activity correlated with sialylation—a process linked to immune evasion. Based on these findings, we developed a prognostic model integrating PGAM2 and sialylation-related genes, which robustly stratified patients into high- and low-risk groups with significantly different survival outcomes, immune contextures, and predicted therapeutic responses. Functional experiments validated AGRN, a component of the signature, as a functional driver of HCC proliferation and invasion. Collectively, our results decode the cellular and molecular heterogeneity of HCC, provide a clinically relevant prognostic tool, and highlight potential targets for further investigation.