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Objective <p>Early diagnosis and prognostic evaluation of hepatocellular carcinoma (HCC) remain significant challenges in clinical management. This study aims to identify prognostic biomarkers in HCC and to explore their implications in immune microenvironment infiltration.</p> Methods <p>In this study, we constructed a single-cell transcriptomic atlas of HCC, focusing on the expression profiles of T cell-related genes. Analytical approaches included cell-cell communication analysis and pseudotime trajectory analysis. To further predict and validate T cell-associated prognostic genes, we integrated transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Patients were stratified into high- and low-risk groups based on a prognostic model derived from these biomarkers. Immune infiltration levels in the tumor microenvironment were then evaluated across risk groups.</p> Results <p>A total of eight primary tumor samples and seven normal tissue samples were included as raw data in this study. Following stringent quality control and filtering, 53,477 cells were retained for downstream analysis. From these, we isolated 12,333 T cells, which were subjected to further clustering and annotation. The T cell subpopulations identified included 6314 natural killer T cells (NK T cells), 5199 effector memory CD4<sup>+</sup> T cells, and 820 central memory CD8<sup>+</sup> T cells. By integrating transcriptomic data from TCGA-LIHC and GEO datasets, we identified six prognostic biomarkers: LYZ, SPP1, EGR1, MARCO, FCN3, and PTTG1. A prognostic model was developed based on these biomarkers, enabling risk stratification into high- and low-risk groups. The model demonstrated robust predictive performance in estimating patient survival rates and immune cell infiltration levels within the tumor microenvironment.</p> Conclusion <p>This study identified and validated prognostic biomarkers in HCC that effectively predict patient survival rates and immune infiltration characteristics. These findings provide a potential foundation for precision medicine strategies in HCC, offering novel insights into the tumor-immune microenvironment and its clinical implications.</p>

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Prediction of prognostic biomarkers for hepatocellular carcinoma and immune microenvironment infiltration based on single-cell sequencing and RNA-Seq integration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月23日

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  • Yiyan Zhai,
  • Xuan Fan,
  • Peiying Lu,
  • Huiling Lei,
  • Jiangying Liu,
  • Meiling Guo,
  • Shuqi Liu,
  • Tong Zhao,
  • Peizhi Ye,
  • Jiarui Wu

摘要

Objective

Early diagnosis and prognostic evaluation of hepatocellular carcinoma (HCC) remain significant challenges in clinical management. This study aims to identify prognostic biomarkers in HCC and to explore their implications in immune microenvironment infiltration.

Methods

In this study, we constructed a single-cell transcriptomic atlas of HCC, focusing on the expression profiles of T cell-related genes. Analytical approaches included cell-cell communication analysis and pseudotime trajectory analysis. To further predict and validate T cell-associated prognostic genes, we integrated transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Patients were stratified into high- and low-risk groups based on a prognostic model derived from these biomarkers. Immune infiltration levels in the tumor microenvironment were then evaluated across risk groups.

Results

A total of eight primary tumor samples and seven normal tissue samples were included as raw data in this study. Following stringent quality control and filtering, 53,477 cells were retained for downstream analysis. From these, we isolated 12,333 T cells, which were subjected to further clustering and annotation. The T cell subpopulations identified included 6314 natural killer T cells (NK T cells), 5199 effector memory CD4+ T cells, and 820 central memory CD8+ T cells. By integrating transcriptomic data from TCGA-LIHC and GEO datasets, we identified six prognostic biomarkers: LYZ, SPP1, EGR1, MARCO, FCN3, and PTTG1. A prognostic model was developed based on these biomarkers, enabling risk stratification into high- and low-risk groups. The model demonstrated robust predictive performance in estimating patient survival rates and immune cell infiltration levels within the tumor microenvironment.

Conclusion

This study identified and validated prognostic biomarkers in HCC that effectively predict patient survival rates and immune infiltration characteristics. These findings provide a potential foundation for precision medicine strategies in HCC, offering novel insights into the tumor-immune microenvironment and its clinical implications.