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Background <p>Existing anti-tumor treatments often fail due to the tumor microenvironment’s role in fostering relapse and resistance. Natural killer (NK) cells are closely associated with tumor progression and treatment resistance. This study aimed to investigate NK cell characteristics in hepatocellular carcinoma (HCC) and develop a risk signature based on NK cell-related molecular features to predict HCC patient prognosis.</p> Methods <p>Single-cell RNA sequencing (scRNA-seq) data were sourced from the GEO database, while bulk RNA-seq and microarray data for HCC were obtained from the GEO and TCGA databases, respectively. NK cell clusters were identified using the Seurat R package. Univariate Cox regression analysis identified prognostic genes related to NK cells, and Lasso regression was used to develop a risk signature. A nomogram was constructed incorporating clinical features and the risk signature, consensus clustering assessed HCC heterogeneity.</p> Results <p>We distinguished five unique NK cell clusters in HCC, with three showing prognostic relevance. The resulting risk signature comprised 12 genes involved in ten pathways, including NRF1 and PI3K. This signature correlated significantly with stromal and immune scores and specific immune cells. Multivariate analysis confirmed the risk signature as an independent prognostic factor and indicated its potential in predicting immunotherapy outcomes. An integrated nomogram provided reliable HCC prognosis predictions.</p> Conclusion <p>NK cell-based risk signatures may serve as a potential tool to evaluate HCC prognosis, improving our understanding of the disease’s immune microenvironment and supporting the development of personalized immunotherapy strategies.</p>

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Bioinformatic characterization of a NK cell-based prognostic risk signature for hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Kaikai Zhao,
  • Youjiao Si,
  • Lin Ouyang

摘要

Background

Existing anti-tumor treatments often fail due to the tumor microenvironment’s role in fostering relapse and resistance. Natural killer (NK) cells are closely associated with tumor progression and treatment resistance. This study aimed to investigate NK cell characteristics in hepatocellular carcinoma (HCC) and develop a risk signature based on NK cell-related molecular features to predict HCC patient prognosis.

Methods

Single-cell RNA sequencing (scRNA-seq) data were sourced from the GEO database, while bulk RNA-seq and microarray data for HCC were obtained from the GEO and TCGA databases, respectively. NK cell clusters were identified using the Seurat R package. Univariate Cox regression analysis identified prognostic genes related to NK cells, and Lasso regression was used to develop a risk signature. A nomogram was constructed incorporating clinical features and the risk signature, consensus clustering assessed HCC heterogeneity.

Results

We distinguished five unique NK cell clusters in HCC, with three showing prognostic relevance. The resulting risk signature comprised 12 genes involved in ten pathways, including NRF1 and PI3K. This signature correlated significantly with stromal and immune scores and specific immune cells. Multivariate analysis confirmed the risk signature as an independent prognostic factor and indicated its potential in predicting immunotherapy outcomes. An integrated nomogram provided reliable HCC prognosis predictions.

Conclusion

NK cell-based risk signatures may serve as a potential tool to evaluate HCC prognosis, improving our understanding of the disease’s immune microenvironment and supporting the development of personalized immunotherapy strategies.