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Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on NK cell marker genes to predict prognosis and immunotherapy response in hepatocellular carcinoma

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Abstract

Background

Prognostic modeling of NK cell marker genes in patients with hepatocellular carcinoma based on single cell sequencing and transcriptome data analysis.

Methods

Marker genes of NK cells were analyzed according to single cell sequencing data of hepatocellular carcinoma. Univariate Cox regression, lasso regression analysis, and multivariate Cox regression were performed to estimate the prognostic value of NK cell marker genes. TCGA, GEO and ICGC transcriptomic data were applied to build and validate the model. Patients were divided into high and low risk groups based on the median risk score. XCELL, timer, quantitative sequences, MCP counter, EPIC, CIBERSORT and CIBERSORT-abs were performed to explore the relationship between risk score and tumor microenvironment in hepatocellular carcinoma. Finally the sensitivity of the model to chemotherapeutic agents was predicted.

Results

Single-cell sequencing identified 207 marker genes for NK cells in hepatocellular carcinoma. Enrichment analysis suggested that NK cell marker genes were mainly involved in cellular immune function. Eight genes were selected for prognostic modeling after multifactorial COX regression analysis. The model was validated in GEO and ICGC data. Immune cell infiltration and function were higher in the low-risk group than in the high-risk group. The low-risk group was more suitable for ICI and PD-1 therapy. Half-maximal inhibitory concentrations of Sorafenib, Lapatinib, Dabrafenib, and Axitinib were significantly different on the two risk groups.

Conclusion

A new signature of hepatocyte NK cell marker genes possesses a powerful ability to predict prognosis and immunotherapeutic response in patients with hepatocellular carcinoma.

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Data availability

Data used in this study can be downloaded from TCGA (https://tcga-data.nci.nih.gov/tcga/), GEO (https://www.ncbi.nlm.nih.gov/geo/) and ICGC (https://dcc.icgc.org/).

Abbreviations

scRNA-seq:

Single-cell RNA sequencing

RNA-seq:

RNA sequencing

TCGA:

The Cancer Genome Atlas

GEO:

Gene Expression Omnibus

ICGC:

International Cancer Genome Consortium

GSVA:

Gene set variation analysis

TME:

Tumor microenvironment

DEGs:

Differentially expressed genes

ICB:

Immune checkpoint blockade

KEGG:

Kyoko Gene and Genome Encyclopedia

GO:

Gene ontology

TMB:

Tumor mutational burden

LASSO:

Least absolute shrinkage and selection operator

KEGG:

Kyoto Encyclopedia of Genes and Genomes

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Acknowledgements

None.

Funding

This study was funded by the National Key Research Programme of China (2022YFC2407304).

Author information

Author notes

  1. Dashuai Yang, Fangrui Zhao, Yang Su, and Yu Zhou have contributed equally to this work.

Authors and Affiliations

  1. Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, No. 99 Zhangzhidong Road, Wuchang District, Wuhan, 430060, China

    Dashuai Yang, Yu Zhou, Jie Shen, Bin Yu, Kailiang Zhao & Youming Ding

  2. Department of Oncology, Renmin Hospital of Wuhan University, No. 99 Zhangzhidong Road, Wuchang District, Wuhan, 430060, China

    Fangrui Zhao

  3. Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College in Huazhong University of Science and Technology, Wuhan, 430060, Hubei, China

    Yang Su

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  1. Dashuai Yang
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Contributions

Conceptualization: DY, FZ, YZ. Data curation: JS, YS. Formal analysis: DY, BY. Writing-original draft: DY, FZ, KZ. Writing—review and editing: YD, KZ.

Corresponding authors

Correspondence to Kailiang Zhao or Youming Ding.

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Yang, D., Zhao, F., Su, Y. et al. Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on NK cell marker genes to predict prognosis and immunotherapy response in hepatocellular carcinoma. J Cancer Res Clin Oncol 149, 10609–10621 (2023). https://doi.org/10.1007/s00432-023-04965-y

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