Research Paper Volume 16, Issue 7 pp 6550—6565

Single-cell data revealed exhaustion of characteristic NK cell subpopulations and T cell subpopulations in hepatocellular carcinoma

Zhongfeng Cui1, , Hongzhi Li2, , Chunli Liu3, , Juan Wang3, , Chunguang Chen1, , Shanlei Hu3, , Xiaoli Zhao3, , Guangming Li3, ,

  • 1 Department of Clinical Laboratory, Henan Provincial Infectious Disease Hospital, Zhengzhou 450000, China
  • 2 Department of Tuberculosis, Henan Provincial Infectious Disease Hospital, Zhengzhou 450000, China
  • 3 Department of Infectious Diseases and Hepatology, Henan Provincial Infectious Disease Hospital, Zhengzhou 450000, China

Received: October 30, 2023       Accepted: March 13, 2024       Published: April 5, 2024
How to Cite

Copyright: © 2024 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: The treatment and prognosis of patients with advanced hepatocellular carcinoma (HCC) have been a major medical challenge. Unraveling the landscape of tumor immune infiltrating cells (TIICs) in the immune microenvironment of HCC is of great significance to probe the molecular mechanisms.

Methods: Based on single-cell data of HCC, the cell landscape was revealed from the perspective of TIICs. Special cell subpopulations were determined by the expression levels of marker genes. Differential expression analysis was conducted. The activity of each subpopulation was determined based on the highly expressed genes. CTLA4+ T-cell subpopulations affecting the prognosis of HCC were determined based on survival analysis. A single-cell regulatory network inference and clustering analysis was also performed to determine the transcription factor regulatory networks in the CTLA4+ T cell subpopulations.

Results: 10 cell types were identified and NK cells and T cells showed high abundance in tumor tissues. Two NK cells subpopulations were present, FGFBP2+ NK cells, B3GNT7+ NK cells. Four T cells subpopulations were present, LAG3+ T cells, CTLA4+ T cells, RCAN3+ T cells, and HPGDS+ Th2 cells. FGFBP2+ NK cells, and CTLA4+ T cells were the exhaustive subpopulation. High CTLA4+ T cells contributed to poor prognostic outcomes and promoted tumor progression. Finally, a network of transcription factors regulated by NR3C1, STAT1, and STAT3, which were activated, was present in CTLA4+ T cells.

Conclusion: CTLA4+ T cell subsets in HCC exhibited functional exhaustion characteristics that probably inhibited T cell function through a transcription factor network dominated by NR3C1, STAT1, and STAT3.


HCC: hepatocellular carcinoma; TIICs: tumor immune infiltrating cells; TME: tumor microenvironment; NK cells: natural killer cells; scRNA-seq: single-cell sequencing; GEO: Gene Expression Omnibus; HVGs: highly variable genes; PCA: principal component analysis; UMAP: Uniform Manifold Approximation and Projection; LIHC: liver cancer; UCSC: University of California, Santa Cruz; ssGSEA: single-sample gene set enrichment analysis; K-M: Kaplan-Meier; MSigDB: Molecular Signatures Database; SCENIC: Single-cell regulatory network inference and clustering.