Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma. (May 2023)

• Record Type: Journal Article
• Authors: Guo, Chengbin; Tang, Yuqin; Li, Qizhuo; Yang, Zhao; Guo, Yuqi; Chen, Chuanliang; Zhang, Yongqiang
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: Natural killer cell -- Immune heterogeneity -- Prognosis -- Therapeutic response -- Hepatocellular carcinoma; ILC1 Type 1 innate lymphoid cells -- NK Natural killer -- HCC Hepatocellular carcinoma -- scRNA-seq Single-cell RNA-sequencing -- NKGs NK cell marker genes -- ssGSEA Single sample gene set enrichment analysis -- TIME Tumor immune microenvironment -- TCR T cell receptor -- TIS Tumor inflammation signature -- IPS Immunophenoscore -- TACE Transcatheter arterial chemoembolization -- TNM Tumor-node-metastasis -- mAbs Monoclonal antibodies -- ICIs Immune checkpoint inhibitors -- TME Tumor microenvironment -- NKscore NK scoring system -- NK subtypes NK cell-related subtypes -- GEO Gene Expression Omnibus -- OS Overall survival -- PFS Progression-free survival -- DSS Disease-specific survival -- DFS Disease-free survival -- TMB Tumor mutation burden -- DEGs Differentially expressed genes -- AUC Area under the ROC curve; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106872
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients. Highlights: NKGs were obtained from scRNA-seq analysis for HCC patients. NKscore, a five-gene model, was established for predicting the prognosis and immunotherapy response of HCC patients. The first study integrating scRNA-seq and bulk-seq data to decipher tumor immune heterogeneity using prognostic NKGs.