Uncovering the heterogeneity of NK cells on the prognosis of HCC by integrating bulk and single-cell RNA-seq data - Li, Jiashuo; Liu, Zhenyi; Zhang, Gongming; Yin, Xue; Yuan, Xiaoxue; Xie, Wen; Ding, Xiaoyan (Frontiers in Oncology, 2025)
BackgroundThe tumor microenvironment (TME) plays a critical role in the development, progression, and clinical outcomes of hepatocellular carcinoma (HCC). Despite the critical role of natural killer (NK) cells in tumor immunity, there is limited research on their status within the tumor microenvironment of HCC. In this study, single-cell RNA sequencing (scRNA-seq) analysis of HCC datasets was performed to identify potential biomarkers and investigate the involvement of natural killer (NK) cells in the TME.MethodsSingle-cell RNA sequencing (scRNA-seq) data were extracted from the GSE149614 dataset and processed for quality control using the “Seurat” package. HCC subtypes from the TCGA dataset were classified through consensus clustering based on differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was employed to construct co-expression networks. Furthermore, univariate and multivariate Cox regression analyses were conducted to identify variables linked to overall survival. The single-sample gene set enrichment analysis (ssGSEA) was used to analyze immune cells and the screened genes.ResultA total of 715 DEGs from GSE149614 and 864 DEGs from TCGA were identified, with 25 overlapping DEGs found between the two datasets. A prognostic risk score model based on two genes was then established. Significant differences in immune cell infiltration were observed between high-risk and low-risk groups. Immunohistochemistry showed that HRG expression was decreased in HCC compared to normal tissues, whereas TUBA1B expression was elevated in HCC.ConclusionOur study identified a two-gene prognostic signature based on NK cell markers and highlighted their role in the TME, which may offer novel insights in immunotherapy strategies. Additionally, we developed an accurate and reliable prognostic model, combining clinical factors to aid clinicians in decision-making.