Li Shuo, Du Hongbo, Gan Da'nan, Li Xiaoke, Zao Xiaobin, Ye Yong'an
Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Institute of Liver Diseases, Beijing University of Chinese Medicine, Beijing, China.
Front Pharmacol. 2023 Jun 14;14:1200114. doi: 10.3389/fphar.2023.1200114. eCollection 2023.
Natural killer (NK) cells are a type of innate immune cell that recognize and eliminate tumor cells and infected cells, without prior sensitization or activation. Herein, we aimed to construct a predictive model based on NK cell-related genes for hepatocellular carcinoma (HCC) patients and assess the feasibility of utilizing this model for prognosis prediction. Single-cell RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database to identify marker genes of NK cells. Univariate Cox and lasso regression were performed to further establish a signature in the TCGA dataset. Subsequently, qPCR and immunohistochemistry (IHC) staining were employed to validate the expression levels of prognosis signature genes in HCC. The effectiveness of the model was further validated using two external cohorts from the GEO and ICGC datasets. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, and biological function were compared for different genetic subtypes and risk groups. Finally, molecular docking was performed to evaluate the binding affinity between the hub gene and chemotherapeutic drugs. A total of 161 HCC-related NK cell marker genes (NKMGs) were identified, 28 of which were significantly associated with overall survival in HCC patients. Based on differences in gene expression characteristics, HCC patients were classified into three subtypes. Ten prognosis genes (KLRB1, CD7, LDB2, FCER1G, PFN1, FYN, ACTG1, PABPC1, CALM1, and RPS8) were screened to develop a prognosis model. The model not only demonstrated excellent predictive performance on the training dataset, but also were successfully validated on two independent external datasets. The risk scores derived from the model were shown to be an independent prognosis factor for HCC and were correlated with pathological severity. Moreover, qPCR and IHC staining confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Finally, molecular docking revealed favorable binding energies between the hub gene ACTG1 and chemotherapeutic drugs. In this study, we developed a model for predicting the prognosis of HCC based on NK cells. The utilization of NKMGs as innovative biomarkers showed promise in the prognosis assessment of HCC.
自然杀伤（NK）细胞是一种先天性免疫细胞，可识别并清除肿瘤细胞和受感染细胞，无需预先致敏或激活。在此，我们旨在构建基于NK细胞相关基因的肝细胞癌（HCC）患者预测模型，并评估利用该模型进行预后预测的可行性。从基因表达综合数据库（GEO）获取单细胞RNA测序数据，以鉴定NK细胞的标记基因。进行单变量Cox和套索回归，以在TCGA数据集中进一步建立特征。随后，采用qPCR和免疫组织化学（IHC）染色来验证HCC中预后特征基因的表达水平。使用来自GEO和ICGC数据集的两个外部队列进一步验证该模型的有效性。比较不同基因亚型和风险组的临床特征、预后、肿瘤突变负担、免疫微环境和生物学功能。最后，进行分子对接以评估枢纽基因与化疗药物之间的结合亲和力。共鉴定出161个与HCC相关的NK细胞标记基因（NKMG），其中28个与HCC患者的总生存期显著相关。基于基因表达特征的差异，将HCC患者分为三个亚型。筛选出十个预后基因（KLRB1、CD7、LDB2、FCER1G、PFN1、FYN、ACTG1、PABPC1、CALM1和RPS8）以建立预后模型。该模型不仅在训练数据集上表现出出色的预测性能，而且在两个独立的外部数据集上也成功得到验证。该模型得出的风险评分被证明是HCC的独立预后因素，并与病理严重程度相关。此外，qPCR和IHC染色证实预后基因的表达通常与生物信息学分析结果一致。最后，分子对接显示枢纽基因ACTG1与化疗药物之间具有良好的结合能。在本研究中，我们开发了一种基于NK细胞的HCC预后预测模型。将NKMG用作创新生物标志物在HCC的预后评估中显示出前景。
