Ren Huili, Zheng Jianglin, Cheng Qi, Yang Xiaoyan, Fu Qin
Department of Pharmacology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Genet. 2022 Jul 25;13:900713. doi: 10.3389/fgene.2022.900713. eCollection 2022.
Hepatocellular carcinoma (HCC) is a common type of primary liver cancer and has a poor prognosis. In recent times, necroptosis has been reported to be involved in the progression of multiple cancers. However, the role of necroptosis in HCC prognosis remains elusive. The RNA-seq data and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes (DEGs) and prognosis-related genes were explored, and the nonnegative matrix factorization (NMF) clustering algorithm was applied to divide HCC patients into different subtypes. Based on the prognosis-related DEGs, univariate Cox and LASSO Cox regression analyses were used to construct a necroptosis-related prognostic model. The relationship between the prognostic model and immune cell infiltration, tumor mutational burden (TMB), and drug response were explored. In this study, 13 prognosis-related DEGs were confirmed from 18 DEGs and 24 prognostic-related genes. Based on the prognosis-related DEGs, patients in the TCGA cohort were clustered into three subtypes by the NMF algorithm, and patients in C3 had better survival. A necroptosis-related prognostic model was established according to LASSO analysis, and HCC patients in TCGA and ICGC were divided into high- and low-risk groups. Kaplan-Meier (K-M) survival analysis revealed that patients in the high-risk group had a shorter survival time compared to those in the low-risk group. Using univariate and multivariate Cox analyses, the prognostic model was identified as an independent prognostic factor and had better survival predictive ability in HCC patients compared with other clinical biomarkers. Furthermore, the results revealed that the high-risk patients had higher stromal, immune, and ESTIMATE scores; higher TP53 mutation rate; higher TMB; and lower tumor purities compared to those in the low-risk group. In addition, there were significant differences in predicting the drug response between the high- and low-risk groups. The protein and mRNA levels of these prognostic genes were upregulated in HCC tissues compared to normal liver tissues. We established a necroptosis-related prognostic signature that may provide guidance for individualized drug therapy in HCC patients; however, further experimentation is needed to validate our results.
肝细胞癌(HCC)是原发性肝癌的一种常见类型,预后较差。近年来,有报道称坏死性凋亡参与多种癌症的进展。然而,坏死性凋亡在HCC预后中的作用仍不明确。从癌症基因组图谱(TCGA)和国际癌症基因组联盟(ICGC)数据库下载了HCC患者的RNA测序数据和临床信息。探索差异表达基因(DEGs)和预后相关基因,并应用非负矩阵分解(NMF)聚类算法将HCC患者分为不同亚型。基于预后相关的DEGs,采用单因素Cox和LASSO Cox回归分析构建坏死性凋亡相关的预后模型。探讨了预后模型与免疫细胞浸润、肿瘤突变负荷(TMB)和药物反应之间的关系。在本研究中,从18个DEGs和24个预后相关基因中确认了13个预后相关的DEGs。基于预后相关的DEGs,通过NMF算法将TCGA队列中的患者聚类为三个亚型,C3组患者的生存率更高。根据LASSO分析建立了坏死性凋亡相关的预后模型,并将TCGA和ICGC中的HCC患者分为高风险组和低风险组。Kaplan-Meier(K-M)生存分析显示,高风险组患者的生存时间比低风险组患者短。通过单因素和多因素Cox分析,该预后模型被确定为独立的预后因素,与其他临床生物标志物相比,在HCC患者中具有更好的生存预测能力。此外,结果显示,与低风险组相比,高风险患者的基质、免疫和ESTIMATE评分更高;TP53突变率更高;TMB更高;肿瘤纯度更低。此外,高风险组和低风险组在预测药物反应方面存在显著差异。与正常肝组织相比,这些预后基因的蛋白质和mRNA水平在HCC组织中上调。我们建立了一种坏死性凋亡相关的预后特征,可为HCC患者的个体化药物治疗提供指导;然而,需要进一步的实验来验证我们的结果。