Department of Medical Oncology, Guangxi Medical University Cancer Hospital, Nanning, China.
Department of Oncology, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, China.
Front Immunol. 2021 Dec 1;12:723271. doi: 10.3389/fimmu.2021.723271. eCollection 2021.
Hepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.
We aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.
Using datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.
A total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan-Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.
We propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.
肝细胞癌（HCC）是一种高致死性疾病。目前缺乏有效的预后工具来指导 HCC 患者的临床决策。
我们旨在建立一个基于 HCC 差异表达基因（DEGs）的稳健预后模型。
使用来自癌症基因组图谱（TCGA）、基因表达综合数据库（GEO）和国际基因组联合会（ICGC）的数据集，鉴定 HCC 组织与相邻正常组织之间的 DEGs。我们使用 TCGA 数据集作为训练队列，应用最小绝对收缩和选择算子（LASSO）算法和多变量 Cox 回归分析来识别多基因表达特征。在构建模型时，评估了比例风险假设和协变量之间的多重共线性。使用 ICGC 队列进行验证。采用 Pearson 检验评估肿瘤突变负担与风险评分之间的相关性。通过单样本基因集富集分析，我们研究了特征基因在 HCC 微环境中的作用。
共鉴定出 274 个 DEGs，建立了一个由 6 个基因组成的预后模型。根据模型的风险评分，患者被分为低风险或高风险组。Kaplan-Meier 分析显示总生存率和无进展生存期存在显著差异。通过单因素和多因素 Cox 分析，与其他临床病理参数相比，该模型是独立的预后因素。时间依赖性接收器工作特征曲线分析显示对总生存率有较好的预测，但对无进展生存期的预测效果不佳。功能富集分析显示癌症相关途径富集，而两组间免疫浸润分析存在差异。风险评分与 PD-1、PD-L1、CTLA4 或肿瘤突变负担水平无相关性。
我们提出了一个由 6 个基因组成的表达特征，可以帮助确定 HCC 患者的预后。这些基因可能作为 HCC 的生物标志物，支持个体化疾病管理。
