Department of General Surgery, The First Affiliated Hospital, Dalian Medical University, Dalian, 116011, Liaoning, China.
Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital, Dalian Medical University, Dalian, 116011, Liaoning, China.
Sci Rep. 2021 May 21;11(1):10728. doi: 10.1038/s41598-021-89747-7.
Hepatocellular carcinoma (HCC) is one of the main causes of cancer deaths globally. Immunotherapy is becoming increasingly important in the cure of advanced HCC. Thus it is essential to identify biomarkers for treatment response and prognosis prediction. We searched publicly available databases and retrieved 465 samples of genes from The Cancer Genome Atlas (TCGA) database and 115 tumor samples from Gene Expression Omnibus (GEO). Meanwhile, we used the ImmPort database to determine the immune-related genes as well. Weighted gene correlation network analysis, Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to identify the key immune related genes (IRGs) which are closely related to prognosis. Gene set enrichment analysis (GSEA) was implemented to explore the difference of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway between Immune high- and low-risk score groups. Finally, we made a prognostic nomogram including Immune-Risk score and other clinicopathologic factors. A total of 318 genes from prognosis related modules were identified through weighted gene co-expression network analysis (WGCNA). 46 genes were strongly linked to prognosis after univariate Cox analysis. We constructed a seven genes prognostic signature which showed powerful prediction ability in both training cohort and testing cohort. 16 significant KEGG pathways were identified between high- and low- risk score groups using GSEA analysis. This study identified and verified seven immune-related prognostic biomarkers for the patients with HCC, which have potential value for immune modulatory and therapeutic targets.
肝细胞癌(HCC)是全球癌症死亡的主要原因之一。免疫疗法在治疗晚期 HCC 中的作用越来越重要。因此,识别治疗反应和预后预测的生物标志物至关重要。我们搜索了公共可用的数据库,并从癌症基因组图谱(TCGA)数据库中检索了 465 个基因样本和基因表达综合数据库(GEO)中的 115 个肿瘤样本。同时,我们还使用 ImmPort 数据库来确定免疫相关基因。使用加权基因相关网络分析、Cox 回归分析和最小绝对收缩和选择算子(LASSO)分析来识别与预后密切相关的关键免疫相关基因(IRGs)。实施基因集富集分析(GSEA)以探讨免疫高风险和低风险评分组之间京都基因与基因组百科全书(KEGG)途径的差异。最后,我们制作了一个包含免疫风险评分和其他临床病理因素的预后列线图。通过加权基因共表达网络分析(WGCNA)鉴定了与预后相关模块的 318 个基因。单因素 Cox 分析后,有 46 个基因与预后密切相关。我们构建了一个由七个基因组成的预后特征,该特征在训练队列和测试队列中均具有强大的预测能力。使用 GSEA 分析,在高风险和低风险评分组之间确定了 16 个显著的 KEGG 途径。本研究鉴定并验证了 HCC 患者的七个免疫相关预后生物标志物,它们具有免疫调节和治疗靶点的潜在价值。