Cancer Center, Nanfang Hospital, Southern Medical University, 510315, Guangzhou, China.
Cancer Center, Zhujiang Hospital, Southern Medical University, 510315, Guangzhou, China.
BMC Cancer. 2022 Jun 4;22(1):613. doi: 10.1186/s12885-022-09647-5.
A malignancy of the liver, hepatocellular carcinoma (HCC) is among the most common and second-leading causes of cancer-related deaths worldwide. A reliable prognosis model for guidance in choosing HCC therapies has yet to be established.
A consensus clustering approach was used to determine the number of immune clusters in the Cancer Genome Atlas and Liver Cancer-RIKEN, JP (LIRI_JP) datasets. The differentially expressed genes (DEGs) among these groups were identified based on RNA sequencing data. Then, to identify hub genes among signature genes, a co-expression network was constructed. The prognostic value and clinical characteristics of the immune clusters were also explored. Finally, the potential key genes for the immune clusters were determined.
After conducting survival and correlation analyses of the DEGs, three immune clusters (C1, C2, and C3) were identified. Patients in C2 showed the longest survival time with the greatest abundance of tumor microenvironment (TME) cell populations. MGene mutations in Ffibroblast growth factor-19 (FGF19) and catenin (cadherin-associated protein),β1(CTNNB1) were mostly observed in C2 and C3, respectively. The signature genes of C1, C2, and C3 were primarily enriched in 5, 23, and 26 pathways, respectively.
This study sought to construct an immune-stratification model for the prognosis of HCC by dividing the expression profiles of patients from public datasets into three clusters and discovering the unique molecular characteristics of each. This stratification model provides insights into the immune and clinical characteristics of HCC subtypes, which is beneficial for the prognosis of HCC.
肝癌是肝脏的一种恶性肿瘤，是全球癌症相关死亡的最常见原因之一，也是第二大原因。目前尚未建立用于指导选择 HCC 治疗方法的可靠预后模型。
使用共识聚类方法确定癌症基因组图谱 (Cancer Genome Atlas，TCGA) 和肝脏癌症-RIKEN，JP（LIRI_JP）数据集的免疫聚类数量。根据 RNA 测序数据确定这些组之间的差异表达基因 (differentially expressed genes，DEGs)。然后，为了在特征基因中识别枢纽基因，构建了一个共表达网络。还探讨了免疫聚类的预后价值和临床特征。最后，确定了免疫聚类的潜在关键基因。
对 DEGs 进行生存和相关性分析后，鉴定出三个免疫聚类 (C1、C2 和 C3)。C2 中的患者具有最长的生存时间和最多的肿瘤微环境 (tumor microenvironment，TME) 细胞群体。Ffibroblast growth factor-19 (FGF19) 和 catenin (cadherin-associated protein),β1(CTNNB1) 的 MGene 突变主要见于 C2 和 C3。C1、C2 和 C3 的特征基因分别主要富集于 5、23 和 26 条途径。
本研究通过将公共数据集的患者表达谱分为三个聚类，并发现每个聚类的独特分子特征，旨在构建 HCC 预后的免疫分层模型。该分层模型深入了解了 HCC 亚型的免疫和临床特征，有助于 HCC 的预后判断。
