Wu Dapeng, Zhu Baiyang, Nie Zonglong, Kong Qingnuan, Zhu Wenjing
Department of Oncology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, 266071, China.
School of Clinical and Basic Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117, Shandong Province, China.
Biochem Genet. 2024 Oct 15. doi: 10.1007/s10528-024-10931-1.
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality in the world. Prognostic indicators such as clinicopathological characteristics and single molecular signature are far from satisfactory in clinical practice. More and more researches have suggested that polygenic prognostic features could predict the prognosis of cancer more precisely than single genes nowadays. In this study, we performed gene set enrichment analysis (GSEA) to identify the sets of TCGA hallmark genes. Univariate Cox regression analysis was used to select preliminary genes, and then multivariate Cox regression analysis was used to identify genes associated with overall survival (OS). We also used Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis to validate the prognostic gene signature. Lastly, qRT-PCR was used to evaluate the expression of these genes in clinical samples, and immunohistochemical staining was performed to confirm the signature. A 12-gene signature was finally built and the risk score was significantly associated with the survival of the patients. Subsequent validation by qRT-PCR and immunohistochemical staining in clinical specimens confirmed the value of the risk score in predicting the prognosis. We developed a 12-gene signature that could predict the prognosis of HCC patients. This signature is of high precision and would help identifying subgroups of HCC patients with high or low risk of unfavorable survival.
肝细胞癌(HCC)是全球癌症死亡的主要原因之一。在临床实践中,诸如临床病理特征和单一分子标志物等预后指标远不能令人满意。越来越多的研究表明,如今多基因预后特征比单基因能更精确地预测癌症预后。在本研究中,我们进行了基因集富集分析(GSEA)以鉴定TCGA特征基因集。采用单变量Cox回归分析来选择初步基因,然后使用多变量Cox回归分析来鉴定与总生存期(OS)相关的基因。我们还使用Kaplan-Meier分析和受试者工作特征(ROC)分析来验证预后基因特征。最后,使用qRT-PCR评估这些基因在临床样本中的表达,并进行免疫组化染色以确认该特征。最终构建了一个12基因特征,且风险评分与患者的生存显著相关。随后通过临床标本中的qRT-PCR和免疫组化染色进行验证,证实了风险评分在预测预后方面的价值。我们开发了一种可预测HCC患者预后的12基因特征。该特征具有很高的准确性,将有助于识别生存预后风险高或低的HCC患者亚组。