Multi-cohort validation study of a four-gene signature for risk stratification and treatment response prediction in hepatocellular carcinoma

肝细胞癌 基因签名 肿瘤科 医学 转录组 内科学 队列 基因 生物信息学 基因表达 计算生物学 癌症研究 生物 遗传学
作者
Cuicui Liu,Zhijun Xiao,Shenghong Wu,Zhen Yang,Guowen Ji,Jingjing Duan,Ting Zhou,Jinming Cao,Xiufeng Liu,Feng Xu
出处
期刊:Computers in Biology and Medicine [Elsevier]
卷期号:167: 107694-107694 被引量:3
标识
DOI:10.1016/j.compbiomed.2023.107694
摘要

The intricate molecular landscape of hepatocellular carcinoma (HCC) presents a significant challenge to achieving precise risk stratification through clinical genetic testing. At present, there is a paucity of robust gene signatures that could assist clinicians in making clinical decisions for patients with HCC. We obtained gene expression profiles of patients with HCC from 20 independent cohorts available in public databases. A gene signature was developed by employing two machine learning algorithms. In addition to validating the signature with high-throughput data in public cohorts, we external validated the signature in 64 HCC cases by RT-PCR method. We compared genomic, transcriptomic and proteomic features between different subgroups. We also compared our signature to 130 gene signatures that have already been published. We developed a novel four-gene signature, designated as HCC4, that demonstrates significant potential for the prediction of survival outcomes in more than 1300 patients with HCC. The HCC4 also has potential for predicting recurrence and tumor volume doubling time, assessing transcatheter arterial chemoembolization and immunotherapy responses, and non-invasive detection of HCC. The high HCC4 score group shows a higher frequency of mutations in genes TP53, RB1 and TSC1/2, as well as increased activity of cell-cycle, glycolysis and hypoxia signaling pathways, higher cancer stemness score, and lower lipid metabolism activity. In seven HCC cohorts, HCC4 exhibited a higher average C-index in predicting overall survival compared to the 130 signatures previously published. Drug screening indicated that patients with high HCC4 scores were more sensitive to agents targeting AURKA, TUBB, JMJD6 and KIFC1. Our findings demonstrated that HCC4 is a powerful tool for improving risk stratification and for identifying HCC patients who are most likely to benefit from TACE treatment, immunotherapy, and other experimental therapies.
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