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:1
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
念念发布了新的文献求助10
1秒前
畅快的鱼发布了新的文献求助10
1秒前
搞怪藏今完成签到 ,获得积分10
2秒前
yu发布了新的文献求助10
2秒前
2秒前
qifa发布了新的文献求助10
2秒前
kingwhitewing完成签到,获得积分10
2秒前
3秒前
WTT发布了新的文献求助10
3秒前
仄兀完成签到,获得积分10
3秒前
四喜完成签到,获得积分10
4秒前
4秒前
5秒前
6秒前
Yenom完成签到 ,获得积分10
6秒前
7秒前
7秒前
SciGPT应助浩浩大人采纳,获得10
7秒前
迅速冰岚发布了新的文献求助10
7秒前
7秒前
WTT完成签到,获得积分20
8秒前
8秒前
苹果煎饼发布了新的文献求助10
8秒前
yan发布了新的文献求助10
8秒前
云肜发布了新的文献求助30
8秒前
Hello应助FatDanny采纳,获得10
9秒前
斯文败类应助娜行采纳,获得10
9秒前
庄小因完成签到,获得积分10
9秒前
热心市民小刘给热心市民小刘的求助进行了留言
9秒前
小钟完成签到,获得积分10
9秒前
徐慕源发布了新的文献求助10
9秒前
10秒前
深情安青应助任医生采纳,获得10
10秒前
10秒前
sherrinford完成签到,获得积分10
10秒前
科研通AI2S应助VDC采纳,获得10
11秒前
YAOYAO发布了新的文献求助10
11秒前
舒适豌豆完成签到,获得积分10
11秒前
Amber应助reck采纳,获得10
11秒前
Renhong完成签到,获得积分10
12秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527469
求助须知:如何正确求助?哪些是违规求助? 3107497
关于积分的说明 9285892
捐赠科研通 2805298
什么是DOI,文献DOI怎么找? 1539865
邀请新用户注册赠送积分活动 716714
科研通“疑难数据库(出版商)”最低求助积分说明 709678