倍增时间
肝细胞癌
比例危险模型
单变量
接收机工作特性
多元统计
多元分析
基因签名
医学
单变量分析
肿瘤科
基因
内科学
基因表达
生物
生存分析
统计
遗传学
细胞
数学
作者
Junyu Huo,Liqun Wu,Yunjin Zang
出处
期刊:Life Sciences
[Elsevier]
日期:2020-09-10
卷期号:260: 118396-118396
被引量:7
标识
DOI:10.1016/j.lfs.2020.118396
摘要
Tumour doubling time (TDT) is an indicator reflecting tumour growth rate, however, the prognostic genes associated with the TDT in hepatocellular carcinoma (HCC) have not been fully identified.We obtained mRNA expression profiles and tumour doubling time from GSE54236 and used the Pearson correlation test to identify tumour doubling time-related genes (TDTRGs). We extracted TDTRGs from The Cancer Genome Atlas (TCGA) and identified prognostic genes using univariate Cox regression analysis and Kaplan-Meier survival analysis. Lasso and multivariate Cox regression analysis assisted in constructing the signature and International Cancer Genome Consortium (ICGC) served as an external validation.We identified a total of 296 genes associated with tumour doubling time and developed a prognostic signature consisting of 9 genes. Patients were divided into high- and low-risk groups according to the uniform cutoff (0.85). Regardless of the clinical characteristics of the patients, the group at high risk exhibited obviously lower overall survival (OS) than did the group with low risk in both TCGA and ICGC cohorts. The prognostic model showed superior accuracy in both TCGA and ICGC cohorts, as confirmed by receiver operating characteristic (ROC) curve analysis. The univariate together with multivariate Cox regression analysis further suggested the ability of the signature to predict prognosis independently.A novel prognostic signature for HCC was developed and validated in the study, which may be beneficial to improve the treatment strategy of HCC.
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