接收机工作特性
比例危险模型
单变量
肿瘤科
Lasso(编程语言)
肝细胞癌
内科学
生存分析
医学
多元统计
多元分析
癌症研究
生物信息学
癌症
生物标志物
计算生物学
计算机科学
机器学习
万维网
作者
Xinxin Zhang,Jia Yu,Juan Hu,Fang Tan,Juan Zhou,Xiaoyan Yang,Zhizhong Xie,Huifang Tang,Sen Dong,Xiaoyong Lei
标识
DOI:10.2174/1386207323666200914095616
摘要
Background: Hepatocellular carcinoma (HCC) is a common type of cancer with a high mortality rate and is usually detected at the middle or late stage, missing the optimal treatment period. The current study aims to identify potential long non-coding RNA (lncRNAs) biomarkers that contribute to the diagnosis and prognosis of HCC. Methods: The differentially expressed lncRNAs (DElncRNAs) in HCC patientsThe differentially expressed lncRNAs (DElncRNAs) in HCC patients were detected from the Cancer Genome Atlas (TCGA) dataset. LncRNAs signature was screened by LASSO regression, univariate, and multivariate Cox regression. The models for predicting diagnosis and prognosis were established, respectively. The prognostic model was evaluated by Kaplan-Meier survival curve receiver operating characteristic (ROC) curve and stratified analysis. The diagnostic model was validated by ROC. The lncRNAs signature was further demonstrated by functional enrichment analysis. were detected from the Cancer Genome Atlas (TCGA) dataset. LncRNAs signature was screened by LASSO regression, univariate and multivariate Cox regression. The models for predicting diagnosis and prognosis were established respectively. The prognostic model was evaluated by Kaplan-Meier survival curve receiver operating characteristic (ROC) curve and stratified analysis. The diagnostic model was validated by ROC. The lncRNAs signature was further demonstrated by functional enrichment analysis. Results: We found the 13-lncRNAs signature that had a good performance in predicting prognosis and could help to improve the value of diagnosis. In the training set, testing set, and entire cohort, the low-risk group had longer survival than the high-risk group (median OS: 3124 vs. 649 days, 2456 vs. 770 days and 3124 vs. 755 days). It performed well in 1-, 3-, and 5-year survival prediction. 13-lncRNAs-based risk score, age, and race were good predictors of prognosis. The AUC of diagnosis was 0.9487, 0.9265, and 0.9376, respectively. Meanwhile, the 13-lncRNAs were involved in important pathways, including the cell cycle and multiple metabolic pathways. Conclusion: In our study, the 13-lncRNAs signature may be a potential marker for the prognosis of HCC and improve the diagnosis.
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