13-lncRNAs Signature to Improve Diagnostic and Prognostic Prediction of Hepatocellular Carcinoma
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Published:2021-05-03
Issue:5
Volume:24
Page:656-667
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ISSN:1386-2073
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Container-title:Combinatorial Chemistry & High Throughput Screening
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language:en
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Short-container-title:CCHTS
Author:
Zhang Xinxin1,
Yu Jia1,
Hu Juan1,
Tan Fang1,
Zhou Juan1,
Yang Xiaoyan1,
Xie Zhizhong1,
Tang Huifang2,
Dong Sen1,
Lei Xiaoyong1
Affiliation:
1. Institute of Pharmacy and Pharmacology, Hunan Province Cooperative Innovation Center for Molecular Target New Drug Study, Hunan Provincial Key Laboratory of Tumor Microenvironment Responsive Drug Research, University of South China, Hengyang, China
2. The First Affiliated Hospital of University of South China, Hengyang, China
Abstract
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.
Funder
Hunan Provincial Students' Training Program for Innovation and Entrepreneurshi
National Students' Training Program for Innovation and Entrepreneurship
The Science and Technology Development Project of Hengyang
Natural Science Foundation of Hunan Province
Publisher
Bentham Science Publishers Ltd.
Subject
Organic Chemistry,Computer Science Applications,Drug Discovery,General Medicine
Cited by
1 articles.
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