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Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma
AIM: To construct a long non-coding RNA (lncRNA) signature for predicting hepatocellular carcinoma (HCC) prognosis with high efficiency. METHODS: Differentially expressed lncRNAs (DELs) between HCC specimens and peritumor liver specimens were identified using the edgeR package to analyze The Cancer...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092581/ https://www.ncbi.nlm.nih.gov/pubmed/30122881 http://dx.doi.org/10.3748/wjg.v24.i30.3426 |
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author | Zhao, Qiu-Jie Zhang, Jiao Xu, Lin Liu, Fang-Feng |
author_facet | Zhao, Qiu-Jie Zhang, Jiao Xu, Lin Liu, Fang-Feng |
author_sort | Zhao, Qiu-Jie |
collection | PubMed |
description | AIM: To construct a long non-coding RNA (lncRNA) signature for predicting hepatocellular carcinoma (HCC) prognosis with high efficiency. METHODS: Differentially expressed lncRNAs (DELs) between HCC specimens and peritumor liver specimens were identified using the edgeR package to analyze The Cancer Genome Atlas (TCGA) LIHC dataset. Univariate Cox proportional hazards regression was performed to obtain the DELs significantly associated with overall survival (OS) in a training set. These OS-related DELs were further analyzed using a stepwise multivariate Cox regression model. Those lncRNAs fitted in the multivariate Cox regression model and independently associated with overall survival were chosen to build a prognostic risk formula. The prognostic value of this formula was then validated in the test group and the entire cohort and further compared with two previously identified prognostic signatures for HCC. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to explore the potential biological functions of the lncRNAs in the signature. RESULTS: Based on lncRNA expression profiling of 370 HCC patients from the TCGA database, we constructed a 5-lncRNA signature (AC015908.3, AC091057.3, TMCC1-AS1, DCST1-AS1 and FOXD2-AS1) that was significantly associated with prognosis. HCC patients with high-risk scores based on the expression of the 5 lncRNAs had significantly shorter survival times compared to patients with low-risk scores in both the training and test groups. Multivariate Cox regression analysis demonstrated that the prognostic value of the 5 lncRNAs was independent of clinicopathological parameters. A comparison study involving two previously identified prognostic signatures for HCC demonstrated that this 5-lncRNA signature showed improved prognostic power compared with the other two signatures. Functional enrichment analysis indicated that the 5 lncRNAs were potentially involved in metabolic processes, fibrinolysis and complement activation. CONCLUSION: Our present study constructed a 5-lncRNA signature that improves survival prediction and can be used as a prognostic biomarker for HCC patients. |
format | Online Article Text |
id | pubmed-6092581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-60925812018-08-17 Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma Zhao, Qiu-Jie Zhang, Jiao Xu, Lin Liu, Fang-Feng World J Gastroenterol Basic Study AIM: To construct a long non-coding RNA (lncRNA) signature for predicting hepatocellular carcinoma (HCC) prognosis with high efficiency. METHODS: Differentially expressed lncRNAs (DELs) between HCC specimens and peritumor liver specimens were identified using the edgeR package to analyze The Cancer Genome Atlas (TCGA) LIHC dataset. Univariate Cox proportional hazards regression was performed to obtain the DELs significantly associated with overall survival (OS) in a training set. These OS-related DELs were further analyzed using a stepwise multivariate Cox regression model. Those lncRNAs fitted in the multivariate Cox regression model and independently associated with overall survival were chosen to build a prognostic risk formula. The prognostic value of this formula was then validated in the test group and the entire cohort and further compared with two previously identified prognostic signatures for HCC. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to explore the potential biological functions of the lncRNAs in the signature. RESULTS: Based on lncRNA expression profiling of 370 HCC patients from the TCGA database, we constructed a 5-lncRNA signature (AC015908.3, AC091057.3, TMCC1-AS1, DCST1-AS1 and FOXD2-AS1) that was significantly associated with prognosis. HCC patients with high-risk scores based on the expression of the 5 lncRNAs had significantly shorter survival times compared to patients with low-risk scores in both the training and test groups. Multivariate Cox regression analysis demonstrated that the prognostic value of the 5 lncRNAs was independent of clinicopathological parameters. A comparison study involving two previously identified prognostic signatures for HCC demonstrated that this 5-lncRNA signature showed improved prognostic power compared with the other two signatures. Functional enrichment analysis indicated that the 5 lncRNAs were potentially involved in metabolic processes, fibrinolysis and complement activation. CONCLUSION: Our present study constructed a 5-lncRNA signature that improves survival prediction and can be used as a prognostic biomarker for HCC patients. Baishideng Publishing Group Inc 2018-08-14 2018-08-14 /pmc/articles/PMC6092581/ /pubmed/30122881 http://dx.doi.org/10.3748/wjg.v24.i30.3426 Text en ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. |
spellingShingle | Basic Study Zhao, Qiu-Jie Zhang, Jiao Xu, Lin Liu, Fang-Feng Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma |
title | Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma |
title_full | Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma |
title_fullStr | Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma |
title_full_unstemmed | Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma |
title_short | Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma |
title_sort | identification of a five-long non-coding rna signature to improve the prognosis prediction for patients with hepatocellular carcinoma |
topic | Basic Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092581/ https://www.ncbi.nlm.nih.gov/pubmed/30122881 http://dx.doi.org/10.3748/wjg.v24.i30.3426 |
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