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Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients

Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) an...

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Autores principales: Bai, Zhenzi, Li, Hongyan, Li, Chenghua, Sheng, Chuanlun, Zhao, Xiaonan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7535691/
https://www.ncbi.nlm.nih.gov/pubmed/33019382
http://dx.doi.org/10.1097/MD.0000000000021503
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author Bai, Zhenzi
Li, Hongyan
Li, Chenghua
Sheng, Chuanlun
Zhao, Xiaonan
author_facet Bai, Zhenzi
Li, Hongyan
Li, Chenghua
Sheng, Chuanlun
Zhao, Xiaonan
author_sort Bai, Zhenzi
collection PubMed
description Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) and explore their underlying functions. One RNA-sequencing dataset (training set, n = 95) and one microarray dataset E-TABM-36 (validation set, n = 44) were collected. Least absolute shrinkage and selection operator analysis was performed to identify an lncRNA-mRNA prognosis signature. The OS difference of patients in the high-risk and low-risk risk groups was evaluated by Kaplan–Meier curve. Area under the receiver operating characteristic curve (AUC), Harrell concordance index (C-index) calculation, and multivariate analyses with clinical characteristics were used to determine the prognostic ability. Furthermore, a coexpression network was constructed to interpret the functions. Nine signature genes (3 lncRNAs and 6 mRNAs) were selected to generate the risk score model. Patients belonging to the high-risk group showed a significantly shorter survival than those of the low-risk group. The prediction accuracy of the risk score for 5-year OS was 0.936 and 0.905 for the training set and validation set, respectively. Also, this risk score was independent of various clinical variables for the prognosis prediction. Incorporation of the risk score remarkably increased the predictive power of the routine clinical prognostic factors (vascular invasion status, tumor recurrence status) (AUC = 0.942 vs 0.628; C-index = 0.7997 vs 0.6908). Furthermore, LncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) and long intergenic non-protein coding RNA 342 (LINC00342) were predicted to exert tumor suppression effects by regulating homeobox D1 (HOXD1) and secreted frizzled related protein 5 (SFRP5), respectively; while lncRNA rhophilin Rho GTPase binding protein 1 antisense RNA 1 (RHPN1-AS1) may possess carcinogenic potential by promoting the transcription of chromobox 2 (CBX2), cell division cycle 20 (CDC20), matrix metallopeptidase 12 (MMP12), stratifin (SFN), tripartite motif containing 16 (TRIM16), and uroplakin 3A (UPK3A). These mRNAs may be associated with cell proliferation or apoptosis related pathways. This study may provide a novel, effective prognostic biomarker, and some therapeutic targets for HBV-HCC patients.
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spelling pubmed-75356912020-10-14 Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients Bai, Zhenzi Li, Hongyan Li, Chenghua Sheng, Chuanlun Zhao, Xiaonan Medicine (Baltimore) 5700 Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) and explore their underlying functions. One RNA-sequencing dataset (training set, n = 95) and one microarray dataset E-TABM-36 (validation set, n = 44) were collected. Least absolute shrinkage and selection operator analysis was performed to identify an lncRNA-mRNA prognosis signature. The OS difference of patients in the high-risk and low-risk risk groups was evaluated by Kaplan–Meier curve. Area under the receiver operating characteristic curve (AUC), Harrell concordance index (C-index) calculation, and multivariate analyses with clinical characteristics were used to determine the prognostic ability. Furthermore, a coexpression network was constructed to interpret the functions. Nine signature genes (3 lncRNAs and 6 mRNAs) were selected to generate the risk score model. Patients belonging to the high-risk group showed a significantly shorter survival than those of the low-risk group. The prediction accuracy of the risk score for 5-year OS was 0.936 and 0.905 for the training set and validation set, respectively. Also, this risk score was independent of various clinical variables for the prognosis prediction. Incorporation of the risk score remarkably increased the predictive power of the routine clinical prognostic factors (vascular invasion status, tumor recurrence status) (AUC = 0.942 vs 0.628; C-index = 0.7997 vs 0.6908). Furthermore, LncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) and long intergenic non-protein coding RNA 342 (LINC00342) were predicted to exert tumor suppression effects by regulating homeobox D1 (HOXD1) and secreted frizzled related protein 5 (SFRP5), respectively; while lncRNA rhophilin Rho GTPase binding protein 1 antisense RNA 1 (RHPN1-AS1) may possess carcinogenic potential by promoting the transcription of chromobox 2 (CBX2), cell division cycle 20 (CDC20), matrix metallopeptidase 12 (MMP12), stratifin (SFN), tripartite motif containing 16 (TRIM16), and uroplakin 3A (UPK3A). These mRNAs may be associated with cell proliferation or apoptosis related pathways. This study may provide a novel, effective prognostic biomarker, and some therapeutic targets for HBV-HCC patients. Lippincott Williams & Wilkins 2020-10-02 /pmc/articles/PMC7535691/ /pubmed/33019382 http://dx.doi.org/10.1097/MD.0000000000021503 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 5700
Bai, Zhenzi
Li, Hongyan
Li, Chenghua
Sheng, Chuanlun
Zhao, Xiaonan
Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients
title Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients
title_full Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients
title_fullStr Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients
title_full_unstemmed Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients
title_short Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients
title_sort integrated analysis identifies a long non-coding rnas-messenger rnas signature for prediction of prognosis in hepatitis b virus-hepatocellular carcinoma patients
topic 5700
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7535691/
https://www.ncbi.nlm.nih.gov/pubmed/33019382
http://dx.doi.org/10.1097/MD.0000000000021503
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