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MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer

BACKGROUND: Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients. METHODS: We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying...

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Autores principales: Liu, Yuejuan, Cui, Yuxia, Bai, Xuefeng, Feng, Chenchen, Li, Meng, Han, Xiaole, Ai, Bo, Zhang, Jian, Li, Xuecang, Han, Junwei, Zhu, Jiang, Jiang, Yong, Pan, Qi, Wang, Fan, Xu, Mingcong, Li, Chunquan, Wang, Qiuyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756031/
https://www.ncbi.nlm.nih.gov/pubmed/33362865
http://dx.doi.org/10.3389/fgene.2020.606940
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author Liu, Yuejuan
Cui, Yuxia
Bai, Xuefeng
Feng, Chenchen
Li, Meng
Han, Xiaole
Ai, Bo
Zhang, Jian
Li, Xuecang
Han, Junwei
Zhu, Jiang
Jiang, Yong
Pan, Qi
Wang, Fan
Xu, Mingcong
Li, Chunquan
Wang, Qiuyu
author_facet Liu, Yuejuan
Cui, Yuxia
Bai, Xuefeng
Feng, Chenchen
Li, Meng
Han, Xiaole
Ai, Bo
Zhang, Jian
Li, Xuecang
Han, Junwei
Zhu, Jiang
Jiang, Yong
Pan, Qi
Wang, Fan
Xu, Mingcong
Li, Chunquan
Wang, Qiuyu
author_sort Liu, Yuejuan
collection PubMed
description BACKGROUND: Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients. METHODS: We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules. RESULTS: We identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets. CONCLUSIONS: Our study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed.
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spelling pubmed-77560312020-12-24 MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer Liu, Yuejuan Cui, Yuxia Bai, Xuefeng Feng, Chenchen Li, Meng Han, Xiaole Ai, Bo Zhang, Jian Li, Xuecang Han, Junwei Zhu, Jiang Jiang, Yong Pan, Qi Wang, Fan Xu, Mingcong Li, Chunquan Wang, Qiuyu Front Genet Genetics BACKGROUND: Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients. METHODS: We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules. RESULTS: We identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets. CONCLUSIONS: Our study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed. Frontiers Media S.A. 2020-12-09 /pmc/articles/PMC7756031/ /pubmed/33362865 http://dx.doi.org/10.3389/fgene.2020.606940 Text en Copyright © 2020 Liu, Cui, Bai, Feng, Li, Han, Ai, Zhang, Li, Han, Zhu, Jiang, Pan, Wang, Xu, Li and Wang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Liu, Yuejuan
Cui, Yuxia
Bai, Xuefeng
Feng, Chenchen
Li, Meng
Han, Xiaole
Ai, Bo
Zhang, Jian
Li, Xuecang
Han, Junwei
Zhu, Jiang
Jiang, Yong
Pan, Qi
Wang, Fan
Xu, Mingcong
Li, Chunquan
Wang, Qiuyu
MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer
title MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer
title_full MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer
title_fullStr MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer
title_full_unstemmed MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer
title_short MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer
title_sort mirna-mediated subpathway identification and network module analysis to reveal prognostic markers in human pancreatic cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756031/
https://www.ncbi.nlm.nih.gov/pubmed/33362865
http://dx.doi.org/10.3389/fgene.2020.606940
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