Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk
© 2019 The Authors. Published by FEBS Press and John Wiley & Sons Ltd..
Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA-mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA-mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA-mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway-based, miRNA-based, and gene-based classification methods. The high-frequency miRNA-mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.
Errataetall: | |
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Medienart: |
E-Artikel |
Erscheinungsjahr: |
2019 |
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Erschienen: |
2019 |
Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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Enthalten in: |
Molecular oncology - 13(2019), 10 vom: 03. Okt., Seite 2211-2226 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Ning, Ziyu [VerfasserIn] |
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Links: |
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Themen: |
Cancer biomarker |
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Anmerkungen: |
Date Completed 29.06.2020 Date Revised 29.06.2020 published: Print-Electronic ErratumIn: Mol Oncol. 2019 Nov;13(11):2512. - PMID 31670488 Citation Status MEDLINE |
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doi: |
10.1002/1878-0261.12563 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM300190190 |
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500 | |a Citation Status MEDLINE | ||
520 | |a © 2019 The Authors. Published by FEBS Press and John Wiley & Sons Ltd. | ||
520 | |a Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA-mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA-mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA-mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway-based, miRNA-based, and gene-based classification methods. The high-frequency miRNA-mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Research Support, Non-U.S. Gov't | |
650 | 4 | |a cancer biomarker | |
650 | 4 | |a classification | |
650 | 4 | |a miRNA-mediated subpathway | |
650 | 4 | |a topological information | |
650 | 7 | |a MicroRNAs |2 NLM | |
700 | 1 | |a Feng, Chenchen |e verfasserin |4 aut | |
700 | 1 | |a Song, Chao |e verfasserin |4 aut | |
700 | 1 | |a Liu, Wei |e verfasserin |4 aut | |
700 | 1 | |a Shang, Desi |e verfasserin |4 aut | |
700 | 1 | |a Li, Meng |e verfasserin |4 aut | |
700 | 1 | |a Wang, Qiuyu |e verfasserin |4 aut | |
700 | 1 | |a Zhao, Jianmei |e verfasserin |4 aut | |
700 | 1 | |a Liu, Yuejuan |e verfasserin |4 aut | |
700 | 1 | |a Chen, Jiaxin |e verfasserin |4 aut | |
700 | 1 | |a Yu, Xiaoyang |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Jian |e verfasserin |4 aut | |
700 | 1 | |a Li, Chunquan |e verfasserin |4 aut | |
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