Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk
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...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-10-01
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| Series: | Molecular Oncology |
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| Online Access: | https://doi.org/10.1002/1878-0261.12563 |
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| _version_ | 1849314979713384448 |
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| author | Ziyu Ning Chenchen Feng Chao Song Wei Liu Desi Shang Meng Li Qiuyu Wang Jianmei Zhao Yuejuan Liu Jiaxin Chen Xiaoyang Yu Jian Zhang Chunquan Li |
| author_facet | Ziyu Ning Chenchen Feng Chao Song Wei Liu Desi Shang Meng Li Qiuyu Wang Jianmei Zhao Yuejuan Liu Jiaxin Chen Xiaoyang Yu Jian Zhang Chunquan Li |
| author_sort | Ziyu Ning |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-30ae4070851a4b32bb54d4a3a2f83a3f |
| institution | Kabale University |
| issn | 1574-7891 1878-0261 |
| language | English |
| publishDate | 2019-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Molecular Oncology |
| spelling | doaj-art-30ae4070851a4b32bb54d4a3a2f83a3f2025-08-20T03:52:16ZengWileyMolecular Oncology1574-78911878-02612019-10-0113102211222610.1002/1878-0261.12563Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walkZiyu Ning0Chenchen Feng1Chao Song2Wei Liu3Desi Shang4Meng Li5Qiuyu Wang6Jianmei Zhao7Yuejuan Liu8Jiaxin Chen9Xiaoyang Yu10Jian Zhang11Chunquan Li12School of Medical Informatics Harbin Medical University Daqing ChinaSchool of Medical Informatics Harbin Medical University Daqing ChinaSchool of Pharmacology Harbin Medical University Daqing ChinaDepartment of Mathematics Heilongjiang Institute of Technology Harbin ChinaCollege of Bioinformatics Science and Technology Harbin Medical University ChinaSchool of Medical Informatics Harbin Medical University Daqing ChinaSchool of Medical Informatics Harbin Medical University Daqing ChinaSchool of Medical Informatics Harbin Medical University Daqing ChinaSchool of Medical Informatics Harbin Medical University Daqing ChinaSchool of Medical Informatics Harbin Medical University Daqing ChinaThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province Harbin University of Science and Technology ChinaSchool of Medical Informatics Harbin Medical University Daqing ChinaSchool of Medical Informatics Harbin Medical University Daqing ChinaAccurate 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.https://doi.org/10.1002/1878-0261.12563cancer biomarkerclassificationmiRNA‐mediated subpathwaytopological information |
| spellingShingle | Ziyu Ning Chenchen Feng Chao Song Wei Liu Desi Shang Meng Li Qiuyu Wang Jianmei Zhao Yuejuan Liu Jiaxin Chen Xiaoyang Yu Jian Zhang Chunquan Li Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk Molecular Oncology cancer biomarker classification miRNA‐mediated subpathway topological information |
| title | Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk |
| title_full | Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk |
| title_fullStr | Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk |
| title_full_unstemmed | Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk |
| title_short | Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk |
| title_sort | topologically inferring active mirna mediated subpathways toward precise cancer classification by directed random walk |
| topic | cancer biomarker classification miRNA‐mediated subpathway topological information |
| url | https://doi.org/10.1002/1878-0261.12563 |
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