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Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer
Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lu...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958511/ https://www.ncbi.nlm.nih.gov/pubmed/24643254 http://dx.doi.org/10.1371/journal.pone.0092395 |
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author | Jia, Xu Miao, Zhengqiang Li, Wan Zhang, Liangcai Feng, Chenchen He, Yuehan Bi, Xiaoman Wang, Liqiang Du, Youwen Hou, Min Hao, Dapeng Xiao, Yun Chen, Lina Li, Kongning |
author_facet | Jia, Xu Miao, Zhengqiang Li, Wan Zhang, Liangcai Feng, Chenchen He, Yuehan Bi, Xiaoman Wang, Liqiang Du, Youwen Hou, Min Hao, Dapeng Xiao, Yun Chen, Lina Li, Kongning |
author_sort | Jia, Xu |
collection | PubMed |
description | Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms. |
format | Online Article Text |
id | pubmed-3958511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39585112014-03-24 Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer Jia, Xu Miao, Zhengqiang Li, Wan Zhang, Liangcai Feng, Chenchen He, Yuehan Bi, Xiaoman Wang, Liqiang Du, Youwen Hou, Min Hao, Dapeng Xiao, Yun Chen, Lina Li, Kongning PLoS One Research Article Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms. Public Library of Science 2014-03-18 /pmc/articles/PMC3958511/ /pubmed/24643254 http://dx.doi.org/10.1371/journal.pone.0092395 Text en © 2014 Jia et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Jia, Xu Miao, Zhengqiang Li, Wan Zhang, Liangcai Feng, Chenchen He, Yuehan Bi, Xiaoman Wang, Liqiang Du, Youwen Hou, Min Hao, Dapeng Xiao, Yun Chen, Lina Li, Kongning Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer |
title | Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer |
title_full | Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer |
title_fullStr | Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer |
title_full_unstemmed | Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer |
title_short | Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer |
title_sort | cancer-risk module identification and module-based disease risk evaluation: a case study on lung cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958511/ https://www.ncbi.nlm.nih.gov/pubmed/24643254 http://dx.doi.org/10.1371/journal.pone.0092395 |
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