Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity
Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyz...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.596794/full |
| _version_ | 1819081527780179968 |
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| author | Yan Zhang Yan Zhang Yan Zhang Ju Xiang Ju Xiang Ju Xiang Liang Tang Jianming Li Qingqing Lu Qingqing Lu Geng Tian Geng Tian Bin-Sheng He Bin-Sheng He Jialiang Yang Jialiang Yang Jialiang Yang |
| author_facet | Yan Zhang Yan Zhang Yan Zhang Ju Xiang Ju Xiang Ju Xiang Liang Tang Jianming Li Qingqing Lu Qingqing Lu Geng Tian Geng Tian Bin-Sheng He Bin-Sheng He Jialiang Yang Jialiang Yang Jialiang Yang |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation. |
| first_indexed | 2024-12-21T20:02:12Z |
| format | Article |
| id | doaj.art-2841e9124e2f4e33aad562cf26f8e2fd |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-21T20:02:12Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-2841e9124e2f4e33aad562cf26f8e2fd2022-12-21T18:51:56ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-08-011210.3389/fgene.2021.596794596794Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network ModularityYan Zhang0Yan Zhang1Yan Zhang2Ju Xiang3Ju Xiang4Ju Xiang5Liang Tang6Jianming Li7Qingqing Lu8Qingqing Lu9Geng Tian10Geng Tian11Bin-Sheng He12Bin-Sheng He13Jialiang Yang14Jialiang Yang15Jialiang Yang16School of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Information Science and Engineering, Changsha Medical University, Changsha, ChinaAcademician Workstation, Changsha Medical University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaAcademician Workstation, Changsha Medical University, Changsha, ChinaNeuroscience Research Center & Department of Basic Medical Sciences, Changsha Medical University, Changsha, ChinaNeuroscience Research Center & Department of Basic Medical Sciences, Changsha Medical University, Changsha, ChinaNeuroscience Research Center & Department of Basic Medical Sciences, Changsha Medical University, Changsha, ChinaQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, ChinaGeneis Beijing Co., Ltd., Beijing, ChinaQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, ChinaGeneis Beijing Co., Ltd., Beijing, ChinaAcademician Workstation, Changsha Medical University, Changsha, ChinaNeuroscience Research Center & Department of Basic Medical Sciences, Changsha Medical University, Changsha, ChinaAcademician Workstation, Changsha Medical University, Changsha, ChinaQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, ChinaGeneis Beijing Co., Ltd., Beijing, ChinaComplex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.https://www.frontiersin.org/articles/10.3389/fgene.2021.596794/fulldisease-gene predictionprotein-protein interactionsKEGG pathwaybreast cancernetwork propagation |
| spellingShingle | Yan Zhang Yan Zhang Yan Zhang Ju Xiang Ju Xiang Ju Xiang Liang Tang Jianming Li Qingqing Lu Qingqing Lu Geng Tian Geng Tian Bin-Sheng He Bin-Sheng He Jialiang Yang Jialiang Yang Jialiang Yang Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity Frontiers in Genetics disease-gene prediction protein-protein interactions KEGG pathway breast cancer network propagation |
| title | Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity |
| title_full | Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity |
| title_fullStr | Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity |
| title_full_unstemmed | Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity |
| title_short | Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity |
| title_sort | identifying breast cancer related genes based on a novel computational framework involving kegg pathways and ppi network modularity |
| topic | disease-gene prediction protein-protein interactions KEGG pathway breast cancer network propagation |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.596794/full |
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