Tamizh Selvan Gnana Sekaran, Vishakh R Kedilaya, Suchetha N Kumari, Praveenkumar Shetty, Pavan Gollapalli
{"title":"探索人类淋巴细胞对电离辐射反应的差异表达基因:一种网络生物学方法。","authors":"Tamizh Selvan Gnana Sekaran, Vishakh R Kedilaya, Suchetha N Kumari, Praveenkumar Shetty, Pavan Gollapalli","doi":"10.3857/roj.2021.00045","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach.</p><p><strong>Materials and methods: </strong>Analysis of gene ontology shows that 80, 42, 25, and 35 genes have roles in the biological process, molecular function, the cellular process, and immune system pathways, respectively. Therefore, our study emphasizes gene/protein network analysis on various differentially expressed genes (DEGs) to reveal the interactions between those proteins and their functional contribution upon radiation exposure.</p><p><strong>Results: </strong>A gene/protein interaction network was constructed, which comprises 79 interactors with 718 interactions and TP53, MAPK8, MAPK1, CASP3, MAPK14, ATM, NOTCH1, VEGFA, SIRT1, and PRKDC are the top 10 proteins in the network with high betweenness centrality values. Further, molecular complex detection was used to cluster these associated partners in the network, which produced three effective clusters based on the Molecular Complex Detection (MCODE) score. Interestingly, we found a high functional similarity from the associated genes/proteins in the network with known radiation response genes.</p><p><strong>Conclusion: </strong>This network-based approach on DEGs of human lymphocytes upon response to ionizing radiation provides clues for an opportunity to improve therapeutic efficacy.</p>","PeriodicalId":46572,"journal":{"name":"Radiation Oncology Journal","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/63/82/roj-2021-00045.PMC8024183.pdf","citationCount":"7","resultStr":"{\"title\":\"Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach.\",\"authors\":\"Tamizh Selvan Gnana Sekaran, Vishakh R Kedilaya, Suchetha N Kumari, Praveenkumar Shetty, Pavan Gollapalli\",\"doi\":\"10.3857/roj.2021.00045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach.</p><p><strong>Materials and methods: </strong>Analysis of gene ontology shows that 80, 42, 25, and 35 genes have roles in the biological process, molecular function, the cellular process, and immune system pathways, respectively. Therefore, our study emphasizes gene/protein network analysis on various differentially expressed genes (DEGs) to reveal the interactions between those proteins and their functional contribution upon radiation exposure.</p><p><strong>Results: </strong>A gene/protein interaction network was constructed, which comprises 79 interactors with 718 interactions and TP53, MAPK8, MAPK1, CASP3, MAPK14, ATM, NOTCH1, VEGFA, SIRT1, and PRKDC are the top 10 proteins in the network with high betweenness centrality values. Further, molecular complex detection was used to cluster these associated partners in the network, which produced three effective clusters based on the Molecular Complex Detection (MCODE) score. Interestingly, we found a high functional similarity from the associated genes/proteins in the network with known radiation response genes.</p><p><strong>Conclusion: </strong>This network-based approach on DEGs of human lymphocytes upon response to ionizing radiation provides clues for an opportunity to improve therapeutic efficacy.</p>\",\"PeriodicalId\":46572,\"journal\":{\"name\":\"Radiation Oncology Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2021-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/63/82/roj-2021-00045.PMC8024183.pdf\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiation Oncology Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3857/roj.2021.00045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/3/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiation Oncology Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3857/roj.2021.00045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/3/24 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach.
Purpose: The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach.
Materials and methods: Analysis of gene ontology shows that 80, 42, 25, and 35 genes have roles in the biological process, molecular function, the cellular process, and immune system pathways, respectively. Therefore, our study emphasizes gene/protein network analysis on various differentially expressed genes (DEGs) to reveal the interactions between those proteins and their functional contribution upon radiation exposure.
Results: A gene/protein interaction network was constructed, which comprises 79 interactors with 718 interactions and TP53, MAPK8, MAPK1, CASP3, MAPK14, ATM, NOTCH1, VEGFA, SIRT1, and PRKDC are the top 10 proteins in the network with high betweenness centrality values. Further, molecular complex detection was used to cluster these associated partners in the network, which produced three effective clusters based on the Molecular Complex Detection (MCODE) score. Interestingly, we found a high functional similarity from the associated genes/proteins in the network with known radiation response genes.
Conclusion: This network-based approach on DEGs of human lymphocytes upon response to ionizing radiation provides clues for an opportunity to improve therapeutic efficacy.