{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T23:09:09Z","timestamp":1771715349267,"version":"3.50.1"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"S2","license":[{"start":{"date-parts":[[2012,3,13]],"date-time":"2012-03-13T00:00:00Z","timestamp":1331596800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted gene co-expression network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of gene co-expression networks which are related to GBM prognosis.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-network in WGCN. Each sub-network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of genes are carried out to determine the thresholds for log-rank test p-values for network selection. Sub-networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (&gt;50%). The functions for each cluster are analyzed using gene ontology enrichment analysis.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Using the eQCM algorithm, we identified 8,124 sub-networks in the WGCN, out of which 170 sub-networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>We identified 16 gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epigenetic events in GBM development and prognosis.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-13-s2-s12","type":"journal-article","created":{"date-parts":[[2019,12,11]],"date-time":"2019-12-11T01:59:53Z","timestamp":1576029593000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data"],"prefix":"10.1186","volume":"13","author":[{"given":"Yang","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cun-Quan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,3,13]]},"reference":[{"issue":"6871","key":"5072_CR1","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1038\/415530a","volume":"415","author":"LJ van 't Veer","year":"2002","unstructured":"van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT: Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002, 415 (6871): 530-536. 10.1038\/415530a.","journal-title":"Nature"},{"issue":"25","key":"5072_CR2","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1056\/NEJMoa021967","volume":"347","author":"MJ van de Vijver","year":"2002","unstructured":"van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ: A gene-expression signature as a predictor of survival in breast cancer. The New England journal of medicine. 2002, 347 (25): 1999-2009. 10.1056\/NEJMoa021967.","journal-title":"The New England journal of medicine"},{"issue":"17","key":"5072_CR3","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1093\/jnci\/djj329","volume":"98","author":"M Buyse","year":"2006","unstructured":"Buyse M, Loi S, van't Veer L, Viale G, Delorenzi M, Glas AM, d'Assignies MS, Bergh J, Lidereau R, Ellis P: Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. Journal of the National Cancer Institute. 2006, 98 (17): 1183-1192. 10.1093\/jnci\/djj329.","journal-title":"Journal of the National Cancer Institute"},{"key":"5072_CR4","first-page":"428","volume-title":"Proceedings of the International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS)","author":"J Zhang","year":"2009","unstructured":"Zhang J, Huang K, Xiang Y, Jin R: Using Frequent Co-expression Network to Identify Gene Clusters for Breast Cancer Prognosis. Proceedings of the International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS). 2009, Shanghai: IEEE Computer Society, 428-434."},{"key":"5072_CR5","doi-asserted-by":"crossref","unstructured":"Zhang J, Xiang Y, Ding L, Keen-Circle K, Borlawsky TB, Ozer HG, Jin R, Payne P, Huang K: Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia. BMC bioinformatics. 11 (Suppl 9): S5-","DOI":"10.1186\/1471-2105-11-S9-S5"},{"key":"5072_CR6","doi-asserted-by":"crossref","unstructured":"Hu H, Yan X, Huang Y, Han J, Zhou XJ: Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics (Oxford, England). 2005, i213-221. 21 Suppl 1","DOI":"10.1093\/bioinformatics\/bti1049"},{"key":"5072_CR7","volume-title":"Statistical applications in genetics and molecular biology","author":"B Zhang","year":"2005","unstructured":"Zhang B, Horvath S: A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology. 2005, 4: Article17"},{"issue":"11","key":"5072_CR8","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1038\/ng.2007.2","volume":"39","author":"MA Pujana","year":"2007","unstructured":"Pujana MA, Han JD, Starita LM, Stevens KN, Tewari M, Ahn JS, Rennert G, Moreno V, Kirchhoff T, Gold B: Network modeling links breast cancer susceptibility and centrosome dysfunction. Nature genetics. 2007, 39 (11): 1338-1349. 10.1038\/ng.2007.2.","journal-title":"Nature genetics"},{"issue":"46","key":"5072_CR9","doi-asserted-by":"publisher","first-page":"17402","DOI":"10.1073\/pnas.0608396103","volume":"103","author":"S Horvath","year":"2006","unstructured":"Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z: Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proceedings of the National Academy of Sciences of the United States of America. 2006, 103 (46): 17402-17407. 10.1073\/pnas.0608396103.","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"5072_CR10","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1186\/1471-2105-9-559","volume":"9","author":"P Langfelder","year":"2008","unstructured":"Langfelder P, Horvath S: WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics. 2008, 9: 559-10.1186\/1471-2105-9-559.","journal-title":"BMC bioinformatics"},{"issue":"4","key":"5072_CR11","doi-asserted-by":"publisher","first-page":"619","DOI":"10.3934\/jimo.2007.3.619","volume":"3","author":"Y Ou","year":"2007","unstructured":"Ou Y, Zhang C-Q: A new multimembership clustering method. Jounral of Industrial and Management Optimization. 2007, 3 (4): 619-624.","journal-title":"Jounral of Industrial and Management Optimization"},{"issue":"2","key":"5072_CR12","doi-asserted-by":"publisher","first-page":"026113","DOI":"10.1103\/PhysRevE.69.026113","volume":"69","author":"M Newman","year":"2004","unstructured":"Newman M, Girvan M: Finding and evaluating community structure in networks. Physical Review E. 2004, 69 (2): 026113-","journal-title":"Physical Review E"},{"key":"5072_CR13","doi-asserted-by":"crossref","unstructured":"Schwartzbaum JA, Huang K, Lawler S, Ding B, Yu J, Chiocca EA: Allergy and inflammatory transcriptome is predominantly negatively correlated with CD133 expression in glioblastoma. Neuro-oncology. 12 (4): 320-327.","DOI":"10.1093\/neuonc\/nop035"},{"issue":"7","key":"5072_CR14","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1016\/j.cell.2009.06.001","volume":"137","author":"JE Phillips","year":"2009","unstructured":"Phillips JE, Corces VG: CTCF: master weaver of the genome. Cell. 2009, 137 (7): 1194-1211. 10.1016\/j.cell.2009.06.001.","journal-title":"Cell"},{"issue":"3","key":"5072_CR15","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/S0092-8674(01)00450-0","volume":"106","author":"M Fuchs","year":"2001","unstructured":"Fuchs M, Gerber J, Drapkin R, Sif S, Ikura T, Ogryzko V, Lane WS, Nakatani Y, Livingston DM: The p400 complex is an essential E1A transformation target. Cell. 2001, 106 (3): 297-307. 10.1016\/S0092-8674(01)00450-0.","journal-title":"Cell"},{"issue":"3","key":"5072_CR16","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1093\/embo-reports\/kvf053","volume":"3","author":"A Eberharter","year":"2002","unstructured":"Eberharter A, Becker PB: Histone acetylation: a switch between repressive and permissive chromatin. Second in review series on chromatin dynamics. EMBO reports. 2002, 3 (3): 224-229. 10.1093\/embo-reports\/kvf053.","journal-title":"EMBO reports"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-13-S2-S12.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/1471-2105-13-S2-S12\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-13-S2-S12.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T18:34:00Z","timestamp":1630521240000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-13-S2-S12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,3,13]]},"references-count":16,"journal-issue":{"issue":"S2","published-print":{"date-parts":[[2012,12]]}},"alternative-id":["5072"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-13-s2-s12","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,3,13]]},"assertion":[{"value":"13 March 2012","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"S12"}}