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Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

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dc.contributor Universitat de Vic. Grup de Recerca en Bioinformàtica i Estadística Mèdica
dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor.author López de Maturana, Evangelina
dc.contributor.author Ye, Yuanging
dc.contributor.author Calle, M. Luz
dc.contributor.author Rothman, Nathaniel
dc.contributor.author Urrea Gales, Víctor
dc.contributor.author Kogevinas, Manolis
dc.contributor.author Petrus, Sandra
dc.contributor.author Chanock, Stephen
dc.contributor.author Tardón, Adonina
dc.contributor.author García-Closas, Montserrat
dc.contributor.author González-Neira, Anna
dc.contributor.author Vellalta, Gemma
dc.contributor.author Carrato, Alfredo
dc.contributor.author Navarro, Arcadi
dc.contributor.author Lorente-Galdós, Belén
dc.contributor.author Silverman, Debra T.
dc.contributor.author Real, Francisco X.
dc.contributor.author Wu, Xifeng
dc.contributor.author Malats i Riera, Núria
dc.date.accessioned 2014-01-15T09:23:34Z
dc.date.available 2014-01-15T09:23:34Z
dc.date.issued 2013
dc.identifier.citation de Maturana EL, Ye Y, Calle ML, Rothman N, Urrea V, et al. (2013) Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk. PLoS ONE 8(12): e83745. doi:10.1371/journal.pone.0083745 ca_ES
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10854/2633
dc.description.abstract The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk. ca_ES
dc.description.sponsorship The work was partially supported by the Fondo de Investigacion Sanitaria, Instituto de Salud Carlos III (G03/174, 00/0745, PI051436, PI061614, PI09-02102, G03/174 and Sara Borrell fellowship to ELM) and Ministry of Science and Innovation (MTM2008-06747-C02-02 and FPU fellowship award to VU), Spain; AGAUR-Generalitat de Catalunya (Grant 2009SGR-581); Fundaciola Maratode TV3; Red Tematica de Investigacion Cooperativa en Cancer (RTICC); Asociacion Espanola Contra el Cancer (AECC); EU-FP7-201663; and RO1-CA089715 and CA34627; the Spanish National Institute for Bioinformatics (www.inab.org); and by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA. MD Anderson support for this project included U01 CA 127615 (XW); R01 CA 74880 (XW); P50 CA 91846 (XW, CPD); Betty B. Marcus Chair fund in Cancer Prevention (XW); UT Research Trust fund (XW) and R01 CA 131335 (JG).
dc.format application/pdf
dc.format.extent 11 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher Public Library of Science ca_ES
dc.relation MEC/PN2008-2011/MTM2008-06747-C02-00
dc.relation AGAUR/2009-2014/2009SGR-581
dc.rights Aquest document està subjecte a aquesta llicència Creative Commons ca_ES
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/ ca_ES
dc.subject.other Càncer ca_ES
dc.subject.other Genètica ca_ES
dc.title Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk ca_ES
dc.type info:eu-repo/semantics/article ca_ES
dc.identifier.doi https://doi.org/10.1371/journal.pone.0083745
dc.rights.accesRights info:eu-repo/semantics/openAccess ca_ES
dc.type.version info:eu-repo/publishedVersion ca_ES
dc.indexacio Indexat a SCOPUS
dc.indexacio Indexat a WOS/JCR ca_ES
dc.contribution.funder Ministerio de Ciencia e Innovación (España)
dc.contribution.funder Generalitat de Catalunya. Agència de Gestió d'Ajuts Universitaris i de Recerca

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