SmulTCan: A Shiny application for multivariable survival analysis of TCGA data with gene sets

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buir.contributor.authorÖzhan, Ayşe
buir.contributor.authorTombaz, Melike
buir.contributor.authorKonu, Özlem
buir.contributor.orcidÖzhan, Ayşe|0000-0003-0282-0777
buir.contributor.orcidTombaz, Melike|0000-0002-0528-6680
dc.citation.epage104793-9en_US
dc.citation.spage104793-1en_US
dc.citation.volumeNumber137en_US
dc.contributor.authorÖzhan, Ayşe
dc.contributor.authorTombaz, Melike
dc.contributor.authorKonu, Özlem
dc.date.accessioned2022-02-02T10:39:32Z
dc.date.available2022-02-02T10:39:32Z
dc.date.issued2021-10
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.description.abstractBackground Survival analysis is widely used in cancer research, and although several methods exist in R, there is the need for a more interactive, flexible, yet comprehensive online tool to analyze gene sets using Cox proportional hazards (CPH) models. The web-based Shiny application (app) SmulTCan extends existing tools to multivariable CPH models of gene sets—as exemplified using the netrins and their receptors (netrins-receptors). It can be used to identify survival gene signatures (GSs) and select the best subsets of input gene, microRNA, methylation level, and copy number variation sets from the Cancer Genome Atlas (TCGA). Objectives To create a tool for CPH model building and best subset selection, using survival data from TCGA with input gene expression files from UCSC Xena. Furthermore, we aim to analyze the input TSV file of netrins-receptors in SmulTCan and discuss our findings. Methods SmulTCan uses Shiny's reactivity with built-in R functions from packages for CPH model analysis and best subset selection including “survminer”, “riskRegression”, “rms”, “glmnet”, and “BeSS”. Results Results from the SmulTCan app with the netrins-receptors gene set indicated unique hazard ratio GSs in certain renal and neural cancers, while the best subsets for this gene set, obtained via the app, could differentiate between prognostic outcomes in these cancers.en_US
dc.description.provenanceSubmitted by Samet Emre (samet.emre@bilkent.edu.tr) on 2022-02-02T10:39:32Z No. of bitstreams: 1 SmulTCan_A_Shiny_application_for_multivariable_survival_analysis_of_TCGA_data_with_gene_sets___TCGA.pdf: 4381034 bytes, checksum: 62e8f9e5ede23bcdf3ffd06a29d45d94 (MD5)en
dc.description.provenanceMade available in DSpace on 2022-02-02T10:39:32Z (GMT). No. of bitstreams: 1 SmulTCan_A_Shiny_application_for_multivariable_survival_analysis_of_TCGA_data_with_gene_sets___TCGA.pdf: 4381034 bytes, checksum: 62e8f9e5ede23bcdf3ffd06a29d45d94 (MD5) Previous issue date: 2021-10en
dc.embargo.release2022-10-31
dc.identifier.doi10.1016/j.compbiomed.2021.104793en_US
dc.identifier.eissn1879-0534
dc.identifier.issn0010-4825
dc.identifier.urihttp://hdl.handle.net/11693/76964
dc.language.isoEnglishen_US
dc.publisherElsevier Ltden_US
dc.relation.isversionofhttps://doi.org/10.1016/j.compbiomed.2021.104793en_US
dc.source.titleComputers in Biology and Medicineen_US
dc.subjectSurvivalen_US
dc.subjectCPHen_US
dc.subjectShinyen_US
dc.subjectNetrinsen_US
dc.subjectK-Men_US
dc.subjectElastic neten_US
dc.subjectPrognosisen_US
dc.subjectTCGAen_US
dc.titleSmulTCan: A Shiny application for multivariable survival analysis of TCGA data with gene setsen_US
dc.typeArticleen_US

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Department of Molecular Biology and Genetics
Institute of Materials Science and Nanotechnology (UNAM)