Identification of cancer related genes using a comprehensive map of human gene expression

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Show simple item record Torrente Orihuela, Ester Aurora Lukk, Margus Xue, Vincent Parkinson, Helen Rung, Johan Brazma, Alvis 2022-11-14T12:11:29Z 2022-11-14T12:11:29Z 2016-06-20
dc.identifier.bibliographicCitation Torrente A, Lukk M, Xue V, Parkinson H, Rung J, Brazma A (2016) Identification of Cancer Related Genes Using a Comprehensive Map of Human Gene Expression. PLoS ONE 11(6): e0157484. doi:10.1371/journal.pone.0157484
dc.identifier.issn 1932-6203
dc.description.abstract Rapid accumulation and availability of gene expression datasets in public repositories have enabled large-scale meta-analyses of combined data. The richness of cross-experiment data has provided new biological insights, including identification of new cancer genes. In this study, we compiled a human gene expression dataset from similar to 40,000 publicly available Affymetrix HG-U133Plus2 arrays. After strict quality control and data normalisation the data was quantified in an expression matrix of similar to 20,000 genes and similar to 28,000 samples. To enable different ways of sample grouping, existing annotations where subjected to systematic ontology assisted categorisation and manual curation. Groups like normal tissues, neoplasmic tissues, cell lines, homoeotic cells and incompletely differentiated cells were created. Unsupervised analysis of the data confirmed global structure of expression consistent with earlier analysis but with more details revealed due to increased resolution. A suitable mixed-effects linear model was used to further investigate gene expression in solid tissue tumours, and to compare these with the respective healthy solid tissues. The analysis identified 1,285 genes with systematic expression change in cancer. The list is significantly enriched with known cancer genes from large, public, peer-reviewed databases, whereas the remaining ones are proposed as new cancer gene candidates. The compiled dataset is publicly available in the ArrayExpress Archive. It contains the most diverse collection of biological samples, making it the largest systematically annotated gene expression dataset of its kind in the public domain.
dc.description.sponsorship AB received funding from the EurocanPlatform grant of FP7 frameworks from the European Commission. AT was partially supported by the Ramón Areces Foundation.
dc.format.extent 20
dc.language.iso eng
dc.publisher PLOS
dc.rights © 2016 Torrente et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights Atribución 3.0 España
dc.subject.other Mixed-effects model
dc.subject.other Microarray data
dc.subject.other Data sets
dc.subject.other Resource
dc.subject.other Tissues
dc.subject.other Update
dc.subject.other Atlas
dc.title Identification of cancer related genes using a comprehensive map of human gene expression
dc.type article
dc.subject.eciencia Biología y Biomedicina
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/260791/EurocanPlatform
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 6
dc.identifier.publicationlastpage 20
dc.identifier.publicationtitle PLoS One
dc.identifier.publicationvolume 11
dc.identifier.uxxi AR/0000018036
dc.contributor.funder European Commission
dc.affiliation.dpto UC3M. Departamento de Matemáticas
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Modelización, Simulación Numérica y Matemática Industrial
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