Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles

BMC Genomics

Research

Javier De Las Rivas¹  Beatriz Roson-Burgo¹  Francisco J Campos-Laborie¹  Sara Aibar¹  Conrad Droste¹  Celia Fontanillo²  Jesus M Hernandez-Rivas³ 

[1] Cancer Research Center (IMBCC, CSIC/USAL/IBSAL), Campus Miguel de Unamuno s/n, 37007, Salamanca, Spain;Cancer Research Center (IMBCC, CSIC/USAL/IBSAL), Campus Miguel de Unamuno s/n, 37007, Salamanca, Spain;Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, c/Isaac Newton 4, 41092, Sevilla, Spain;Hematology Department, Hospital Universitario de Salamanca (HUS/IBSAL/USAL), Paseo San Vicente 58-182, 37007, Salamanca, Spain;

关键词: gene;    expression;    expression profile;    gene networks;    microarray;    RNA-Seq;    disease;    disease classification;    cancer;    leukemia;    acute leukemia;   

DOI  :  10.1186/1471-2164-16-S5-S3

来源: Springer

【 摘 要 】

BackgroundDespite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes.ResultsWe present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifies the compared disease subtypes; (iii) provide a measurement of the discriminant power of these genes and (iv) identify links between the genes that characterise each of the disease subtypes. This bioinformatic approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. To illustrate the performance of the tool, we applied it to two independent datasets: 250 samples from patients with four major leukemia subtypes analysed using expression arrays; another leukemia dataset analysed with RNA-Seq that includes a subtype also present in the previous set. The results show the selection of key deregulated genes recently reported in the literature and assigned to the leukemia subtypes studied. We also show, using these independent datasets, the selection of similar genes in a network built for the same disease subtype.ConclusionsThe construction of gene networks related to specific disease subtypes that include parameters such as gene-to-gene association, gene disease specificity and gene discriminant power can be very useful to draw gene-disease maps and to unravel the molecular features that characterize specific pathological states. The application of the bioinformatic tool here presented shows a neat way to achieve such molecular characterization of the diseases using genome-wide expression data.

【 授权许可】

Unknown   
© Aibar et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.