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

被引:17
作者
Aibar, Sara [1 ]
Fontanillo, Celia [1 ,3 ]
Droste, Conrad [1 ]
Roson-Burgo, Beatriz [1 ]
Campos-Laborie, Francisco J. [1 ]
Hernandez-Rivas, Jesus M. [2 ]
Rivas, Javier De Las [1 ]
机构
[1] USAL, CSIC, IMBCC,IBSAL, Canc Res Ctr, Salamanca 37007, Spain
[2] USAL, Hosp Univ Salamanca, HUS,IBSAL, Dept Hematol, Salamanca 37007, Spain
[3] CITRE, Seville 41092, Spain
来源
BMC GENOMICS | 2015年 / 16卷
关键词
DOWN-REGULATION; CLASSIFICATION; PACKAGE; MEIS1;
D O I
10.1186/1471-2164-16-S5-S3
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: Despite 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. Results: We 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. Conclusions: The 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.
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页数:10
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共 30 条
  • [11] Gene expression profiling of Polycomb, Hox and Meis genes in patients with acute myeloid leukaemia
    Grubach, Lykke
    Juhl-Christensen, Caroline
    Rethmeier, Anita
    Olesen, Lene Hyldahl
    Aggerholm, Anni
    Hokland, Peter
    Ostergaard, Mette
    [J]. EUROPEAN JOURNAL OF HAEMATOLOGY, 2008, 81 (02) : 112 - 122
  • [12] Clinical Utility of Microarray-Based Gene Expression Profiling in the Diagnosis and Subclassification of Leukemia: Report From the International Microarray Innovations in Leukemia Study Group
    Haferlach, Torsten
    Kohlmann, Alexander
    Wieczorek, Lothar
    Basso, Giuseppe
    Kronnie, Geertruy Te
    Bene, Marie-Christine
    De Vos, John
    Hernandez, Jesus M.
    Hofmann, Wolf-Karsten
    Mills, Ken I.
    Gilkes, Amanda
    Chiaretti, Sabina
    Shurtleff, Sheila A.
    Kipps, Thomas J.
    Rassenti, Laura Z.
    Yeoh, Allen E.
    Papenhausen, Peter R.
    Liu, Wei-min
    Williams, P. Mickey
    Foa, Robin
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2010, 28 (15) : 2529 - 2537
  • [13] Exploration, normalization, and summaries of high density oligonucleotide array probe level data
    Irizarry, RA
    Hobbs, B
    Collin, F
    Beazer-Barclay, YD
    Antonellis, KJ
    Scherf, U
    Speed, TP
    [J]. BIOSTATISTICS, 2003, 4 (02) : 249 - 264
  • [14] On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles
    Kendziorski, CM
    Newton, MA
    Lan, H
    Gould, MN
    [J]. STATISTICS IN MEDICINE, 2003, 22 (24) : 3899 - 3914
  • [15] Wrappers for feature subset selection
    Kohavi, R
    John, GH
    [J]. ARTIFICIAL INTELLIGENCE, 1997, 97 (1-2) : 273 - 324
  • [16] Machine learning in bioinformatics
    Larranaga, Pedro
    Calvo, Borja
    Santana, Roberto
    Bielza, Concha
    Galdiano, Josu
    Inza, Inaki
    Lozano, Jose A.
    Armananzas, Ruben
    Santafe, Guzman
    Perez, Aritz
    Robles, Victor
    [J]. BRIEFINGS IN BIOINFORMATICS, 2006, 7 (01) : 86 - 112
  • [17] Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia
    Ley, Timothy J.
    Miller, Christopher
    Ding, Li
    Raphael, Benjamin J.
    Mungall, Andrew J.
    Robertson, A. Gordon
    Hoadley, Katherine
    Triche, Timothy J., Jr.
    Laird, Peter W.
    Baty, Jack D.
    Fulton, Lucinda L.
    Fulton, Robert
    Heath, Sharon E.
    Kalicki-Veizer, Joelle
    Kandoth, Cyriac
    Klco, Jeffery M.
    Koboldt, Daniel C.
    Kanchi, Krishna-Latha
    Kulkarni, Shashikant
    Lamprecht, Tamara L.
    Larson, David E.
    Lin, Ling
    Lu, Charles
    McLellan, Michael D.
    McMichael, Joshua F.
    Payton, Jacqueline
    Schmidt, Heather
    Spencer, David H.
    Tomasson, Michael H.
    Wallis, John W.
    Wartman, Lukas D.
    Watson, Mark A.
    Welch, John
    Wendl, Michael C.
    Ally, Adrian
    Balasundaram, Miruna
    Birol, Inanc
    Butterfield, Yaron
    Chiu, Readman
    Chu, Andy
    Chuah, Eric
    Chun, Hye-Jung
    Corbett, Richard
    Dhalla, Noreen
    Guin, Ranabir
    He, An
    Hirst, Carrie
    Hirst, Martin
    Holt, Robert A.
    Jones, Steven
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2013, 368 (22) : 2059 - 2074
  • [18] HIV-Tat protein transduction domain specifically attenuates growth of polyamine deprived tumor cells
    Mani, Katrin
    Sandgren, Staffan
    Lilja, Johanna
    Cheng, Fang
    Svensson, Katrin
    Persson, Lo
    Belting, Mattias
    [J]. MOLECULAR CANCER THERAPEUTICS, 2007, 6 (02) : 782 - 788
  • [19] Meyer D., SUPPORT VECTOR MACHI
  • [20] minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information
    Meyer, Patrick E.
    Lafitte, Frederic
    Bontempi, Gianluca
    [J]. BMC BIOINFORMATICS, 2008, 9 (1)