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Analysis of Significance Patterns Identifies Ubiquitous and Disease‐Specific Gene‐Expression Signatures in Patient Peripheral Blood Leukocytes
Author(s) -
CHAUSSABEL DAMIEN,
ALLMAN WINDY,
MEJIAS ASUNCION,
CHUNG WENDY,
BENNETT LYNDA,
RAMILO OCTAVIO,
PASCUAL VIRGINIA,
PALUCKA A. KAROLINA,
BANCHEREAU JACQUES
Publication year - 2005
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1196/annals.1358.017
Subject(s) - context (archaeology) , gene signature , peripheral blood mononuclear cell , disease , dna microarray , pathogenesis , peripheral blood , gene expression , immunology , gene expression profiling , gene , microarray , biology , identification (biology) , medicine , bioinformatics , pathology , genetics , in vitro , paleontology , botany
A bstract : The utilization of gene‐expression microarrays in patient‐based research creates new prospects for the discovery of diagnostic biomarkers and the identification of genes or pathways linked to pathogenesis. Gene‐expression signatures in peripheral blood mononuclear cells isolated from over one hundred patients with conditions presenting a strong immunological component (patient with autoimmune, graft versus host and infectious diseases, as well as immunosuppressed transplant recipients) were generated. This dataset provides the opportunity to carry out comparative analyses and define disease signatures in a broader context. Transcriptional changes of 22,283 probe sets were evaluated through statistical group comparison performed systematically for seven diseases versus their respective healthy control group. Patterns of significance were generated by hierarchical clustering of P ‐values. This approach led to the identification of a SLE‐specific “diagnostic signature,” formed by genes that did not change compared to healthy subjects in the other six diseases. Conversely, a “sentinel signature” that was common to all seven diseases was characterized. These findings bring new perspectives for the application of blood leukocyte expression signatures for diagnosis and early disease detection.