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Profiling Meta‐Analysis Reveals Primarily Gene Coexpression Concordance between Systemic Lupus Erythematosus and Rheumatoid Arthritis
Author(s) -
SILVA GUILHERME L.,
JUNTA CRISTINA M.,
MELLO STEPHANO S.,
GARCIA PAULA S.,
RASSI DIANE M.,
SAKAMOTOHOJO ELZA T.,
DONADI EDUARDO A.,
PASSOS GERALDO A. S.
Publication year - 2007
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.1423.005
Subject(s) - concordance , rheumatoid arthritis , medicine , gene expression profiling , profiling (computer programming) , meta analysis , systemic lupus erythematosus , immunology , gene , computational biology , biology , genetics , pathology , gene expression , disease , computer science , operating system
: Consensus gene expression profiling by meta‐analysis of 4,500 cDNA sequence microarray data obtained from patients with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) was assembled and systematically analyzed. The normalized data were statistically analyzed by the significance analysis of microarray (SAM) program (false discovery rate ≤ 0.01). Patient data input was realized together, in the Cluster and Tree‐View program, using the unsupervised function. Individual expression signatures permitted a hierarchical clustering of samples, separately identifying SLE and RA patients and the transcriptome profiling featured modules of the specifically induced or repressed and the comodulated genes. Gene expression profiling meta‐analysis showed that patients with SLE or RA share gene modulation but also present genes whose expression patterns are exclusive (induced or repressed) in corroboration with their clinical features. Among the genes that were differentially expressed, we found those that could play specific roles in these two diseases. This approach permits a clearer understanding of the molecular basis of SLE and RA concordance/divergence.