Understanding gene expression in coronary artery disease through global profiling, network analysis and independent validation of key candidate genes

Molecular mechanism underlying the patho-physiology of coronary artery disease (CAD) is complex. We used global expression profiling combined with analysis of biological network to dissect out potential genes and pathways associated with CAD in a representative case–control Asian Indian cohort. We i...

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Bibliographic Details
Published in:Journal of genetics 2015-12, Vol.94 (4), p.601-610
Main Authors: ARVIND, PRATHIMA, JAYASHREE, SHANKER, JAMBUNATHAN, SRIKARTHIKA, NAIR, JINY, KAKKAR, VIJAY V.
Format: Article
Language:English
Subjects:
Animal Genetics and Genomics
Biomedical and Life Sciences
Cardiovascular disease
Case-Control Studies
Computational linguistics
Coronary Artery Disease - genetics
Coronary heart disease
Ethylenediaminetetraacetic acid
Evolutionary Biology
Female
Gene expression
Gene Expression - genetics
Gene Expression Profiling - methods
Genes
Genetic aspects
Genetics
Humans
Language processing
Life Sciences
Male
Microbial Genetics and Genomics
Middle Aged
Natural language interfaces
Plant Genetics and Genomics
Research Article
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Summary:Molecular mechanism underlying the patho-physiology of coronary artery disease (CAD) is complex. We used global expression profiling combined with analysis of biological network to dissect out potential genes and pathways associated with CAD in a representative case–control Asian Indian cohort. We initially performed blood transcriptomics profiling in 20 subjects, including 10 CAD patients and 10 healthy controls on the Agilent microarray platform. Data was analysed with Gene Spring Gx12.5, followed by network analysis using David v 6.7 and Reactome databases. The most significant differentially expressed genes from microarray were independently validated by real time PCR in 97 cases and 97 controls. A total of 190 gene transcripts showed significant differential expression (fold change >2, P 8-fold higher expression in cases relative to the controls implying their important role in CAD. In conclusion, global gene expression profiling combined with network analysis can help in identifying key genes and pathways for CAD.
ISSN:0022-1333
0973-7731
DOI:10.1007/s12041-015-0548-3