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Published ahead of print on June 15, 2007, doi:10.1164/rccm.200612-1819OC
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American Journal of Respiratory and Critical Care Medicine Vol 176. pp. 676-684, (2007)
© 2007 American Thoracic Society
doi: 10.1164/rccm.200612-1819OC


Original Article

The Use of Gene-Expression Profiling to Identify Candidate Genes in Human Sepsis

Benjamin M. P. Tang1, Anthony S. McLean1, Ian W. Dawes2, Stephen J. Huang1 and Ruby C. Y. Lin2

1 Department of Intensive Care Medicine, Nepean Hospital and Western Clinical School, University of Sydney, Penrith, Australia; and 2 Ramaciotti Centre for Gene Function Analysis and School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia

Correspondence and requests for reprints should be addressed to Ruby C. Y. Lin, Ph.D., Ramacotti Centre for Gene Function Analysis, School of Biotechnology and Biomolecular Sciences (D26), University of New South Wales, NSW 2052, Australia. E-mail: rubyl{at}unsw.edu.au

Rationale: Our understanding of the pathophysiology of sepsis remains incomplete. Genomewide study offers an unbiased, system biology approach to examine the expression patterns of circulating leukocytes and may reveal novel insights into the host response to sepsis.

Objectives: We examined whether gene-expression profiling of neutrophils could identify signature genes and important pathways in the clinical syndrome of sepsis.

Methods: Gene-expression profiling was performed using oligonucleotide microarrays on peripheral blood samples of 94 critically ill patients (71 septic and 23 nonseptic). Using a supervised learning algorithm based on support vector machine, a molecular signature of sepsis was generated from a training set of 44 samples and validated in an independent set of 50 samples. The diagnostic performance of the signature genes was assessed against a reference standard based on the International Sepsis Forum Consensus Conference definition of infection.

Measurements and Main Results: A set of 50 signature genes correctly identified sepsis with a prediction accuracy of 91 and 88% in the training and validation sets, respectively. The diagnostic performance remained high regardless of patient's age, comorbidities, or prior antibiotic treatment. Compared with controls, genes involved in immune modulation and inflammatory response had reduced expression in patients with sepsis. In particular, the activation of nuclear factor-{kappa}B pathway was reduced, whereas its inhibitor gene, NFKBIA, was significantly up-regulated.

Conclusions: The signature genes reflect suppression of neutrophils' immune and inflammatory function by sepsis. Gene-expression profiling therefore provides a novel approach to advance our understanding of the host response in sepsis.

Key Words: microarray analysis • sepsis syndrome


AT A GLANCE COMMENTARY

Scientific Knowledge on the Subject
Current methods to diagnose sepsis in critically ill patients lack sensitivity and specificity.

What This Study Adds to the Field
Gene-expression profiling represents a novel approach to diagnose sepsis. It also provides important biological insights into the host response to sepsis.

 

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Opening the Window on Genome-wide Expression Analyses in Sepsis
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AJRCCM 2007 176: 631-632. [Full Text]  



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