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

• Published in: American journal of respiratory and critical care medicine Vol. 176; no. 7; pp. 676 - 684
• Main Authors: Tang, Benjamin M. P, McLean, Anthony S, Dawes, Ian W, Huang, Stephen J, Lin, Ruby C. Y
• Format: Journal Article
• Language: English
• Published: New York, NY Am Thoracic Soc 01.10.2007; American Lung Association; American Thoracic Society
• Subjects: Aged; Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Antibiotics; Biological and medical sciences; Biomarkers; Blood and lymphatic vessels; Cardiology. Vascular system; Diseases of the peripheral vessels. Diseases of the vena cava. Miscellaneous; Emergency and intensive care: infection, septic shock; Enrollments; Female; Gene Expression Profiling; Genes; Genomes; Hospitals; Humans; Infections; Intensive care; Intensive care medicine; Leukocytes; Male; Medical records; Medical sciences; Middle Aged; Neutrophils; Oligonucleotide Array Sequence Analysis; Patients; Sepsis; Sepsis - diagnosis; Sepsis - genetics; Human; Infection; Sepsis syndrome; Intensive care; microarray analysis; Gene expression; Resuscitation
• ISSN: 1073-449X; 1535-4970
• DOI: 10.1164/rccm.200612-1819OC

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. We examined whether gene-expression profiling of neutrophils could identify signature genes and important pathways in the clinical syndrome of sepsis. 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. 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-kappaB pathway was reduced, whereas its inhibitor gene, NFKBIA, was significantly up-regulated. 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.