Research article
Identification of pediatric septic shock subclasses based on genome-wide expression profiling
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* Corresponding author: Hector R Wong hector.wong@cchmc.org
1 Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
2 Children's Hospital and Research Center Oakland, Oakland, CA, USA
3 The Children's Hospital of Philadelphia, Philadelphia, PA, USA
4 Children's Mercy Hospital, Kansas City, MO, USA
5 Penn State Children's Hospital, Hershey, PA, USA
6 University of Virginia, Charlottesville, VA, USA
7 Children's National Medical Center, Washington, DC, USA
8 Children's Hospital of Orange County, Orange, CA, USA
9 Miami Children's Hospital, Miami, FL, USA
10 St Louis Children's Hospital, St Louis, MO USA
11 C.S. Mott Children's Hospital at the University of Michigan, Ann Arbor, MI, USA
BMC Medicine 2009, 7:34 doi:10.1186/1741-7015-7-34
Published: 22 July 2009Abstract
Background
Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling.
Methods
Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization.
Results
Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the three putative subclasses (analysis of variance, Bonferonni correction, P < 0.05) identified 6,934 differentially regulated genes. K-means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the three subclasses. Leave one out cross-validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C.
Conclusion
Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.