title: Gene Network for Identifying the Entropy Changes of Different Modules in Pediatric Sepsis.
abstract: OBJECTIVE
  Pediatric sepsis is a disease that threatens life of children. The incidence of pediatric sepsis is higher in developing countries due to various reasons, such as insufficient immunization and nutrition, water and air pollution, etc. Exploring the potential genes via different methods is of significance for the prevention and treatment of pediatric sepsis. This study aimed to identify potential genes associated with pediatric sepsis utilizing analysis of gene network and entropy.
  
  
  METHODS
  The mRNA expression in the blood samples collected from 20 septic children and 30 healthy controls was quantified by using Affymetrix HG-U133A microarray. Two condition-specific protein-protein interaction networks (PINs), one for the healthy control and the other one for the children with sepsis, were deduced by combining the fundamental human PINs with gene expression profiles in the two phenotypes. Subsequently, distinct modules from the two conditional networks were extracted by adopting a maximal clique-merging approach. Delta entropy (ΔS) was calculated between sepsis and control modules.
  
  
  RESULTS
  Then, key genes displaying changes in gene composition were identified by matching the control and sepsis modules. Two objective modules were obtained, in which ribosomal protein RPL4 and RPL9 as well as TOP2A were probably considered as the key genes differentiating sepsis from healthy controls.
  
  
  CONCLUSIONS
  According to previous reports and this work, TOP2A is the potential gene therapy target for pediatric sepsis. The relationship between pediatric sepsis and RPL4 and RPL9 needs further investigation.
authors.affiliation: Pediatric Department, Qilu Hospital of Shandong University Qingdao, Qingdao, P.R. China.
authors.forename: Jing
authors.initials: J
authors.lastname: Yang
authors.forename: Pingli
authors.initials: P
authors.lastname: Zhang
authors.forename: Lumin
authors.initials: L
authors.lastname: Wang
mesh_terms: D002648:Child; D019277:Entropy; D053263:Gene Regulatory Networks; D006801:Humans; D025941:Protein Interaction Mapping; D018805:Sepsis / Q000235:genetics*