Title of article :
Determination of Biomarkers for Neonatal Sepsis Based on Differential
Modules
Author/Authors :
Li Ming نويسنده , Wang Caixia نويسنده Department of Pediatrics, Qingdao Municipal Hospital,
Qingdao 266011, PR China , Luan Shaoyong نويسنده Department of Pediatrics, Qingdao Municipal Hospital,
Qingdao 266011, PR China , Zhang Ruiyun نويسنده Department of Pediatrics, Qingdao Municipal Hospital,
Qingdao 266011, PR China , Chen Xiuxia نويسنده Department of Pediatrics, Qingdao Municipal Hospital,
Qingdao 266011, PR China
Abstract :
Background The exact interacting factor that response to the
infection for neonatal sepsis is still needed to urgently to be
disclosed. Objectives This research was aimed to explore the potential
biomarkers and illuminate the underlying molecular mechanisms associated
with neonatal sepsis via identifying differential modules (DMs). Methods
This is a case-control bioinformatics analysis using already published
microarray data of neonatal sepsis. This study was conducted in Qingdao,
China from September 2015 to May 2016. We recruited the gene expression
profile of neonatal sepsis from the Array Express database
(http://www.ebi.ac.uk/arrayexpress) under the accessing number of
E-GEOD-25504, which included 27 neonatal samples with a confirmed blood
culture-positive test for sepsis (bacterial infected cases) as well as
35 matched controls. Meanwhile, the human protein-protein interaction
(PPI) data was collected from the database of Search Tool for the
Retrieval of Interacting Genes/Proteins (STRING, http://string-db.org).
All of the data was preprocessed. Then, the differential co-expression
network (DCN) was constructed by integrating co-expression analysis and
differential expression analysis. Next, a systemic module searching
strategy, which contained seed genes selection, module searching and
refinement of modules, was performed by select DMs. Results Starting
from the gene expression data and PPI data, the DCN that included 430
edges (covering 324 nodes) was constructed, in which each edge was
assigned a weight value. From the DCN, we selected a total of 16 seed
genes. Starting from these seed genes, a total of 3 modules were
identified from the DCN based on the systemic module algorithm. Of them,
only one module (Module 3) was considered as DM under P < 0.05.
This DM was involved in the progress of ribosome biogenesis in
eukaryotes. Conclusions In the present study, we identified a key gene
RPS16 and a significant module involved in ribosome biogenesis in
eukaryotes that were related to neonatal sepsis, which might be
potential biomarkers for early detection and therapy for neonatal
sepsis.
Journal title :
Astroparticle Physics