Caixia Wang; Shaoyong Luan; Ming Li; Ruiyun Zhang; Xiuxia Chen
Volume 19, Issue 3 , March 2017, , Pages 1-7
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 ...
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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 ofInteracting 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
Zhili Zhang; Xia Yan; Jingqin Jiang
Volume 19, Issue 3 , March 2017, , Pages 1-8
Abstract
Background: Asthmatic chronic rhinosinusitis with nasal polyps (aCRSwNP) is a common disruptive eosinophilic disease. However, up to now, there is no effective medical treatment for the disease, which is partly due to that the molecular mechanism of aCRSwNP is still unknown.Objectives: The aim of this ...
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Background: Asthmatic chronic rhinosinusitis with nasal polyps (aCRSwNP) is a common disruptive eosinophilic disease. However, up to now, there is no effective medical treatment for the disease, which is partly due to that the molecular mechanism of aCRSwNP is still unknown.Objectives: The aim of this study was to facilitate the systematic discovery of diagnostic biomarkers of aCRSwNP based on integrating pathways, differentially expressed genes (DEGs), and mutual information networks (MINs).Methods: This was a foundation-application study carried out in Dongying, Shandong Province, P.R. China, in 2016. First, the gene expression profile of aCRSwNP composed of 13 normal samples and 21 aCRSwNP samples was recruited from the gene expression omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) and then, data preprocessing was performed. Second, the attract method was utilized to identify differential pathways. In the following, MINs were constructed and underwent topological analysis. Then, DEGs were examined in aCRSwNP group and normal control group to identify significant genes and key genes. Finally, the support vector machine (SVM) with C-classification was utilized to evaluate the performance of the classification.Results: A total of 11,100 genes and 273 pathways (gene count > 5) were initially obtained. Then, 5 differential pathways which contained 346 genes were identified. Topological analysis conducted on the MINs revealed 20 hub genes (degree centrality ≥ 220). In the following, 795 DEGs were identified (|log fold change (FC)|≥ 2.0, P value≤0.01). Furthermore, 35 significant genes and 14 key genes were detected. Finally, the results of SVM with C-classification indicated that the key genes gave the best result.Conclusions: Our research identified several key genes (such as IL6R), which might play key roles in the occurrence and development of aCRSwNP. We predicted that these genes might provide additional diagnostic and therapeutic targets for aCRSwNP.
