Identification of potential biomarkers of sepsis using bioinformatics analysis.
Experimental and therapeutic medicine, June 1, 2017
Sepsis is defined as the systemic inflammatory response to infection and is one of the leading causes of mortality in critically ill patients. The goal of the present study is to elucidate the molecular mechanism of sepsis. Transcription profile data (GSE12624) were downloaded that had a total of 70 samples (36 sepsis samples and 34 non-sepsis samples) from the Gene Expression Omnibus database. Protein-protein interaction network analysis was conducted in order to comprehensively understand the interactions of genes in all samples. Hierarchical clustering and analysis of covariance (ANCOVA) global test were performed to identify the differentially expressed clusters in the networks, followed by function and pathway enrichment analyses. Finally, a support vector machine (SVM) was performed to classify the clusters, and 10-fold cross-validation method was performed to evaluate the classification results. A total of 7,672 genes were obtained after preprocessing of the mRNA expression profile data. The PPI network of genes under sepsis and non-sepsis status collected 1,996/2,147 genes and 2,645/2,783 interactions. Moreover, following the ANCOVA global test (P
Pubmed Link: 28565754