Characteristic bioanalysis of skeletal muscle cells gene markers in septic patients.
10.3760/cma.j.issn.2095-4352.2019.03.007
- Author:
Kuo TIAN
1
;
Jianjian WANG
;
Peifang LIU
;
Huixue ZHANG
;
Xiaoyu LU
;
Chen XU
Author Information
1. Stroke Center, the Second Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang, China. Corresponding author: Wang Lihua, Email: wanglh211@163.com.
- Publication Type:Journal Article
- MeSH:
Computational Biology;
Gene Expression Profiling;
Gene Ontology;
Gene Regulatory Networks;
Genetic Markers;
Humans;
Muscle, Skeletal/metabolism*;
Sepsis/genetics*
- From:
Chinese Critical Care Medicine
2019;31(3):293-297
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To analyze the characteristics of skeletal muscle cells gene markers in septic patients by using bioinformatics.
METHODS:The differential gene expression of marker microarrays (GSE13205) in skeletal muscle tissue of patients with sepsis was obtained from gene expression omnibus (GEO) database of National Center for Biotechnology Information (NCBI). Gene differential expression analysis was carried out using online GEO2R provided by NCBI. Data processing, analysis and mapping were carried out using online bioinformatics array research tool (BART) and Cytoscpe software, the software of the national resource for network biology. Functional annotation and pathway analysis of differential expression genes were performed using Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) provided by the database for annotation, visualization and integrated discovery (DAVID), and protein interaction analysis was further performed in search tool for the retrieve of interacting genes/proteins (STRING-DB).
RESULTS:The TOP250 genes were extracted from the GSE13205 dataset. A total of 242 differentially expressed genes were included in the analysis. Among them, 78 up-regulated genes and 164 down-regulated genes were identified. After extensive data analysis, these differentially expressed genes were enriched into different biological processes or subsets of molecular functions, mainly enriched in the positive and negative regulation of growth, mineral absorption and other pathways. The 14 most closely related genes among differentially expressed genes were identified from the protein interaction network.
CONCLUSIONS:The differential expression genes in patients with sepsis were mainly concentrated on cell growth and apoptosis and mediating tumor-related immune function regulation.
