BACKGROUND: Sepsis-induced myopathy (SIM) is a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study aimed to use bioinformatics analysis to identify hub genes and molecular pathways involved in SIM, to identify potential diagnostic or therapeutic biomarkers.

MATERIAL AND METHODS: The Gene Expression Omnibus (GEO) database was used to acquire the GSE13205 expression profile. The differentially expressed genes (DEGs) in cases of SIM and healthy controls, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the limma R/Bioconductor software package and clusterProfiler package in R, respectively. The protein–protein interaction (PPI) network data of DEGs was retrieved using the STRING database and analyzed using the Molecular Complex Detection (MCODE) Cytoscape software plugin.

RESULTS: A total of 196 DEGs were obtained in SIM samples compared with healthy samples, including 93 upregulated genes. The DEGs were significantly upregulated in mineral absorption, and the interleukin-17 (IL-17) signaling pathway and 103 down-regulated genes were associated with control of the bile secretion signaling pathway. A protein–protein interaction (PPI) network was constructed with 106 nodes and 192 edges. The top two important clusters were selected from the PPI by MCODE analysis. There were 16 hub genes with a high degree of connectivity in the PPI network that were selected, including heme oxygenase 1 (HMOX1), nicotinamide adenine dinucleotide phosphate quinone dehydrogenase 1 (NQO1), and metallothionein (MT)-1E.

CONCLUSIONS: Bioinformatics network analysis identified key hub genes and molecular mechanisms in SIM.

Keywords: Gene Expression Profiling, Intensive Care Units, Sepsis, Cluster Analysis, Computational Biology, Down-Regulation, gene ontology, Gene Regulatory Networks, Muscular Diseases, Protein Interaction Maps