Identification of potential genes and miRNAs associated with sepsis based on microarray analysis

Abstract

Sepsis is a type of systemic inflammatory response syndrome caused by infection. The present study aimed to examine key genes and microRNAs (miRNAs) involved in the pathogenesis of sepsis. The GSE13205 microarray dataset, downloaded from the Gene Expression Omnibus was analyzed using bioinformatics tools, and included muscle biopsy specimens of 13 patients with sepsis and eight healthy controls. The differentially expressed genes (DEGs) in samples from patients with sepsis were identified using the Linear Models for Microarray package in R language. Using the Database for Annotation, Visualization and Integration Discovery tool, functional and pathway enrichment analyses were performed to examine the potential functions of the DEGs. The protein‑protein interaction (PPI) network was constructed with the DEGs using the Search Tool for the Retrieval of Interacting Genes, and the network topology was analyzed using CytoNCA. Subsequently, MCODE in Cytoscape was used to identify modules in the PPI network. Finally, the integrated regulatory network was constructed based on the DEGs, miRNAs and transcription factors (TFs). A total of 259 upregulated DEGs (MYC and BYSL) and 204 downregulated DEGs were identified in the patients with sepsis. NOP14, NOP2, AATF, GTPBP4, BYSL and TRMT6 were key genes in the MCODE module. In the integrated DEG‑miRNA‑TF regulatory network, hsa‑miR‑150 (target gene MYLK3) and 21 TFs, comprising 14 upregulated DEGs (including MYC) and seven downregulated DEGs, were identified. The results suggested that NOP14, NOP2, AATF, GTPBP4, BYSL, MYC, MYLK3 and miR‑150 may be involved in the pathogenesis of sepsis.

Keywords: sepsis; microRNA; arget gene; rotein‑protein interaction; integrated regulatory network.

Figure 1.

Heatmap of clustering analysis. The heatmap showed all significant DEGs (|log fold change| >1 and P<0.05) identified from the comparison of samples from patients with sepsis, vs. those from healthy controls. GSM333436-GSM333448 (purple) indicates the sepsis group and GSM333449-GSM333456 (brown) indicates the healthy control group. The red color indicates upregulated DEGs and the green color indicates downregulated DEGs, compared with corresponding samples from healthy controls; DEGs, differentially expressed genes.

Figure 2.

PPI network of DEGs. All upregulated and downregulated DEGs were used to construct a PPI network. Orange nodes represent upregulated DEGs and green nodes represent downregulated DEGs. The edges between them represent PPIs. DEGs, differentially expressed genes; PPI, protein-protein interaction.

Figure 3.

GO-enriched terms in the MCODE module. The module contains the highest number of genes (15 upregulated differentially expressed genes) of all modules identified via MCODE from the protein-protein interaction network and has the highest MCODE score (14.286). The yellow color indicates GO terms with a significant difference; white indicates GO terms for which the difference is not significant. Larger nodes and darker colors represent higher degrees of difference. The arrows indicate that the hierarchical structure of GO terms. GO, Gene Ontology.

Figure 4.

Genes in the top five terms in the MCODE module. Orange indicates upregulated DEGs, blue indicates biogenesis of the ribonucleoprotein complex, red indicates biogenesis of ribosomes, green indicates biogenesis of cellular components, yellow indicates ncRNA metabolic processes, purple indicates ncRNA processing. The lines indicate there are interacted with the two nodes in the network.

Figure 5.

Integrated regulatory network. Circles represent DEGs; orange represents upregulated DEGs and green represents downregulated DEGs. The triangles represent upregulated TFs, inverted triangles represent downregulated TFs and diamonds represent microRNAs. Lines without arrows indicate regulation associations of microRNAs. The arrowed lines indicate that the regulation associations of TFs. DEGs, differentially expressed genes; TFs, transcription factors; miR, microRNA.