ArticleMolecular medicine reports2018
Identification of potential genes and miRNAs associated with sepsis based on microarray analysis.
Article in Molecular medicine reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Psoriasis-Obesity-Related Gene COX7C Promotes Keratinocyte Proliferation and Positively Regulates Inflammatory Responses in Psoriasis.Journal of cellular and molecular medicine · 2026Article
- Identification and validation of MYC as an autophagy-related gene for diagnosis and immune infiltration in sepsis-induced myocardial dysfunction.Journal of cardiothoracic surgery · 2026Article
- Genome-wide identification and functional analysis of dysregulated alternative splicing profiles in sepsis.Journal of inflammation (London, England) · 2023Article
- Fast Track Diagnostic Tools for Clinical Management of Sepsis: Paradigm Shift from Conventional to Advanced Methods.Diagnostics (Basel, Switzerland) · 2023Review
- Liver proteomic analysis reveals the key proteins involved in host immune response to sepsis.PeerJ · 2023Article
- Identification and validation of key biomarkers based on RNA methylation genes in sepsis.Frontiers in immunology · 2023Article
- TheInfection and drug resistance · 2021Article
- MYC Participates in Lipopolysaccharide-Induced Sepsis via Promoting Cell Proliferation and Inhibiting Apoptosis.Cell journal · 2020Article
- Bioinformatics Analysis for Multiple Gene Expression Profiles in Sepsis.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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