ArticleFunctional & integrative genomics2022
Identification of the TF-miRNA-mRNA co-regulatory networks involved in sepsis.
Article in Functional & integrative genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Article
- Article
- New chalcone compound exhibits microrna-mediated anticancer properties in glioblastoma.PloS one · 2025Article
- miR-130a-3p Exacerbates Sepsis-Induced Cardiac Dysfunction by Promoting Inflammation and Targeting PDK4.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a life-threatening medical condition caused by a dysregulated host response to infection. Recent studies have found that the expression of miRNAs is associated with the pathogenesis of sepsis and septic shock. Our study aimed to reveal which miRNAs may be involved in the dysregulated immune response in sepsis and how these miRNAs interact with transcription factors (TFs) using a computational approach with in vitro validation studies. To determine the network of TFs, miRNAs, and target genes involved in sepsis, GEO datasets GSE94717 and GSE131761 were used to identify differentially expressed miRNAs and DEGs. TargetScan and miRWalk databases were used to predict biological targets that overlap with the identified DEGs of differentially expressed miRNAs. The TransmiR database was used to predict the differential miRNA TFs that overlap with the identified DEGs. The TF-miRNA-mRNA network was constructed and visualized. Finally, qRT-PCR was used to verify the expression of TFs and miRNA in HUVECs. Between the healthy and sepsis groups, there were 146 upregulated and 98 downregulated DEGs in the GSE131761 dataset, and there were 1 upregulated and 183 downregulated DEMs in the GSE94717 dataset. A regulatory network of the TF-miRna target genes was established. According to the experimental results, RUNX3 was found to be downregulated while MAPK14 was upregulated, which corroborates the result of the computational expression analysis. In a HUVECs model, miR-19b-1-5p and miR-5009-5p were found to be significantly downregulated. Other TFs and miRNAs did not correlate with our bioinformatics expression analysis. We constructed a TF-miRNA-target gene regulatory network and identified potential treatment targets RUNX3, MAPK14, miR-19b-1-5p, and miR-5009-5p. This information provides an initial basis for understanding the complex sepsis regulatory mechanisms.
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Registered trials
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