ArticleBMC medical genomics2021
Integrated bioinformatics analysis of core regulatory elements involved in keloid formation.
Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Deciphering Pain and Pruritus in Keloids from the Perspective of Neurological Dysfunction: Where Are We Now?Biomedicines · 2025Article
- Identification of potential therapeutic target SPP1 and related RNA regulatory pathway in keloid based on bioinformatics analysis.Annals of medicine · 2024Article
- Article
- Profibrotic role of transcription factor SP1 in cross-talk between fibroblasts and M2 macrophages.iScience · 2023Article
- Bioinformatics analysis and identification of dysregulated POSTN in the pathogenesis of keloid.International wound journal · 2023Article
- Construction of a HOXA11-AS-Interact Ed Network in Keloid Fibroblasts Using Integrated Bioinformatic Analysis andFrontiers in genetics · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKeloid is a benign fibro-proliferative dermal tumor formed by an abnormal scarring response to injury and characterized by excessive collagen accumulation and invasive growth. The mechanism of keloid formation has not been fully elucidated, especially during abnormal scarring. Here, we investigated the regulatory genes, micro-RNAs (miRNAs) and transcription factors (TFs) that influence keloid development by comparing keloid and normal scar as well as keloid and normal skin.
methodsGene expression profiles (GSE7890, GSE92566, GSE44270 and GSE3189) of 5 normal scar samples, 10 normal skin samples and 18 keloid samples from the Gene Expression Omnibus (GEO) database were interrogated. Differentially expressed genes (DEGs) were identified between keloid and normal skin samples as well as keloid and normal scar samples with R Project for Statistical Computing. Gene Ontology (GO) functional enrichment analysis was also performed with R software. DEG-associated protein-protein interaction (PPI) network was constructed by STRING, followed by module selection from the PPI network based on the MCODE analysis. Regulatory relationships between TF/miRNA and target genes were predicted with miRnet and cytoscape. Core regulatory genes were verified by RT-qPCR.
resultsWe identified 628 DEGs, of which 626 were up-regulated and 2 were down-regulated. Seven core genes [neuropeptide Y(NPY), 5-hydroxytryptamine receptor 1A(HTR1A), somatostatin (SST), adenylate cyclase 8 (ADCY8), neuromedin U receptor 1 (NMUR1), G protein subunit gamma 3 (GNG3), and G protein subunit gamma 13 (GNG13)] all belong to MCODE1 and were enriched in the "G protein coupled receptor signaling pathway" of the GO biological process category. Furthermore, nine core miRNAs (hsa-mir-124, hsa-let-7, hsa-mir-155, hsa-mir-26a, hsa-mir-941, hsa-mir-10b, hsa-mir-20, hsa-mir-31 and hsa-mir-372), and two core TFs (SP1 and TERT) were identified to play important roles in keloid formation. In the TF/miRNA-target gene network, both hsa-mir-372 and hsa-mir-20 had a regulatory effect on GNG13, ADCY8 was predicted to be target by hsa-mir-10b, and HTR1A and NPY were potentially by SP1. Furthermore, the expression of core regulatory genes (GNG13, ADCY8, HTR1A and NPY) was validated in clinical samples.
conclusionsGNG13, ADCY8, NPY and HTR1A may act as core genes in keloid formation and these core genes establish relationship with SP1 and miRNA (hsa-mir-372, hsa-mir-20, hsa-mir-10b), which may influence multiple signaling pathways in the pathogenesis of keloid.
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