Dataset Information

Key regulatory genes and signaling pathways involved in islet culture: a bioinformatic analysis.

ABSTRACT: Type 1 diabetes (T1D) is characterized by non-ideal mass and low survival rate of islets. Therefore, it is necessary to find intrinsic factors that prolong the survival of islets. This study aimed to track out hub genes and pathways in the process of islet culture by bioinformatic analysis. We downloaded the gene expression microarray of GSE42591 from the Gene Expression Omnibus (GEO). Aberrant Differentially methylated genes (DMGs) were obtained using the GEO2R tool. Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses were performed on selected genes by using the Database for Annotation Visualization and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed with the Retrieval of Interacting Genes (STRING) and visualized in Cytoscape 3.7.2. A total of 434 genes were overexpressed and 114 genes underexpressed in fresh to cultured 4 h tissue. KEGG pathway enrichment analyses revealed the TGF-beta signaling pathway, MAPK signaling pathway, or VEGF signaling pathway. The genes FN1, MKI67, IGF1, MAPK14, COL1A1 might be involved in islet culture. In general, this work scrutinized islet culture-relevant knowledge and provided insight into the regulation and mediation of islet survival.

SUBMITTER: Shi J

PROVIDER: S-EPMC7868784 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Key regulatory genes and signaling pathways involved in islet culture: a bioinformatic analysis.

Shi Jing J Zhang Yong-Qun YQ Hao Dou-Dou DD Fu Su-Hong SH Meng Jin-Li JL

International journal of clinical and experimental pathology 20210201 2

Type 1 diabetes (T1D) is characterized by non-ideal mass and low survival rate of islets. Therefore, it is necessary to find intrinsic factors that prolong the survival of islets. This study aimed to track out hub genes and pathways in the process of islet culture by bioinformatic analysis. We downloaded the gene expression microarray of GSE42591 from the Gene Expression Omnibus (GEO). Aberrant Differentially methylated genes (DMGs) were obtained using the GEO2R tool. Gene ontology (GO) analysis ...[more]

PMID: 33564361

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Project description:Psoriasis is an immune‑mediated cutaneous disorder with a high incidence and prevalence. Patients with psoriasis may experience irritation, pain and psychological problems. The cause and underlying molecular etiology of psoriasis remains unknown. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of psoriasis, the gene expression profiles of 175 pairs of lesional and corresponding non‑lesional skin samples were downloaded from 5 data sets in the Gene Expression Omnibus (GEO) database. Integrated differentially expressed genes (DEGs) were obtained with the use of R software. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using the DAVID online analysis tool. The protein‑protein interaction (PPI) network was constructed on the STRING platform and hub genes were calculated with the use of Cytoscape software. Finally, GEO2R was used to determine the expression of the hub genes in scalp psoriasis. A total of 373 genes from the 5 data sets were identified as DEGs, including 277 upregulated and 96 downregulated genes. GO analysis revealed that immune responses and epidermal differentiation/development were the most enriched terms in biological processes, extracellular space/matrix was the most enriched term in cellular components, and endopeptidase inhibitor activity was the most enriched term in molecular functions. In the KEGG pathway enrichment, DEGs were mainly enriched in the metabolic and viral infection‑associated pathways. A total of 17 hub genes were calculated, including CSK2, CDC45, MCM10, SPC25, NDC80, NUF2, AURKA, CENPE, RRM2, DLGP5, HMMR, TTK, IFIT1, RSAD2, IFI6, IFI27 and ISG20, among which interferon‑α‑inducible genes were revealed to display a similar expression pattern as that obtained in scalp psoriasis. This comprehensive bioinformatic re‑analysis of GEO data provides new insights on the molecular pathogenesis of psoriasis and the identification of potential therapeutic targets for the treatment of psoriasis.

Dataset Information

Key regulatory genes and signaling pathways involved in islet culture: a bioinformatic analysis.

Publications

Key regulatory genes and signaling pathways involved in islet culture: a bioinformatic analysis.

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