Literature DB >> 33827531

Identification of hub genes related to the progression of type 1 diabetes by computational analysis.

G Prashanth1, Basavaraj Vastrad2, Anandkumar Tengli3, Chanabasayya Vastrad4, Iranna Kotturshetti5.   

Abstract

BACKGROUND: Type 1 diabetes (T1D) is a serious threat to childhood life and has fairly complicated pathogenesis. Profound attempts have been made to enlighten the pathogenesis, but the molecular mechanisms of T1D are still not well known.
METHODS: To identify the candidate genes in the progression of T1D, expression profiling by high throughput sequencing dataset GSE123658 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and gene ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI), modules, target gene - miRNA regulatory network and target gene - TF regulatory network analysis were constructed and analyzed using HIPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, validation of hub genes was conducted by using ROC (Receiver operating characteristic) curve and RT-PCR analysis. A molecular docking study was performed.
RESULTS: A total of 284 DEGs were identified, consisting of 142 up regulated genes and 142 down regulated genes. The gene ontology (GO) and pathways of the DEGs include cell-cell signaling, vesicle fusion, plasma membrane, signaling receptor activity, lipid binding, signaling by GPCR and innate immune system. Four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell-cell signaling, cytokine signaling in immune system, signaling by GPCR and innate immune system. ROC curve and RT-PCR analysis showed that EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN might be involved in the advancement of T1D. Molecular docking studies showed high docking score.
CONCLUSIONS: DEGs and hub genes identified in the present investigation help us understand the molecular mechanisms underlying the advancement of T1D, and provide candidate targets for diagnosis and treatment of T1D.

Entities:  

Keywords:  bioinformatics; differentially expressed genes; enrichment analysis; pathways; type 1 diabetes

Year:  2021        PMID: 33827531     DOI: 10.1186/s12902-021-00709-6

Source DB:  PubMed          Journal:  BMC Endocr Disord        ISSN: 1472-6823            Impact factor:   2.763


  76 in total

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Journal:  Can J Diabetes       Date:  2016-08-18       Impact factor: 4.190

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  1 in total

1.  Epigenetic and transcriptomic alterations in offspring born to women with type 1 diabetes (the EPICOM study).

Authors:  Sine Knorr; Anne Skakkebæk; Jesper Just; Emma B Johannsen; Christian Trolle; Søren Vang; Zuzana Lohse; Birgitte Bytoft; Peter Damm; Kurt Højlund; Dorte M Jensen; Claus H Gravholt
Journal:  BMC Med       Date:  2022-09-23       Impact factor: 11.150

  1 in total

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