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

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 o...

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Autores principales: Prashanth, G., Vastrad, Basavaraj, Tengli, Anandkumar, Vastrad, Chanabasayya, Kotturshetti, Iranna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028841/
https://www.ncbi.nlm.nih.gov/pubmed/33827531
http://dx.doi.org/10.1186/s12902-021-00709-6
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author Prashanth, G.
Vastrad, Basavaraj
Tengli, Anandkumar
Vastrad, Chanabasayya
Kotturshetti, Iranna
author_facet Prashanth, G.
Vastrad, Basavaraj
Tengli, Anandkumar
Vastrad, Chanabasayya
Kotturshetti, Iranna
author_sort Prashanth, G.
collection PubMed
description 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.
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spelling pubmed-80288412021-04-09 Identification of hub genes related to the progression of type 1 diabetes by computational analysis Prashanth, G. Vastrad, Basavaraj Tengli, Anandkumar Vastrad, Chanabasayya Kotturshetti, Iranna BMC Endocr Disord Research Article 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. BioMed Central 2021-04-07 /pmc/articles/PMC8028841/ /pubmed/33827531 http://dx.doi.org/10.1186/s12902-021-00709-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Prashanth, G.
Vastrad, Basavaraj
Tengli, Anandkumar
Vastrad, Chanabasayya
Kotturshetti, Iranna
Identification of hub genes related to the progression of type 1 diabetes by computational analysis
title Identification of hub genes related to the progression of type 1 diabetes by computational analysis
title_full Identification of hub genes related to the progression of type 1 diabetes by computational analysis
title_fullStr Identification of hub genes related to the progression of type 1 diabetes by computational analysis
title_full_unstemmed Identification of hub genes related to the progression of type 1 diabetes by computational analysis
title_short Identification of hub genes related to the progression of type 1 diabetes by computational analysis
title_sort identification of hub genes related to the progression of type 1 diabetes by computational analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028841/
https://www.ncbi.nlm.nih.gov/pubmed/33827531
http://dx.doi.org/10.1186/s12902-021-00709-6
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