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Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis

Objectives Adipocytes and adipocyte lipid metabolism are closely related with obesity and type 2 diabetes, but the molecular mechanism still needs further investigation. The aim of this study is to discover the adipocyte genes and pathways involved in obesity and type 2 diabetes using bioinformatics...

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Autores principales: Dong, Zhiyong, Lei, Xinyi, Kujawa, Stacy A., Bolu, NaciEmre, Zhao, Hong, Wang, Cunchuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183531/
https://www.ncbi.nlm.nih.gov/pubmed/34085602
http://dx.doi.org/10.1080/21623945.2021.1933297
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author Dong, Zhiyong
Lei, Xinyi
Kujawa, Stacy A.
Bolu, NaciEmre
Zhao, Hong
Wang, Cunchuan
author_facet Dong, Zhiyong
Lei, Xinyi
Kujawa, Stacy A.
Bolu, NaciEmre
Zhao, Hong
Wang, Cunchuan
author_sort Dong, Zhiyong
collection PubMed
description Objectives Adipocytes and adipocyte lipid metabolism are closely related with obesity and type 2 diabetes, but the molecular mechanism still needs further investigation. The aim of this study is to discover the adipocyte genes and pathways involved in obesity and type 2 diabetes using bioinformatics analysis. Methods The GSE27951 gene expression profile was obtained. Software and online tools (STRING, Cytoscape, BioGPS, CTD, and FunRich) were used to identify core genes.21 human subcutaneous adipose samples, with 10 from type 2 diabetic patients and 11 from normal controls, were included in these analyses. Results 184 differentially expressed genes (DEGs) including 42 up-regulated genes and 142 down-regulated genes were found to be enriched in metabolism, receptor activity, collagen type IV and glutamine biosynthesis I pathway by using the enrichment analysis. Seven hub genes were identified from the PPI network using various software (Cytoscape, STRING, BioGPS, and CTD). Four core genes (COL4A2, ACACB, GLUL, and CD36) were found to be highly expressed in subcutaneous adipose tissue of obese patients accompanying type 2 diabetes. Conclusion COL4A2, ACACB, GLUL and CD36 might be the core molecular biomarkers of obesity in patients with or without type 2 diabetes.
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spelling pubmed-81835312021-06-11 Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis Dong, Zhiyong Lei, Xinyi Kujawa, Stacy A. Bolu, NaciEmre Zhao, Hong Wang, Cunchuan Adipocyte Research Paper Objectives Adipocytes and adipocyte lipid metabolism are closely related with obesity and type 2 diabetes, but the molecular mechanism still needs further investigation. The aim of this study is to discover the adipocyte genes and pathways involved in obesity and type 2 diabetes using bioinformatics analysis. Methods The GSE27951 gene expression profile was obtained. Software and online tools (STRING, Cytoscape, BioGPS, CTD, and FunRich) were used to identify core genes.21 human subcutaneous adipose samples, with 10 from type 2 diabetic patients and 11 from normal controls, were included in these analyses. Results 184 differentially expressed genes (DEGs) including 42 up-regulated genes and 142 down-regulated genes were found to be enriched in metabolism, receptor activity, collagen type IV and glutamine biosynthesis I pathway by using the enrichment analysis. Seven hub genes were identified from the PPI network using various software (Cytoscape, STRING, BioGPS, and CTD). Four core genes (COL4A2, ACACB, GLUL, and CD36) were found to be highly expressed in subcutaneous adipose tissue of obese patients accompanying type 2 diabetes. Conclusion COL4A2, ACACB, GLUL and CD36 might be the core molecular biomarkers of obesity in patients with or without type 2 diabetes. Taylor & Francis 2021-06-04 /pmc/articles/PMC8183531/ /pubmed/34085602 http://dx.doi.org/10.1080/21623945.2021.1933297 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Paper
Dong, Zhiyong
Lei, Xinyi
Kujawa, Stacy A.
Bolu, NaciEmre
Zhao, Hong
Wang, Cunchuan
Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis
title Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis
title_full Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis
title_fullStr Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis
title_full_unstemmed Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis
title_short Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis
title_sort identification of core gene in obese type 2 diabetes patients using bioinformatics analysis
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183531/
https://www.ncbi.nlm.nih.gov/pubmed/34085602
http://dx.doi.org/10.1080/21623945.2021.1933297
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