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A meta-analysis of genome-wide gene expression differences identifies promising targets for type 2 diabetes mellitus

AIMS/INTRODUCTION: Due to the heterogeneous nature of type 2 diabetes mellitus and its complex effects on hemodynamics, there is a need to identify new candidate markers which are involved in the development of type 2 diabetes mellitus (DM) and can serve as potential targets. As the global diabetes...

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Detalles Bibliográficos
Autores principales: Huang, Tao, Nazir, Bisma, Altaf, Reem, Zang, Bolun, Zafar, Hajra, Paiva-Santos, Ana Cláudia, Niaz, Nabeela, Imran, Muhammad, Duan, Yongtao, Abbas, Muhammad, Ilyas, Umair
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424486/
https://www.ncbi.nlm.nih.gov/pubmed/36051390
http://dx.doi.org/10.3389/fendo.2022.985857
Descripción
Sumario:AIMS/INTRODUCTION: Due to the heterogeneous nature of type 2 diabetes mellitus and its complex effects on hemodynamics, there is a need to identify new candidate markers which are involved in the development of type 2 diabetes mellitus (DM) and can serve as potential targets. As the global diabetes prevalence in 2019 was estimated as 9.3% (463 million people), rising to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045, the need to limit this rapid prevalence is of concern. The study aims to identify the possible biomarkers of type 2 diabetes mellitus with the help of the system biology approach using R programming. MATERIALS AND METHODS: Several target proteins that were found to be associated with the source genes were further curated for their role in type 2 diabetes mellitus. The differential expression analysis provided 50 differentially expressed genes by pairwise comparison between the biologically comparable groups out of which eight differentially expressed genes were short-listed. These DEGs were as follows: MCL1, PTX3, CYP3A4, PTGS1, SSTR2, SERPINA3, TDO2, and GALNT7. RESULTS: The cluster analysis showed clear differences between the control and treated groups. The functional relationship of the signature genes showed a protein–protein interaction network with the target protein. Moreover, several transcriptional factors such as DBX2, HOXB7, POU3F4, MSX2, EBF1, and E4F1 showed association with these identified differentially expressed genes. CONCLUSIONS: The study highlighted the important markers for diabetes mellitus that have shown interaction with other proteins having a role in the progression of diabetes mellitus that can serve as new targets in the management of DM.