Hub genes, key miRNAs and interaction analyses in type 2 diabetes mellitus: an integrative in silico approach.

MAPK10 MAPT PPI hub gene miR-16-5p type 2 diabetes

Journal

Integrative biology : quantitative biosciences from nano to macro
ISSN: 1757-9708
Titre abrégé: Integr Biol (Camb)
Pays: England
ID NLM: 101478378

Informations de publication

Date de publication:
23 Jan 2024
Historique:
received: 23 06 2023
revised: 20 10 2023
accepted: 18 12 2023
medline: 17 2 2024
pubmed: 17 2 2024
entrez: 17 2 2024
Statut: ppublish

Résumé

Diabetes is a rising global metabolic disorder and leads to long-term consequences. As a multifactorial disease, the gene-associated mechanisms are important to know. This study applied a bioinformatics approach to explore the molecular underpinning of type 2 diabetes mellitus through differential gene expression analysis. We used microarray datasets GSE16415 and GSE29226 to identify differentially expressed genes between type 2 diabetes and normal samples using R software. Following that, using the STRING database, the protein-protein interaction network was constructed and further analyzed by Cytoscape software. The EnrichR database was used for Gene Ontology and pathway enrichment analysis to explore key pathways and functional annotations of hub genes. We also used miRTarBase and TargetScan databases to predict miRNAs targeting hub genes. We identified 21 hub genes in type 2 diabetes, some showing more significant changes in the PPI network. Our results revealed that GLUL, SLC32A1, PC, MAPK10, MAPT, and POSTN genes are more important in the PPI network and can be experimentally investigated as therapeutic targets. Hsa-miR-492 and hsa-miR-16-5p are suggested for diagnosis and prognosis by targeting GLUL, SLC32A1, PC, MAPK10, and MAPT genes involved in the insulin signaling pathway. Insight: Type 2 diabetes, as a rising global and multifactorial disorder, is important to know the gene-associated mechanisms. In an integrative bioinformatics analysis, we integrated different finding datasets to put together and find valuable diagnostic and prognostic hub genes and miRNAs. In contrast, genes, RNAs, and enzymes interact systematically in pathways. Using multiple databases and software, we identified differential expression between hub genes of diabetes and normal samples. We explored different protein-protein interaction networks, gene ontology, key pathway analysis, and predicted miRNAs that target hub genes. This study reported 21 significant hub genes and some miRNAs in the insulin signaling pathway for innovative and potential diagnostic and therapeutic purposes.

Identifiants

pubmed: 38366952
pii: 7609541
doi: 10.1093/intbio/zyae002
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.

Auteurs

Zeinab Nematollahi (Z)

Royal Free Hospital, University College London, London, UK.

Shiva Karimian (S)

Electrical and Computer Research Center, Islamic Azad University Sanandaj Branch, Sanandaj, Iran.

Ali Taghavirashidizadeh (A)

Department of Electrical and Electronics Engineering, Islamic Azad University Central Tehran Branch, Tehran, Iran.

Mohammad Darvishi (M)

Infectious Diseases and Tropical Medicine Research Center(IDTMC), School of Aerospace and Subaquatic Medicine, AJA University of Medical Sciences, Tehran, Iran.

SeyedAbbas Pakmehr (S)

School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.

Amin Erfan (A)

Department of Electrical and Computer Engineering, Technical and Vocational University, Tehran, Iran.

Mohammad Javad Teimoury (MJ)

Department of Computer, Islamic Azad University Science and Research Branch, Tehran, Iran.

Neda Mansouri (N)

Instituto de Biología Molecular y Celular del Cáncer (CSIC-Universidad de Salamanca) IBSAL and CIBERONC, Salamanca, Spain.

Iraj Alipourfard (I)

Institute of Physical Chemistry, Polish Academy of Science, Warsaw, Poland.

Classifications MeSH