scholarly journals Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis

2020 ◽  
Vol Volume 13 ◽  
pp. 1793-1801
Author(s):  
YiXuan Lin ◽  
Jinju Li ◽  
Di Wu ◽  
FanJing Wang ◽  
ZhaoHui Fang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Bioinformatics Analysis ◽  
Hub Genes

scholarly journals Bioinformatics Analysis of Key Genes and Pathways for Obesity Associated Type 2 Diabetes Mellitus as a Therapeutic Target

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Differentially Expressed Genes ◽  
Regulatory Network ◽  
Metabolic Disorder ◽  
Target Gene ◽  
Bioinformatics Analysis ◽  
Hub Genes

AbstractObesity associated type 2 diabetes mellitus is one of the most common metabolic disorder worldwide. The prognosis of obesity associated type 2 diabetes mellitus patients has remained poor, though considerable efforts have been made to improve the treatment of this metabolic disorder. Therefore, identifying significant differentially expressed genes (DEGs) associated in metabolic disorder advancement and exploiting them as new biomarkers or potential therapeutic targets for metabolic disorder is highly valuable. Differentially expressed genes (DEGs) were screened out from gene expression omnibus (GEO) dataset (GSE132831) and subjected to GO and REACTOME pathway enrichment analyses. The protein - protein interactions network, module analysis, target gene - miRNA regulatory network and target gene - TF regulatory network were constructed, and the top ten hub genes were selected. The relative expression of hub genes was detected in RT-PCR. Furthermore, diagnostic value of hub genes in obesity associated type 2 diabetes mellitus patients was investigated using the receiver operating characteristic (ROC) analysis. Small molecules were predicted for obesity associated type 2 diabetes mellitus by using molecular docking studies. A total of 872 DEGs, including 439 up regulated genes and 432 down regulated genes were observed. Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for obesity associated type 2 diabetes mellitus. The hub genes were validated in obesity associated type 2 diabetes mellitus. This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for obesity associated type 2 diabetes mellitus.

scholarly journals The Identification of Hub Genes and Pathways in type 2 Diabetes Mellitus by Bioinformatics Analysis

2020 ◽  
Author(s):  
Jing Li ◽  
Xinkui Jiang ◽  
Libo Chen ◽  
Hong Chen
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Metabolism Pathway ◽  
Core Genes

Abstract Background This study aimed to identify potential core genes and pathways involved in type 2 diabetes mellitus (T2DM) through exhaustive bioinformatics analysis. This study elucidated parts of the pathogenesis of T2DM and screened therapeutic targets of the treatment. Method: The original microarray data GSE25724 was downloaded from the Gene Expression Omnibus database. Data were processed by the limma package in R software and the differentially expressed genes(DEGs) were identified. Gene Ontology(GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were carried out to identify potential biological functions and pathways of the DEGs. The STRING(Search Tool for the Retrieval of Interacting Genes ) and Cytoscape software were used to establish a protein-protein interaction(PPI) network for the DEGs.Hub genes were identified using the PPI network.Results In total, 75 DEGs were involved in T2DM, with 1 up-regulated gene, and 74 down-regulated genes. GO enrichment analysis showed that DEGs mainly enriched in the regulation of hormone levels, unfolded protein binding.KEGG pathway enrichment analysis showed that DEGs were significantly enriched in the fatty acid metabolism pathway, propionate metabolism pathway, and degradation pathway of valine, leucine, and isoleucine. Furthermore,Neuroendocrine protein 7B2(SCG5),Synaptosomal-associated protein 25 (SNAP25), Sterol Carrier Protein 2 (SCP2), Carboxypeptidase E (CPE),and Protein Convertase Subtilisin/Kexin Type 1 (PCSK1) were the core genes in the PPI network.Conclusion This study identified 5 hub genes as potential biomarkers of type 2 diabetes through bioinformatics analysis, which might increase our understanding of the potential molecular mechanisms of T2DM and provided targets for further research.

scholarly journals Serum microRNA profiling and bioinformatics analysis of patients with type 2 diabetes mellitus in a Chinese population

2017 ◽  
Vol 15 (4) ◽  
pp. 2143-2153 ◽  
Author(s):  
Ze-Min Yang ◽  
Long-Hui Chen ◽  
Min Hong ◽  
Ying-Yu Chen ◽  
Xiao-Rong Yang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Chinese Population ◽  
Bioinformatics Analysis ◽  
Serum Microrna ◽  
Microrna Profiling

scholarly journals Investigation of Candidate Genes and Mechanisms Underlying Obesity Associated Type 2 Diabetes Mellitus Using Bioinformatics Analysis and Screening of Small Drug Molecules 

2020 ◽  
Author(s):  
Prashanth G ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Plasma Membrane ◽  
Pathway Analysis ◽  
Regulatory Network ◽  
Target Genes ◽  
Hub Genes ◽  
Drug Molecules

Abstract BackgroundObesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the development mechanism of obesity associated type 2 diabetes mellitus. MethodsTo screen the differentially expressed genes (DEGs) that may play essential roles in obesity associated type 2 diabetes mellitus, the public expression profiling by high throughput sequencing data (GSE143319) were downloaded and screened for DEGs. Then, Gene Ontology (GO) function analysis and REACTOME pathway analysis were performed. To screen hub and target genes, the protein–protein interaction network, miRNA-target genes regulatory network and TF-target gene regulatory network were constructed. The Receiver operating characteristic (ROC) curve analysis and RT- PCR analysis of hub genes in obesity associated type 2 diabetes mellitus were also analyzed. Final molecular docking studies performed for screening small drug molecules. ResultsThere were 409 up regulated and 411 down regulated genes detected, and the biological processes of the GO analysis were enriched in regulation of ion transmembrane transport, intrinsic component of plasma membrane, transferase activity, transferring phosphorus-containing groups, cell adhesion, integral component of plasma membrane and signaling receptor binding, whereas, the REACTOME pathway analysis was enriched in integration of energy metabolism and extracellular matrix organization. The hub genes CEBPD, TP73, ESR2, TAB1, MAP3K5, FN1, UBD, RUNX1, PIK3R2 and TNF, which might play a essential role in obesity associated type 2 diabetes mellitus was further screened. ConclusionsThe present study could deepen the understanding of the molecular mechanism of obesity associated type 2 diabetes mellitus, which could be useful in developing clinical treatments of obesity associated type 2 diabetes mellitus.

Exploring Shared Pathogenesis of Alzheimer’s Disease and Type 2 Diabetes Mellitus via Co-expression Networks Analysis

2020 ◽  
Vol 17 (6) ◽  
pp. 566-575 ◽  
Author(s):  
Yukun Zhu ◽  
Xuelu Ding ◽  
Zhaoyuan She ◽  
Xue Bai ◽  
Ziyang Nie ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Background: Alzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (T2DM) have an increased incidence in modern society. Although increasing evidence has supported the close linkage between these two disorders, the inter-relational mechanisms remain to be fully elucidated. Objective: The primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and T2DM. Methods: We downloaded the microarray data of AD and T2DM from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene network modules related to AD and T2DM. Then, Gene Ontology (GO) and pathway enrichment analysis were performed on the common genes existing in the AD and T2DM related modules by clusterProfiler and DOSE package. Finally, we utilized the STRING database to construct the protein-protein interaction network and found out the hub genes in the network. Results: Our findings indicated that seven and four modules were the most significant with AD and T2DM, respectively. Functional enrichment analysis showed that AD and T2DM common genes were mainly enriched in signaling pathways such as circadian entrainment, phagosome, glutathione metabolism and synaptic vesicle cycle. Protein-protein interaction network construction identified 10 hub genes (CALM1, LRRK2, RBX1, SLC6A1, TXN, SNRPF, GJA1, VWF, LPL, AGT) in AD and T2DM shared genes. Conclusions: Our work identified common pathogenesis of AD and T2DM. These shared pathways might provide a novel idea for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosis and treatment of AD and T2DM.

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