scholarly journals Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis

2019 ◽  
Vol 25 ◽  
pp. 9237-9244
Author(s):  
Xuanqiang Che ◽  
Ran Zhao ◽  
Hua Xu ◽  
Xue Liu ◽  
Shumiao Zhao ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Pathway Analysis ◽  
Bioinformatics Analysis ◽  
Differently Expressed Genes

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.

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 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 Association of potential salivary biomarkers with diabetic retinopathy and its severity in type-2 diabetes mellitus: a proteomic analysis by mass spectrometry

2016 ◽  
Author(s):  
Chin Soon Chee ◽  
Khai Meng Chang ◽  
Mun Fai Loke ◽  
Voon Pei Angela Loo ◽  
Visvaraja Subrayan
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Pathway Analysis ◽  
Differentially Expressed ◽  
Diabetic Patients ◽  
Disease Group

Aim/hypothesis The aim of our study was to characterize the human salivary proteome and determine the changes in protein expression in 2 different stages of diabetic retinopathy with type-2 diabetes mellitus: (1) with non-proliferative diabetic retinopathy (NPDR) and (2) with proliferative diabetic retinopathy (PDR). Type-2 diabetes without diabetic retinopathy (XDR) was designated as control. Method In this study, 45 saliva samples were collected (15 samples from XDR control group, 15 samples from NPDR disease group and 15 samples from PDR disease group). Salivary proteins were extracted, reduced, alkylated, trypsin digested and labeled with iTRAQ before analyzing by Orbitrap fusion tribrid mass spectrometer. Proteins annotation, fold change calculation and statistical analysis were interrogated by Proteome Discoverer. Biological pathway analysis was performed by Ingenuity Pathway Analysis. Data are available via ProteomeXchange with identifiers PXD003723-PX003725. Results A total of 315 proteins were identified from the salivary proteome and 119 proteins were found to be differentially expressed. The differentially expressed proteins from the NPDR disease group and the PDR disease group were assigned to respective canonical pathways indicating increased LXR/RXR activation, FXR/RXR activation, acute phase response signaling, sucrose degradation V and regulation of actin-based motility by Rho in the PDR disease group compared to the NPDR disease group Conclusions/Interpretation Progression from non-proliferative to proliferative retinopathy in type-2 diabetic patients is a complex multi-mechanism and systemic process. Furthermore, saliva was shown to be a feasible alternative sample source for diabetic retinopathy biomarkers.

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