Identification of key genes involved in type 2 diabetic islet dysfunction: a bioinformatics study

Abstract Aims: To identify the key differentially expressed genes (DEGs) in islet and investigate their potential pathway in the molecular process of type 2 diabetes. Methods: Gene Expression Omnibus (GEO) datasets (GSE20966, GSE25724, GSE38642) of type 2 diabetes patients and normal controls were downloaded from GEO database. DEGs were further assessed by enrichment analysis based on the Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8. Then, by using Search Tool for the Retrieval Interacting Genes (STRING) 10.0 and gene set enrichment analysis (GSEA), we identified hub gene and associated pathway. At last, we performed quantitative real-time PCR (qPCR) to validate the expression of hub gene. Results: Forty-five DEGs were co-expressed in the three datasets, most of which were down-regulated. DEGs are mostly involved in cell pathway, response to hormone and binding. In protein–protein interaction (PPI) network, we identified ATP-citrate lyase (ACLY) as hub gene. GSEA analysis suggests low expression of ACLY is enriched in glycine serine and threonine metabolism, drug metabolism cytochrome P450 (CYP) and NOD-like receptor (NLR) signaling pathway. qPCR showed the same expression trend of hub gene ACLY as in our bioinformatics analysis. Conclusion: Bioinformatics analysis revealed that ACLY and the pathways involved are possible target in the molecular mechanism of type 2 diabetes.

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Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

BioMed Research International ◽

10.1155/2018/4761064 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Binbin Xie ◽

Yiran Li ◽

Rongjie Zhao ◽

Yuzi Xu ◽

Yuhui Wu ◽

...

Keyword(s):

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Regulation Of Transcription ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Search Tool ◽

Poor Outcomes

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

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Bioinformatics Analysis of the Molecular Mechanism and Potential Treatment Target of Ankylosing Spondylitis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/7471291 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Fanyan Meng ◽

Ningna Du ◽

Daoming Xu ◽

Li Kuai ◽

Lanying Liu ◽

...

Keyword(s):

Gene Expression ◽

Ankylosing Spondylitis ◽

Molecular Mechanism ◽

Soft Tissues ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

R Package ◽

Gene Expression Omnibus ◽

Treatment Targets ◽

Protein Protein Interaction

Ankylosing spondylitis (AS) is an autoimmune disease that mainly affects the spinal joints, sacroiliac joints, and adjacent soft tissues. We conducted bioinformatics analysis to explore the molecular mechanism related to AS pathogenesis and uncover novel potential molecular targets for the treatment of AS. The profiles of GSE25101, containing gene expression data extracted from the blood of 16 AS patients and 16 matched controls, were acquired from the Gene Expression Omnibus (GEO) database. The background correction and standardization were carried out utilizing the transcript per million (TPM) method. After analysis of AS patients and the normal groups, we identified 199 differentially expressed genes (DEGs) with upregulation and 121 DEGs with downregulation by the limma R package. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) biological process enrichment analysis revealed that the DEGs with upregulation were mainly associated with spliceosome, ribosome, RNA-catabolic process, electron transport chain, etc. And the DEGs with downregulation primarily participated in T cell-associated pathways and processes. After analysis of the protein-protein interaction (PPI) network, our data revealed that the hub genes, comprising MRPL13, MRPL22, LSM3, COX7A2, COX7C, EP300, PTPRC, and CD4, could be the treatment targets in AS. Our data furnish new hints to uncover the features of AS and explore more promising treatment targets towards AS.

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

Current Alzheimer Research ◽

10.2174/1567205017666200810164932 ◽

2020 ◽

Vol 17 (6) ◽

pp. 566-575 ◽

Cited By ~ 1

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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Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.580190 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jingni Wu ◽

Xiaomeng Xia ◽

Ye Hu ◽

Xiaoling Fang ◽

Sandra Orsulic

Keyword(s):

Network Analysis ◽

Topological Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Messenger Rnas ◽

Hub Genes ◽

Protein Protein Interaction ◽

Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

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BIOINFORMATICS ANALYSIS OF GENES ASSOCITED WITH TYPE 2 DIABETES MELLITUS

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v5.i7.2017.2118 ◽

2017 ◽

Vol 5 (7) ◽

pp. 159-178

Author(s):

Shruti Mishra ◽

Sunita Singh ◽

Rajeev Mishra ◽

Prashant Ankur Jain ◽

Raghvendra Raman Mishra ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Bioinformatics Analysis ◽

Target Prediction ◽

Target Protein ◽

World Population ◽

Primary Role ◽

Protein Protein Interaction

Type 2 Diabetes mellitus is a multi-factorial disease caused due to gene defect as well as environmental factor. GWAS have played a primary role in demonstrating that genetic variation in a number of loci, SNPs, affects the risk of T2DM. there are our objective is to find out Disease pathway map by taking all genes of T2DM which are 35 in numbers and but in all there are 10 mostly involve in T2Dm from all over world population and it is find out by GWAS method then after we analyzed the KEGG pathway by analyzing T2DM pathway, Insulin signaling pathway, and WNT signalling pathway to find out common protein then after by bioinformatics analysis combined and expend these pathways toward common protein for understanding the Diseases mechanism. We do Protein-protein interaction and find out their complete target hub protein and target prediction for network hub. so for all these analysis I collect the total genes involve in T2DM and take those gene which are common for all population and their SNPs ,chromosome location in these all genes and by using string database I tried to find out the target protein hub which are found in this disease so there I have taken 5 most frequent genes and doing PPI in human so there are all have their own target protein hub-KCNJ11 have target protein hub PPKACA & TCF7L2 have complete target protein hub TLEI & PPARG have a target protein hub EP300 & CDKL1 have compete target protein hub UCB & HHEX complete target protein SOX2.

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Identification of MEG8-miR378d-SOBP Axis as a Novel Regulatory Network and Associated With Immune Infiltrates in Ovarian Carcinoma by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-76980/v1 ◽

2020 ◽

Author(s):

Jian Lei ◽

Zhen-Yu He ◽

Jun Wang ◽

Min Hu ◽

Ping Zhou ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Human Protein Atlas

Abstract BackgroundTo investigate the potential molecular mechanism of ovarian cancer (OC) evolution and immunological correlation using the integrated bioinformatics analysis.MethodsData from the Gene Expression Omnibus (GEO) was used to gain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were completed by utilizing the Database for Annotation, Visualization, and Integrated Discovery (DAVID). After multiple validation via The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis 2 (GEPIA 2), the Human Protein Atlas (HPA) and Kaplan-Meier (KM) plotter, immune logical relationships of the key gene SOBP were evaluated based on Tumor Immune Estimation Resource (TIMER), and Gene Set Enrichment Analysis (GSEA) software. Finally, the lncRNAs-miRNAs-mRNAs sub-network was predicted by starBase, Targetscan, miRBD, and LncBase, individually. Correlation of expression and prognosis for mRNAs, miRNAs and lncRNAs were confirmed by TCGA, GEPIA 2, starBase, and KM.ResultsA total of 192 shared DEGs were discovered from the four data sets, including 125 upregulated and 67 downregulated genes. Functional enrichment analysis presented that they were mainly enriched in cartilage development, pathway in PI3K-Akt signaling pathway. Lower expression of SOBP was the independent prognostic factor for inferior prognosis in OC patients. Intriguingly, downregulated SOBP enhanced the infiltration levels of B cells, CD8+ T cells, Macrophage, Neutrophil and Dendritic cells. GSEA also disclosed low SOBP showed significantly association with the activation of various immune-related pathways. Finally, we firstly reported that MEG8-miR378d-SOBP axis was linked to development and prognosis of ovarian cancer through regulating cytokines pathway.Conclusions Our study establishes a novel MEG8-miR378d-SOBP axis in the development and prognosis of OC, and the triple sub-network probably affects the progression of ovarian tumor by regulating cytokines pathway.

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Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/4530180 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Yanzhe Wang ◽

Wenjuan Cai ◽

Liya Gu ◽

Xuefeng Ji ◽

Qiusheng Shen

Keyword(s):

Atrial Fibrillation ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Cell Counting ◽

Ppi Network ◽

Hub Genes ◽

Bioinformatics Analyses ◽

Protein Protein Interaction ◽

Endothelial Growth Factor

Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result. 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion. Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment.

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Screening of Potential Biomarkers and Therapeutic Targets for T2DM-related Alzheimer's Disease Based on Bioinformatics Analysis

10.21203/rs.3.rs-952508/v1 ◽

2021 ◽

Author(s):

Zhengqiang He

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Actin Filament ◽

Enrichment Analysis ◽

Underlying Mechanism ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Protein Protein Interaction

Abstract More and more studies have suggested that type 2 diabetes mellitus (T2DM) was susceptible to trigger Alzheimer’s disease(AD), but the common underlying mechanism were unclear. We want to perform bioinformatic analysis with public databases. The T2DM dataset GSE95849 and AD dataset GSE97760 were selected from Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) and the communal DEGs between the two diseases, which perform to the enrichment analysis, protein-protein interaction (PPI) network analysis, correlation analysis.We found 255 communal DEGs between T2DM and AD. They were enriched in negative regulation of actin filament depolymerization and regulation of actin filament depolymerization. Top 5 hub genes which identified from the PPI network were enriched in autophagy. The actin filament and autophagy may be the key association between the two diseases.

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Bioinformatics analysis of key genes and pathways in Hashimoto thyroiditis tissues

Bioscience Reports ◽

10.1042/bsr20200759 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Long Zheng ◽

Xiaojie Dou ◽

Huijia Song ◽

Pengwei Wang ◽

Wei Qu ◽

...

Keyword(s):

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Interaction Analysis ◽

Differentially Expressed Gene ◽

Enrichment Analysis ◽

Hashimoto Thyroiditis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Key Genes

Abstract Hashimoto thyroiditis (HT) is one of the most common autoimmune diseases, and the incidence of HT continues to increase. Long-term, uncontrollable HT results in thyroid dysfunction and even increases carcinogenesis risks. Since the origin and development of HT involve many complex immune processes, there is no effective therapy for HT on a pathogenesis level. Although bioinformatics analysis has been utilized to seek key genes and pathways of thyroid cancer, only a few bioinformatics studies that focus on HT pathogenesis and mechanisms have been reported. In the present study, the Gene Expression Omnibus dataset (GSE29315) containing 6 HT and 8 thyroid physiological hyperplasia samples was downloaded, and differentially expressed gene (DEG) analysis, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, protein–protein interaction analysis, and gene set enrichment analysis were performed. In total, 85 DEGs, containing 76 up-regulated and 9 down-regulated DEGS, were identified. The DEGs were mainly enriched in immune and inflammatory response, and the signaling pathways were involved in cytokine interaction and cytotoxicity. Moreover, ten hub genes were identified, and IFN-γ, IFN-α, IL6/JAK/STAT3, and inflammatory pathways may promote the origin and progression of HT. The present study indicated that exploring DEGs and pathways by bioinformatics analysis has important significance in understanding the molecular mechanisms of HT and providing potential targets for the prevention and treatment of HT.

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Bioinformatics approach for potential genes associated with osteoarthritis

10.21203/rs.2.21759/v1 ◽

2020 ◽

Author(s):

Yi Chai ◽

Cong-Ying Mai ◽

Ming-Yue Zhang ◽

Dao-Ming Xu ◽

Feng Tan ◽

...

Keyword(s):

Gene Expression ◽

Characteristic Curve ◽

Principal Component ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

Receiver Operator Characteristic Curve ◽

Gene Set Enrichment Analysis ◽

Protein Protein Interaction ◽

Matrix Quality

Abstract Background: Osteoarthritis is the main cause of disability and pain. Due to limited understanding of the disease mechanism, it is generally difficult to diagnose early and eliminate sequelae. The aim of this study was to identify novel biomarkers for osteoarthritis (OA) using a bioinformatics approach. Method: The gene expression dataset GSE82107 were obtained from the Gene Expression Omnibus (GEO) database. Matrix quality assessment was performed using corrplot packages and principal component analysis. The differentially expressed genes (DEGs) picked out using GEO2R tool. Enrichment analyses were performed using The Database for Annotation, Visualization and Integrated Discovery and Gene Set Enrichment Analysis (GSEA). Weighted correlation network analysis (WGCNA) was used to find gene modules highly associated with OA. Cytoscape with Molecular Complex Detection (MCODE) plug-in was utilized to analyze protein-protein interaction of these DEGs. Receiver operator characteristic curve analysis was used to evaluate the diagnostic effectiveness of genes. Results: Samples with different conditions (HC and OA) are obviously distinguished, and the distance between biological replicates is relatively close. In total, 1676 DEGs were identified. Enrichment analysis showed that there were some gene sets related to OA pathology, such as chondrocyte development. The results of WGCNA analysis showed that 298 genes were most positive associated with OA. 10 common genes obtained were selected as candidate core genes. ROC results showed that 5 of these genes had the greatest diagnostic value. Conclusion: This is the first study to identify biomarkers related to OA by combining multiple algorithms such as GSEA, WGCNA, MCODE and ROC. We suggest that GLG1, PAPSS2, CTSK, TIMP1 and SDC1 could serve as valuable biomarkers. Further studies are needed to examine the precise role and mechanism of these genes in OA.

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