Dataset Information

Integrative Analyses of Genes Associated with Fulminant Type 1 Diabetes.

ABSTRACT: Objective:Fulminant type 1 diabetes (FT1D) is a type of type 1 diabetes, which is characterized by rapid onset of disease and severe metabolic disorders. We intend to screen for crucial genes and potential molecular mechanisms in FT1D in this study. Method:We downloaded GSE44314, which includes six healthy controls and five patients with FT1D, from the GEO database. Identification of differentially expressed genes (DEGs) was performed by NetworkAnalyst. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were screened by an online tool-Database for Annotation, Visualization, and Integration Discovery (DAVID). Protein-protein interaction (PPI) network and hub genes among DEGs were analyzed by NetworkAnalyst. And we also use NetworkAnalyst to find out the microRNAs (miRNAs) and transcription factors (TFs) which regulate the expression of DEGs. Result:We identified 130 DEGs (60 upregulated and 70 downregulated DEGs) between healthy controls and FT1D patients. GO analysis results revealed that DEGs were mostly enriched in generation of precursor metabolites and energy, neurohypophyseal hormone activity, and mitochondrial inner membrane. KEGG pathway analysis demonstrated that DEGs were mostly involved in nonalcoholic fatty liver disease. Results indicated that NCOA1, SRF, ERBB3, EST1, TOP1, UBE2S, INO80, COX7C, ITGAV, and COX6C were the top hub genes in the PPI network. Furthermore, we recognized that LDLR, POTEM, IFNAR2, BAZ2A, and SRF were the top hub genes in the miRNA-target gene network, and SRF, TSPAN4, CD59, ETS1, and SLC25A25 were the top hub genes in the TF-target gene network. Conclusion:Our study pinpoints key genes and pathways associated with FT1D by a sequence of bioinformatics analysis on DEGs. These identified genes and pathways provide more detailed molecular mechanisms of FT1D and may provide novel therapeutic targets.

SUBMITTER: Ye X

PROVIDER: S-EPMC7559223 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrative Analyses of Genes Associated with Fulminant Type 1 Diabetes.

Ye Xiaofeng X Zeng Tianshu T Kong Wen W Chen Lu-Lu LL

Journal of immunology research 20201006

<h4>Objective</h4>Fulminant type 1 diabetes (FT1D) is a type of type 1 diabetes, which is characterized by rapid onset of disease and severe metabolic disorders. We intend to screen for crucial genes and potential molecular mechanisms in FT1D in this study.<h4>Method</h4>We downloaded GSE44314, which includes six healthy controls and five patients with FT1D, from the GEO database. Identification of differentially expressed genes (DEGs) was performed by NetworkAnalyst. The Gene Ontology (GO) and ...[more]

PMID: 33083497

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Project description:ObjectiveHashimoto's thyroiditis, also known as chronic lymphocytic thyroiditis, is a common autoimmune thyroiditis, which mostly occurs in young and middle-aged women. It can be manifested as hyperthyroidism in the early stage; hypothyroidism may appear with the progression of the disease. Studies have shown that multiple factors such as heredity, environment, and autoimmunity are involved in the pathogenesis, but the specific mechanism is not clear. In our study, we tried to find key genes and potential molecular mechanisms of Hashimoto's thyroiditis to provide new ideas for the therapeutic targets of Hashimoto's thyroiditis.MethodGSE138198 and GSE54958 were downloaded from the GEO database, and two datasets were combined for analysis. The combined data were normalized to identify the differentially expressed genes (DEGs), and GO and KEGG enrichment analyses were performed. Protein-protein interaction (PPI) networks and hub genes between DEGs were identified. We also used the miRWalk database to identify regulatory miRNAs associated with expressions of DEGs.ResultWe identified 182 DEGs (160 upregulated and 22 downregulated) between Hashimoto's disease patients and the healthy control group. GO analysis showed that DEGs were mostly concentrated in detection of chemical stimulus involved in sensory perception, intermediate filament cytoskeleton, and olfactory receptor activity. KEGG pathway analysis showed that DEGs were mainly related to olfactory transduction. Some members of the KRTAP family and HTR5A, KNG1, DRD3, HTR1D, TAS2R16, INSL5, TAS2R42, and GRM7 are the most important hub genes in the PPI network. In addition, we recognized that OTUD4, LLPH, and ECHDC1 were the most important hub genes in the miRNA-target gene network.ConclusionIn this study, a series of bioinformatics analyses of DEGs were performed to identify the key genes and pathways associated with Hashimoto's thyroiditis. These genes and pathways provide a more detailed understanding of the pathogenesis of Hashimoto's disease and provide new ideas for the therapeutic targets of Hashimoto's thyroiditis.

Project description:: Insulin resistance is present in the majority of patients with non-insulin-dependent diabetes mellitus (NIDDM) and obesity. In this study, we aimed to investigate the key genes and potential molecular mechanism in insulin resistance. Expression profiles of the genes were extracted from the Gene Expression Omnibus (GEO) database. Pathway and Gene Ontology (GO) enrichment analyses were conducted at Enrichr. The protein⁻protein interaction (PPI) network was settled and analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database constructed by Cytoscape software. Modules were extracted and identified by the PEWCC1 plugin. The microRNAs (miRNAs) and transcription factors (TFs) which control the expression of differentially expressed genes (DEGs) were analyzed using the NetworkAnalyst algorithm. A database (GSE73108) was downloaded from the GEO databases. Our results identified 873 DEGs (435 up-regulated and 438 down-regulated) genetically associated with insulin resistance. The pathways which were enriched were pathways in complement and coagulation cascades and complement activation for up-regulated DEGs, while biosynthesis of amino acids and the Notch signaling pathway were among the down-regulated DEGs. Showing GO enrichment were cardiac muscle cell⁻cardiac muscle cell adhesion and microvillus membrane for up-regulated DEGs and negative regulation of osteoblast differentiation and dendrites for down-regulated DEGs. Subsequently, myosin VB (MYO5B), discs, large homolog 2(DLG2), axin 2 (AXIN2), protein tyrosine kinase 7 (PTK7), Notch homolog 1 (NOTCH1), androgen receptor (AR), cyclin D1 (CCND1) and Rho family GTPase 3 (RND3) were diagnosed as the top hub genes in the up- and down-regulated PPI network and modules. In addition, GATA binding protein 6 (GATA6) , ectonucleotide pyrophosphatase/phosphodiesterase 5 (ENPP5), cyclin D1 (CCND1) and tubulin, beta 2A (TUBB2A) were diagnosed as the top hub genes in the up- and down-regulated target gene⁻miRNA network, while tubulin, beta 2A (TUBB2A), olfactomedin-like 1 (OLFML1), prostate adrogen-regulated mucin-like protein 1 (PARM1) and aldehyde dehydrogenase 4 family, member A1 (ALDH4A1)were diagnosed as the top hub genes in the up- and down-regulated target gene⁻TF network. The current study based on the GEO database provides a novel understanding regarding the mechanism of insulin resistance and may provide novel therapeutic targets.

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Project description:Background: Loss or partial loss of one X chromosome induces Turner syndrome (TS) in females, causing major medical concerns, including otologic disorders. However, the underlying genetic pathophysiology of otologic disorders in TS is mostly unclear. Methods: Ear-related genes of TS (TSEs) were identified by analyzing differentially expressed genes (DEGs) in two Gene Expression Omnibus (GEO)-derived expression profiles and ear-genes in the Comparative Toxicogenomic Database (CTD). Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) analyses; Gene Set Enrichment Analysis (GSEA); and Gene Set Variation Analysis (GSVA) were adopted to study biological functions. Moreover, hub genes within the TSEs were identified by assessing protein-protein interaction (PPI), gene-microRNA, and gene-transcription factor (TF) networks. Drug-Gene Interaction Database (DGIdb) analysis was performed to predict molecular drugs for TS. Furthermore, three machine-learning analysis outcomes were comprehensively compared to explore optimal biomarkers of otologic disorders in TS. Finally, immune cell infiltration was analyzed. Results: The TSEs included 30 significantly upregulated genes and 14 significantly downregulated genes. Enrichment analyses suggested that TSEs play crucial roles in inflammatory responses, phospholipid and glycerolipid metabolism, transcriptional processes, and epigenetic processes, such as histone acetylation, and their importance for inner ear development. Subsequently, we described three hub genes in the PPI network and confirmed their involvement in Wnt/β-catenin signaling pathway and immune cell regulation and roles in maintaining normal auditory function. We also constructed gene-microRNA and gene-TF networks. A novel biomarker (SLC25A6) of the pathogenesis of otologic disorders in TS was identified by comprehensive comparisons of three machine-learning analyses with the best predictive performance. Potential therapeutic agents in TS were predicted using the DGIdb. Immune cell infiltration analysis showed that TSEs are related to immune-infiltrating cells. Conclusion: Overall, our findings have deepened the understanding of the pathophysiology of otologic disorders in TS and made contributions to present a promising biomarker and treatment targets for in-depth research.

Dataset Information

Integrative Analyses of Genes Associated with Fulminant Type 1 Diabetes.

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Integrative Analyses of Genes Associated with Fulminant Type 1 Diabetes.

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