Dataset Information

Weighted Gene Coexpression Network Analysis Identified MicroRNA Coexpression Modules and Related Pathways in Type 2 Diabetes Mellitus.

ABSTRACT: Objective:Type 2 diabetes mellitus (T2DM) is a metabolic disease with high incidence, which has seriously affected human life and health. MicroRNA, a short-chain noncoding RNA, plays an important role in T2DM. Identification of meaningful microRNA modules and the role of microRNAs provide a basis for searching potential biomarkers of T2DM. Materials and Methods:In this study, three newly diagnosed patients with T2DM and three controls were selected for Whole Peripheral Blood RNA Sequencing to establish a microRNA library. Weighted gene coexpression network analysis (WGCNA) was applied to construct coexpression modules and to detect the trait-related microRNA modules; then, KEGG enrichment analysis was performed to predict the biological function of the interest modules, and candidate hub microRNAs were screened out by the value of module membership (MM) and protein-protein interaction (PPI) network. Result:Four microRNA modules (blue, brown, magenta, and turquoise) were highly associated with the T2DM; the number of miRNAs in these modules ranged from 41 to 469. The Fc gamma R-mediated phagocytosis pathway, Rap1 signaling pathway, MAPK signaling pathway, and Lysosome pathway were common pathways in three of the four modules. RPS27A, UBC, and RAC1 were the top three proteins in our study; their corresponding RNAs were miR-1271-5p, miR-130a-3p, miR-130b-3p, and miR-574-3p. Conclusion:In summary, this study identified blood miRNAs in human T2DM using RNA sequencing. The findings may be the foundation for understanding the potential role of miRNAs in T2DM.

SUBMITTER: Feng T

PROVIDER: S-EPMC6935443 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Weighted Gene Coexpression Network Analysis Identified MicroRNA Coexpression Modules and Related Pathways in Type 2 Diabetes Mellitus.

Feng Tianyu T Li Kexin K Zheng Pingping P Wang Yanjun Y Lv Yaogai Y Shen Li L Chen Yang Y Xue Zhiqiang Z Li Bo B Jin Lina L Yao Yan Y

Oxidative medicine and cellular longevity 20191213

<h4>Objective</h4>Type 2 diabetes mellitus (T2DM) is a metabolic disease with high incidence, which has seriously affected human life and health. MicroRNA, a short-chain noncoding RNA, plays an important role in T2DM. Identification of meaningful microRNA modules and the role of microRNAs provide a basis for searching potential biomarkers of T2DM.<h4>Materials and methods</h4>In this study, three newly diagnosed patients with T2DM and three controls were selected for Whole Peripheral Blood RNA S ...[more]

PMID: 31915515

Similar Datasets

Project description:BackgroundType 2 diabetes mellitus (T2DM) is characterized by chronic low-grade inflammation, showing an increasing trend. The infiltration of immune cells into adipose tissue has been shown to be an important pathogenic cause of T2DM. The purpose of this study is to use the relevant database to identify some abnormally expressed or dysfunctional genes related to diabetes from the perspective of immune infiltration.MethodsWeighted gene coexpression network analysis (WGCNA) was employed to systematically identify the coexpressed gene modules and hub genes associated with T2DM development based on a microarray dataset (GSE23561) from the Gene Expression Omnibus (GEO) database. The key genes in modules highly related to clinical features were calculated and screened by using R software, and their participation in T2DM was determined by gene enrichment analysis. The mRNA levels of CSF1R, H2AFV, LCK, and TLR9 in pre-T2DM mice and normal wild-type mice were detected by WGCNA screening and real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR).ResultsWe constructed 14 coexpressed gene modules, and the brown module was shown to be significantly related to T2DM. Through verification of the protein-protein interaction (PPI) network, four upregulated hub genes, CSF1R, H2AFV, LCK, and TLR9, were screened from the brown module and successfully distinguishedT2DM patients from healthy people. These hub genes may be used as biomarkers and important indicators for patient diagnosis. Enrichment analysis showed that these hub genes were highly associated with IL-6-related inflammatory metabolism, immune regulation, and immune cell infiltration. Finally, we verified the hub genes CSF1R, LCK, and TLR9 in a T2DM animal model and found that their mRNA levels were significantly higher in animals with T2DM than in control group mice (NC).ConclusionsIn summary, our results suggest that these hub genes (CSF1R, LCK, and TLR9) can serve as biomarkers and immunotherapeutic targets for T2DM.

Project description:Purpose:Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease. Patients and Methods:Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well. Results:We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function. Conclusion:The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.

Project description:AimsHeart failure (HF) is a chronic heart disease with a high incidence and mortality. Due to the regulatory complexity of gene coexpression networks, the underlying hub genes regulation in HF remain incompletely appreciated. We aimed to explore potential key modules and genes for HF using weighted gene coexpression network analysis (WGCNA).Methods and resultsThe expression profiles by high throughput sequencing of heart tissues samples from HF and non-HF samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HF and non-HF samples were firstly identified. Then, a coexpression network was constructed to identify key modules and potential hub genes. The biological functions of potential hub genes were analysed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, a protein-protein interaction (PPI) network was constructed using the STRING online tool. A total of 135 DEGs (133 up-regulated and 2 down-regulated DEGs) between HF and non-HF samples were identified in the GSE135055 and GSE123976 datasets. Moreover, a total of 38 modules were screened based on WGCNA in the GSE135055 dataset, and six potential hub genes (UCK2, ASB1, CCNI, CUX1, IRX6, and STX16) were screened from the key module by setting the gene significance over 0.2 and the module membership over 0.8. Furthermore, 78 potential hub genes were obtained by taking the intersection of the 135 DEGs and all genes in the key module, and enrichment analysis revealed that they were mainly involved in the MAPK and PI3K-AKT signalling pathways. Finally, in a PPI network constructed with the 78 potential hub genes, CUX1 and ASB1 were identified as hub genes in HF because they were also identified as potential hub genes in the WGCNA.ConclusionsTo the best of our knowledge, our study is the first to employ WGCNA to identify the key module and hub genes for HF. Our study identified a module and two genes that might play important roles in HF, which may provide potential biomarkers for the diagnosis of HF and improve our knowledge of the molecular mechanisms underlying HF.

Dataset Information

Weighted Gene Coexpression Network Analysis Identified MicroRNA Coexpression Modules and Related Pathways in Type 2 Diabetes Mellitus.

Publications

Weighted Gene Coexpression Network Analysis Identified MicroRNA Coexpression Modules and Related Pathways in Type 2 Diabetes Mellitus.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets