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Differential co-expression analysis of venous thromboembolism based on gene expression profile data.

ABSTRACT: The aim of the present study was to screen differentially co-expressed genes and the involved transcription factors (TFs) and microRNAs (miRNAs) in venous thromboembolism (VTE). Microarray data of GSE19151 were downloaded from Gene Expression Omnibus, including 70 patients with VTE and 63 healthy controls. Principal component analysis (PCA) was performed using R software. Differential co-expression analysis was performed using R, followed by screening of modules using Cytoscape. Functional annotation was performed using Database for Annotation, Visualization, and Integrated Discovery. Moreover, Fisher test was used to screen key TFs and miRNAs for the modules. PCA revealed the disease and healthy samples could not be distinguished at the gene expression level. A total of 4,796 upregulated differentially co-expressed genes (e.g. zinc finger protein 264, electron-transfer-flavoprotein, beta polypeptide and Janus kinase 2) and 3,629 downregulated differentially co-expressed genes (e.g. adenylate cyclase 7 and single-stranded DNA binding protein 2) were identified, which were further mined to obtain 17 and eight modules separately. Functional annotation revealed that the largest upregulated module was primarily associated with acetylation and the largest downregulated module was mainly involved in mitochondrion. Moreover, 48 TFs and 62 miRNA families were screened for the 17 upregulated modules, such as E2F transcription factor 4, miR-30 and miR-135 regulating the largest module. Conversely, 35 TFs and 18 miRNA families were identified for the 8 downregulated modules, including mitochondrial ribosomal protein S12 and miR-23 regulating the largest module. Differentially co-expressed genes regulated by TFs and miRNAs may jointly contribute to the abnormal acetylation and mitochondrion presentation in the progression of VTE.

SUBMITTER: Ming Z

PROVIDER: S-EPMC4887825 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Differential co-expression analysis of venous thromboembolism based on gene expression profile data.

Ming Zhibing Z Ding Wenbin W Yuan Ruifan R Jin Jie J Li Xiaoqiang X

Experimental and therapeutic medicine 20160330 6

The aim of the present study was to screen differentially co-expressed genes and the involved transcription factors (TFs) and microRNAs (miRNAs) in venous thromboembolism (VTE). Microarray data of GSE19151 were downloaded from Gene Expression Omnibus, including 70 patients with VTE and 63 healthy controls. Principal component analysis (PCA) was performed using R software. Differential co-expression analysis was performed using R, followed by screening of modules using Cytoscape. Functional annot ...[more]

PMID: 27284300

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Project description:BackgroundDiabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression.MethodsThe identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed.ResultsAfter normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes.ConclusionsThe identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease.

Dataset Information

Differential co-expression analysis of venous thromboembolism based on gene expression profile data.

Publications

Differential co-expression analysis of venous thromboembolism based on gene expression profile data.

Similar Datasets

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