Dataset Information

Weighted Gene Coexpression Network Analysis Identifies Key Genes and Pathways Associated with Idiopathic Pulmonary Fibrosis.

ABSTRACT: BACKGROUND Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease with an unknown etiology. Gene expression microarray data have provided some insights into the molecular mechanisms of IPF. This study aimed to identify key genes and significant signaling pathways involved in IPF using bioinformatics analysis. MATERIAL AND METHODS Differentially expressed genes (DEGs) were identified using integrated analysis of gene expression data with a robust rank aggregation (RRA) method. The Connectivity Map (CMAP) was used to identify gene-expression signatures associated with IPF. Weighted gene coexpression network analysis (WGCNA) was used to explore the functional modules involved in the pathogenesis of IPF. RESULTS A total of 191 patients with IPF and 101 normal controls from six genome-wide expression datasets were included. CMAP predicted several small molecular agents as potential gene targets in IPF. Several functional modules were detected that showed the highest correlation with IPF, including an extracellular matrix (ECM) component, and a myeloid leukocyte migration and activation component involved in the immune response. Hub genes were identified in the key functional modules that might have a role in the progression of IPF. CONCLUSIONS WGCNA was used to identify functional modules and hub genes involved in the pathogenesis of IPF.

SUBMITTER: Wang Z

PROVIDER: S-EPMC6582683 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Weighted Gene Coexpression Network Analysis Identifies Key Genes and Pathways Associated with Idiopathic Pulmonary Fibrosis.

Wang Zheng Z Zhu Jie J Chen Fengzhe F Ma Lixian L

Medical science monitor : international medical journal of experimental and clinical research 20190609

BACKGROUND Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease with an unknown etiology. Gene expression microarray data have provided some insights into the molecular mechanisms of IPF. This study aimed to identify key genes and significant signaling pathways involved in IPF using bioinformatics analysis. MATERIAL AND METHODS Differentially expressed genes (DEGs) were identified using integrated analysis of gene expression data with a robust rank aggregation (RRA) method. The Conn ...[more]

PMID: 31177264

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Project description:Introduction: Acute myeloid leukemia (AML) is the most common type of leukemia in adults. However, there is a gap in understanding the molecular basis of the disease, partly because key genes associated with AML have not been extensively explored. In the current study, we aimed to identify genes that have strong association with AML based on a cross-species integrative approach. Methods: We used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules significantly correlated with human AML, and further selected the genes exhibiting a significant difference in expression between AML and healthy mouse. Protein-protein interactions, transcription factors, gene function, genetic regulation, and coding sequence variants were integrated to identify key hub genes in AML. Results: The cross-species approach identified a total of 412 genes associated with both human and mouse AML. Enrichment analysis confirmed an association of these genes with hematopoietic and immune-related functions, phenotypes, processes, and pathways. Further, the integrated analysis approach identified a set of important module genes including Nfe2, Trim27, Mef2c, Ets1, Tal1, Foxo1, and Gata1 in AML. Six of these genes (except ETS1) showed significant differential expression between human AML and healthy samples in an independent microarray dataset. All of these genes are known to be involved in immune/hematopoietic functions, and in transcriptional regulation. In addition, Nfe2, Trim27, Mef2c, and Ets1 harbor coding sequence variants, whereas Nfe2 and Trim27 are cis-regulated, making them attractive candidates for validation. Furthermore, subtype-specific analysis of the hub genes in human AML indicated high expression of NFE2 across all the subtypes (M0 through M7) and enriched expression of ETS1, LEF1, GATA1, and TAL1 in M6 and M7 subtypes. A significant correlation between methylation status and expression level was observed for most of these genes in AML patients. Conclusion: Findings from the current study highlight the importance of our cross-species approach in the identification of multiple key candidate genes in AML, which can be further studied to explore their detailed role in leukemia/AML.

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Dataset Information

Weighted Gene Coexpression Network Analysis Identifies Key Genes and Pathways Associated with Idiopathic Pulmonary Fibrosis.

Publications

Weighted Gene Coexpression Network Analysis Identifies Key Genes and Pathways Associated with Idiopathic Pulmonary Fibrosis.

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