Dataset Information

Coexpression modules constructed by weighted gene co-expression network analysis indicate ubiquitin-mediated proteolysis as a potential biomarker of uveal melanoma.

ABSTRACT: Uveal melanoma (UM) is a tumor that affects individuals throughout the world. Although gene expression analysis of UM has been performed previously, systemic co-expression analysis for this type of cancer remains lacking. Microarray data of UM samples was obtained from the Genome Expression Omnibus (dataset GSE44295). Co-expression modules were built by weighted gene co-expression network analysis. Functional enrichment analysis was performed on the co-expressed genes from important modules. Seven co-expression modules were constructed from the 5,000 genes gathered from the 58 human UM samples. The number of genes in these modules ranged from 73 to 3,051, with the mean number being 711. There was a marked difference in interactions among pairwise modules. Functional enrichment analysis demonstrated that module 2 was mainly enriched in pathways associated with the regulation of transcription. Additionally, modules 2-4 were significantly enriched in the ubiquitin mediated proteolysis pathway, suggesting it could serve a critical role in the occurrence and development of UM. The findings of the present study present a framework of co-expressed gene modules for human UM and provide an improved understanding of these modules at a functional level. Understanding the molecular mechanism and cellular pathways involved in pathogenesis of UM is extremely important for the development of more effective diagnostic and therapeutic strategies.

SUBMITTER: Yang M

PROVIDER: S-EPMC6307452 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Coexpression modules constructed by weighted gene co-expression network analysis indicate ubiquitin-mediated proteolysis as a potential biomarker of uveal melanoma.

Yang Meng M Wan Qi Q Hu Xiang X Yin Haitao H Hao Dawei D Wu Chengjun C Li Jianmin J

Experimental and therapeutic medicine 20181109 1

Uveal melanoma (UM) is a tumor that affects individuals throughout the world. Although gene expression analysis of UM has been performed previously, systemic co-expression analysis for this type of cancer remains lacking. Microarray data of UM samples was obtained from the Genome Expression Omnibus (dataset GSE44295). Co-expression modules were built by weighted gene co-expression network analysis. Functional enrichment analysis was performed on the co-expressed genes from important modules. Sev ...[more]

PMID: 30651788

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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

Coexpression modules constructed by weighted gene co-expression network analysis indicate ubiquitin-mediated proteolysis as a potential biomarker of uveal melanoma.

Publications

Coexpression modules constructed by weighted gene co-expression network analysis indicate ubiquitin-mediated proteolysis as a potential biomarker of uveal melanoma.

Similar Datasets

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