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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.

Meng Yang | Qi Wan | Xiang Hu | Haitao Yin | Dawei Hao | Chengjun Wu | Jianmin Li
Experimental and therapeutic medicine | 2019

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.

Pubmed ID: 30651788

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KEGG (tool)

RRID:SCR_012773

Integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.

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