ArticleBMC genomics2015
Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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35 citing papers in PubMed, 1 synthesis or guideline pooled it, 105 citations in OpenAlex.
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBladder cancer is the most common malignant tumor of the urinary system and it is a heterogeneous disease with both superficial and invasive growth. However, its aetiological agent is still unclear. And it is indispensable to find key genes or modules causing the bladder cancer. Based on gene expression microarray datasets, constructing differential co-expression networks (DCNs) is an important method to investigate diseases and there have been some relevant good tools such as R package 'WGCNA', 'DCGL'.
resultsEmploying an integrated strategy, 36 up-regulated differentially expressed genes (DEGs) and 356 down-regulated DEGs were selected and main functions of those DEGs are cellular physiological precess(24 up-regulated DEGs; 167 down-regulated DEGs) and cellular metabolism (19 up-regulated DEGs; 104 down-regulated DEGs). The up-regulated DEGs are mainly involved in the the pathways related to "metabolism". By comparing two DCNs between the normal and cancer states, we found some great changes in hub genes and topological structure, which suggest that the modules of two different DCNs change a lot. Especially, we screened some hub genes of a differential subnetwork between the normal and the cancer states and then do bioinformatics analysis for them.
conclusionsThrough constructing and analyzing two differential co-expression networks at different states using the screened DEGs, we found some hub genes associated with the bladder cancer. The results of the bioinformatics analysis for those hub genes will support the biological experiments and the further treatment of the bladder cancer.
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