Identification of bladder cancer prognostic biomarkers using an ageing gene-related competitive endogenous RNA network
Metrics: PDF 547 views | HTML 1538 views | ?
Changliang Wang1,*, Liang Chen1,*, Yang Yang1, Menglei Zhang1 and Garry Wong1
1Faculty of Health Sciences, University of Macau, Macau, China
*These authors have contributed equally to this work
Garry Wong, email: GarryGWong@umac.mo
Keywords: ageing gene; bladder cancer; microRNA; competitive endogenous RNA; prognostic biomarker
Received: July 11, 2017 Accepted: November 15, 2017 Published: December 04, 2017
Competitive endogenous RNAs (ceRNAs) are a newly proposed RNA interaction mechanism that has been associated with the initiation and progression of various cancers. In this study, we constructed an ageing gene related ceRNA network (AgeingCeNet) in bladder cancer. Network analysis revealed that ageing gene ceRNAs have a larger degree and closeness centrality than ageing genes themselves. Notably, the difference of betweenness centrality of ageing genes and their ceRNAs is not significant, suggesting that the ceRNAs of ageing genes and ageing genes themselves both play important communication roles in AgeingCeNet. KEGG pathway enrichment analysis for genes in AgeingCeNet revealed that AgeingCeNet genes are enriched in cancer pathways and several cancer related singaling pathways. We also identified 37 core modules from AgeingCeNet using CFinder software. Next, we identified 2 potential prognostic modules, named K11M14 and K13M4, whose prognostic ability is better than that of age and gender. Finally, we identified microRNAs (miRNAs) regulating the two modules, which include miR-15b-5p, miR-195-5p, miR-30 family members, and several other cancer-related miRNAs. Our study demonstrated that constructing an ageing gene related ceRNA network is a feasible strategy to explore the mechanism of initiation and progression of bladder cancer, which might benefit the treatment of this disease.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.