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As bladder cancer (BC) is very heterogeneous and complicated in the genetic level, exploring genes to serve as biomarkers and therapeutic targets is practical. We searched Gene Expression Omnibus (GEO) and downloaded the eligible microarray datasets. After intersection analysis for identified differentially expressed genes (DEGs) of included datasets, overlapped DEGs were identified and subsequently analyzed with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-Protein Interaction (PPI) and hub genes identification. Hub genes were further analyzed with mRNA expression comparation in Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) database, proteomics-based validation in The Human Protein Atlas (THPA) and survival analysis in GEO and Oncolnc database. We analyzed five eligible GEO datasets and identified 76 overlapped DEGs mapped into PPI network with 459 edges which were mainly enriched in cell cycle pathway and related terms in GO and KEGG analysis. Among five identified hub genes, which are Cyclin-Dependent Kinase 1 (CDK1), Ubiquitin-Conjugating Enzyme E2 C (UBE2C), Cell Division Cycle 20 (CDC20), Microtubule Nucleation Factor (TPX2) and Cell Division Cycle Associated 8 (CDCA8); CDC20 and CDCA8 were confirmed as significant in mRNA expression comparation and proteomics-based validation. However, only CDC20 was considered prognostically significant in both GEO and Oncolnc database. CDC20 and CDCA8 were identified as candidate diagnostic biomarkers for BC in the present study; however, only CDC20 was validated as prognostically valuable and may possibly serve as a candidate prognostic biomarker and potential therapeutic target. Still, further validation studies are essential and indispensable. © 2020 The Author(s).

Citation

Peilin Shen, Xuejun He, Lin Lan, Yingkai Hong, Mingen Lin. Identification of cell division cycle 20 as a candidate biomarker and potential therapeutic target in bladder cancer using bioinformatics analysis. Bioscience reports. 2020 Jul 31;40(7)

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PMID: 32677673

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