Background: Dysfunction of primary cilia (PC), which could influence cell cycle and modulate cilia-related signaling transduction, has been reported in several cancers. However, there is no evidence of their function in bladder cancer (BLCA). This study was performed to investigate the presence of PC in BLCA and to explore the potential molecular mechanisms underlying the PC in BLCA.
Patients and methods: The presence of PC was assessed in BLCA and adjacent non-cancerous tissues. The gene expression dataset GSE52519 was employed to obtain differentially expressed genes (DEGs) associated with PC. The mRNA expression of the DEGs were confirmed by Gene Expression Profiling Interactive Analysis. The DEGs properties and pathways were analyzed by Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Genomatix software was used to predict putative transcription factor binding sites (TFBS) in the promoter region of DEGs, and the transcription factors were achieved according to the shared TFBS, which were supported by the ChIP-Sequence data.
Results: PC were found to be reduced in BLCA tissue samples in this study. Seven DEGs were observed to be associated with PC, and gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that these DEGs exhibited the properties and functions of PC, and that the Hedgehog signaling pathway probably participated in the pathogenesis and progression of BLCA. The mRNA expression of the seven DEGs in 404 BLCA and 28 normal tissue samples were analyzed, and five DEGs including CENPF , STIL , AURKA , STK39  and OSR1  were identified. Five TFBS including CREB, E2FF, EBOX, ETSF and HOXF in the promoter region of five DEGs were calculated and the transcription factors were obtained according to the shared TFBS.
Conclusion: PC were found to be reduced in BLCA, and the potential molecular mechanisms of PC in BLCA helped to provide novel diagnosis and therapeutic targets for BLCA.
Keywords: bladder cancer, primary cilia, differentially expressed genes, microarray analysis, transcription factors