Identification of Core Genes and Key Pathways via Integrated Analysis of Gene Expression and DNA Methylation Profiles in Bladder Cancer
Yongzhen Zhang, Liang Fang, Yuanwei Zang, Zhonghua Xu
(Department of Urology, Qilu Hospital, Shandong University, Jinan, Shandong, China (mainland))
Med Sci Monit 2018; 24:3024-3033
Bladder cancer (BC) is the most common urological malignant tumor. In BC, aberrant DNA methylation is believed to be associated with carcinogenesis. Therefore, the identification of key genes and pathways could help determine the potential molecular mechanisms of BC development.
MATERIAL AND METHODS: Microarray data on gene expression and gene methylation were downloaded from the Gene Expression Omnibus (GEO) database. Abnormal methylated/expressed genes were analyzed by GEO2R and statistical software R. Gene Ontology term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID database and KOBAS 3.0. STRING and Cytoscape software were used to construct protein–protein interaction (PPI) networks and analyze modules of the PPI network.
RESULTS: A total of 71 hypomethylated/upregulated genes were significantly enriched in cell–cell adhesion and blood vessel development. KEGG pathway analysis highlighted p53 signaling and metabolic pathways. Five core genes in the PPI network were determined: CDH1, DDOST, CASP8, DHX15, and PTPRF. Additionally, 89 hypermethylated/downregulated genes were found. These genes were enriched mostly in cell adhesion and signal transduction. KEGG pathway analysis revealed enrichment in focal adhesion. The top 5 core genes in the PPI network were GNG4, ADCY9, NPY, ADRA2B, and PENK. We found most of the core genes were also significantly altered in the Cancer Genome Atlas database.
CONCLUSIONS: Abnormal methylated/expressed genes and key signaling pathways involved in BC were identified through integrated bioinformatics analysis. In the future, these genes may serve as biomarkers for diagnosis and therapeutic targets in BC.
Keywords: Computational Biology, Signal Transduction, Urinary Bladder Neoplasms