Identification of Co-Deregulated Genes in Urinary Bladder Cancer Using High-Throughput Methodologies

• Main Authors: George I. Lambrou, Kleanthis Vichos, Dimitrios Koutsouris, Apostolos Zaravinos
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Applied Sciences
• Subjects: urinary bladder cancer; microarray; common gene expression; unsupervised machine learning algorithms
• Online Access: https://www.mdpi.com/2076-3417/11/4/1785

Although several genes are known to be deregulated in urinary bladder cancer (UBC), the list of candidate prognostic markers has expanded due to the advance of high-throughput methodologies, but they do not always accord from study to study. We aimed to detect global gene co-expressional profiles among a high number of UBC tumors. We mined gene expression data from 5 microarray datasets from GEO, containing 131 UBC and 15 normal samples. Data were analyzed using unsupervised classification algorithms. The application of clustering algorithms resulted in the isolation of 6 down-regulated genes (<i>TMP2, ACTC1, TAGLN, MFAP4, SPARCL1,</i> and <i>GLP1R</i>), which were mainly implicated in the proteasome, base excision repair, and DNA replication functions. We also detected 6 up-regulated genes (<i>CDC20, KRT14, APOBEC3B, MCM5, STMN,</i> and <i>YWHAB</i>) mainly involved in cancer pathways. We identified lists of drugs that could potentially associate with the Differentially Expressed Genes (DEGs), including Vardenafil, Pyridone 6, and Manganese (co-upregulated genes) or 1D-myo-inositol 1,4,5-triphosphate (co-down regulated genes). We propose 12 novel candidate markers for UBC, as well as potential drugs, shedding more light on the underlying cause of the development and progression of the disease.