Identification of Co-Deregulated Genes in Urinary Bladder Cancer Using High-Throughput Methodologies
Author | Lambrou, George I. |
Author | Vichos, Kleanthis |
Author | Koutsouris, Dimitrios |
Author | Zaravinos, Apostolos |
Available date | 2021-02-21T07:24:13Z |
Publication Date | 2021-02-18 |
Publication Name | Applied Sciences |
Identifier | http://dx.doi.org/10.3390/app11041785 |
Citation | Lambrou, G.I.; Vichos, K.; Koutsouris, D.; Zaravinos, A. Identification of Co-Deregulated Genes in Urinary Bladder Cancer Using High-Throughput Methodologies. Appl. Sci. 2021, 11, 1785. https://doi.org/10.3390/app11041785 |
Abstract | Urinary bladder cancer (UBC) is the second most common urogenital solid tumor and the eleventh in the rank among all types of solid tumors. Although several oncogenes and tumor suppressors are known to be implicated in the disease, the list of candidate prognostic markers has recently expanded, as a result of the power of new high-throughput methodologies. The prognosis and therapy of UBC have progressed greatly during the last years. However, a majority of the different tumor subtypes still relapses, manifesting poor prognosis. Here, we identified gene expression patterns being common across different histological phenotypes of UBC. Such an approach could be useful in the discovery of prognostic and therapeutic targets able to be applied in the majority of the tumor’s subtypes. |
Language | en |
Publisher | MDPI |
Subject | urinary bladder cancer microarray common gene expression unsupervised machine learning algorithms |
Type | Article |
Issue Number | 4 |
Volume Number | 11 |
ESSN | 2076-3417 |
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