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Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer

Department of Urology, University of Leipzig, 04103 Leipzig, Germany
Author to whom correspondence should be addressed.
Received: 13 March 2020 / Revised: 8 April 2020 / Accepted: 10 April 2020 / Published: 16 April 2020
(This article belongs to the Special Issue Benchmarking Datasets in Bioinformatics)
Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up- and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa. View Full-Text
Keywords: bioinformatics; bladder cancer; biomarker; data bioinformatics; bladder cancer; biomarker; data
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MDPI and ACS Style

Zhang, C.; Berndt-Paetz, M.; Neuhaus, J. Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer. Data 2020, 5, 38.

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