Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer

Abstract

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. Dataset:The following are available online at

Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

“…Data was organized by [8] and released under creative commons license. Authors of the data-set provided N = 406 anonymized clinical samples containing gene expression values of 14 hub genes related to bladder cancer.…”

“…Nonetheless, these groups do not match perfectly the hub and seed sets, suggesting that genes among these two groups do not contribute equally to the informative content of the data-set. In the original paper [8], Dr. Zhang described an opposite behavior of CRYAB, TPM1, and CASQ2 genes compared to other hub genes. The negative correlation between these three genes and the other hub genes appears on the dendrogram, with their inclusion in the seed group.…”

“…The negative correlation between these three genes and the other hub genes appears on the dendrogram, with their inclusion in the seed group. Moreover, gene expression data was pre-selected by medical doctors that authored the original data-set [8], and feature selection techniques based on variance or correlation may not consider all the intuitions underlying their research. To preserve all the knowledge present in the data-set and reduce the feature space removing redundant information, dimensionality reduction was usually preferred to feature selection because it creates new synthetic features by combining the original ones.…”