Gene Network Profiling in Muscle-Invasive Bladder Cancer: A Systematic Review and Meta-Analysis - Beyond the Abstract

Technology advances have enabled us to examine the expression of several genes simultaneously. As such, gene-expression profiling enables us to evaluate not only the patterns of gene expression but potentially identify many as prognostic markers. Determination of distinctive genetic signatures can support patient risk stratification, treatment allocation, and clinical trials. Our present study “Gene network profiling in muscle-invasive bladder cancer: A systematic review and meta-analysis of the existing gene expression data aims to facilitate the identification of a range of predictive biomarkers for muscle-invasive bladder cancer within this project's scope. Constant and reliable differential expression though may indicate a role of a gene as a driver, and the defined marker gene candidates should be experimentally validated. Accurately predicting the risk of invasiveness of muscle-invasive bladder cancer could also enrich patient quality of life.

There is currently an abundance of online databases holding microarray and RNA sequencing (RNA-seq) data, which can be used in a context-query-driven manner and provide information on gene expression, giving information on upregulation and downregulation of genes within different tissue types. In this study, we used a number of applications for meta-analysis of gene expression, alongside interactions data to validate the general significance of already suggested markers. We have analyzed several Gene Expression Omnibus datasets representing different contrasts of gene expression in muscle-invasive bladder cancer compared to normal bladder tissue as a means to identify potential prognostic and therapeutic targets for muscle-invasive bladder cancer.

 The results of our meta-analysis have identified a total of nine genes as potential therapeutic targets in MIBC. Six genes are reported as upregulated (ProTα, SPINT1, UBE2E1, RAB25, KPNB1, HDAC1) and 3 genes as downregulated (NUP188, IPO13, NUP124). Genes were also found to be involved in “ubiquitin mediated proteolysis,” “protein processing in endoplasmic reticulum,” “transcriptional misregulation in cancer,” and “RNA transport” pathways. Further analysis and validation are required for the gene-markers and interactions suggested in this study. Crosstalk between regulatory pathways and candidate gene factors is likely to define a complex landscape for the invasiveness of muscle-invasive bladder cancer and should be considered for complex treatments and a personalized medicine approach. In conclusion, a study of large databases of genetic information, coupled with translational advances should permit selective investigations of predictive factors and permit more efficient application in clinical oncology.

Written by: Ilaha Isali, MD, & Laura Bukavina, MD, MPH, Case Western Reserve University, Cleveland, OH

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