Integrated technologies in the post-genomic era for discovery of bladder cancer urinary markers

Abstract

The incidence of bladder cancer (BC) continues to rise with high recurrence and mortality rate, especially in the past three decades. The development of accurate and successful BC treatment relies mainly on early diagnosis. BC is a heterogeneous disease reflected by the presence of many potential biomarkers associated with different disease phenotypes. Nowadays, cystoscopy and urinary cytology are considered the gold standard diagnostic tools for BC. There are many limitations to cystoscopy including being invasive, labor-intensive and carcinoma in situ of the bladder may easily be missed. Urinary cytology is still a noninvasive technique with high accuracy in high-grade BC with a median sensitivity of 35%. Furthermore, the need for a sensitive, specific, non invasive, easily accessible BC biomarker is a major clinical need. The field of urinary BC biomarkers discovery is still a rapidly evolving discipline in which more recent technologies are evaluated and often optimized if they are not clinically significant to the urologists. Most of the current strategies for BC urinary biomarker detection depend on integration of information gleaned from the fields of genomics, transcriptomics, proteomics, epigenetics, metabolomics and bionanotechnology. Effort is currently being made to identify the most potentially beneficial urinary biomarkers. The purpose of this review is to summarize and explore the efficacy of gathering the information revealed from the cooperation of different omic strategies that paves the way towards various urinary markers discovery for screening, diagnosis and prognosis of human BC.

Keywords: Bladder cancer, Urinary biomarkers, Genomics, Proteomics, Bionanotechnology, Metabolomics, Transcriptomics, Epigenetics

Core tip: Capturing information from in silico data, proteomic data, gene expression data and bionanotechnology data outlines a promising approach to discover significant urinary biomarkers whose activity patterns are discriminative of bladder cancer vs control.