The evolving understanding of microRNA in bladder cancer.

PURPOSE

Micro ribonucleic acid (miR) expression is altered in urologic malignancies, including bladder cancer (BC). Individual miRs have been shown to modulate multiple signaling pathways that contribute to BC. We reviewed the primary literature on the role of miRs in BC; we provide a general introduction to the processing, regulation, and function of miRs as tumor suppressors and oncogenes and critically evaluate the literature on the implications of altered miR expression in BC.

MATERIALS AND METHODS

We searched the English language literature for original and review articles in PubMed from 1993 to March 2013, using the terms "microRNA" and "bladder cancer," "transitional cell carcinoma," or "urothelial carcinoma." This search yielded 133 unique articles with more than 85% of them published within the last 3 years.

RESULTS

To date, the majority of miR studies in BC use profiling to describe dynamic changes in miR expression across stage and grade. Generalized down-regulation of miRs, including those that target the fibroblast growth factor 3 pathway, such as miR-145, miR-101, miR-100, and miR-99a, has been observed in low-grade, non-muscle invasive BC. In contrast, generalized increased expression of miRs is observed in high-grade, muscle-invasive BC compared with adjacent normal bladder urothelium, including miRs predicted to target p53, such as miR-21 and miR-373. Furthermore, p53 suppresses transcriptional factors that promote mesenchymal differentiation, ZEB-1 and ZEB-2, through regulation of the miR200 family.

CONCLUSIONS

Aberrations in miR expression identified between non-muscle invasive BC and muscle-invasive BC provide insight into the molecular alterations known to distinguish the two parallel pathways of bladder carcinogenesis. The heterogeneity of tumor specimens and research methods limits the reproducibility of changes in miR expression profiles between studies and underscores the importance of in vivo validation in a field that utilizes in silico miR target-prediction models.