Signatures containing miR-133a identified by large scale miRNA profiling in bladder cancer

Background: Identifying bladder cancer-specific miRNA expression signatures by large scale miRNA profiling Methods：30 bladder cancer (BC) tissue samples and matched adjacent normal bladder tissue samples from patients with BC were collected and were divided into two groups； a training group and a blind testing group. Expressions of 1900 miRNAs and controls were detected in a BC miRNA pool and in a normal miRNAs pool, respectively. 380 differential expressed miRNAs between the BC miRNA pool and the normal miRNA pool were selected. The primers for detecting the 380 selected miRNAs and controls were used to generate one 384-well panel. This panel was used to profile miRNA expression of each individual sample in the training group and in the blind testing group. Data analysis was performed using a machine learning approach of a support vector machine classifier with a Student's t-test feature selection procedure. Results： We identified signatures consisting of three or four miRNAs that could distinguish BC from normal controls with an accuracy of 100% in the training model and an accuracy of over 95% in the blind test. All identified signatures contain hsa-miR-133a. We also revealed that the miRNA183-96 cluster and the miR200 cluster are both significantly up-regulated in BC. Conclusions：The identified signatures containing hsa-miR-133a could be used as biomarkers in the diagnosis and prognosis of BC.