Objective: This study aimed to identify the novel microRNAs (miRNAs) for early diagnosis of bladder cancer. Materials and Methods: Differentially expressed miRNAs between early and advanced bladder cancer were identified by differential expression analysis, using miRNA-seq data from The Cancer Genome Atlas (TCGA). The optimal subset of feature miRNAs for pathologic stage prediction was acquired by Random Forest algorithm and was used to construct a support vector machine (SVM) classifier. The performance of the SVM classifier in predicting the progression of bladder cancer samples was validated using an independent validating dataset. An miRNA-regulated target gene network was finally constructed and functional annotation were performed for the target genes. Results: A total of 52 significantly differentially expressed miRNAs were identified between early and advanced bladder cancer samples and 17 of these miRNAs were identified to be feature miRNAs. The 17 feature miRNAs were used to construct an SVM classifier, which showed a high performance in pathologic stage prediction for both training and validating dataset. Besides, our functional annotation analysis showed that the feature miRNAs were significantly involved in biological processes and pathways related to extracellular matrix process and PI3K/Akt signaling. Conclusions: The optimal subset of miRNAs may act as potential therapeutic targets and diagnostic markers of bladder cancer.
Keywords: bladder cancer; miRNA; pathologic stage; support vector machine.