Identification and bioinformatics analysis of miRNAs associated with human muscle invasive bladder cancer

Abstract

Accumulated evidence has indicated that micro (mi)RNAs play vital roles in the occurrence and development of human muscle invasive bladder cancer (MIBC), however, little is known about the miRNAs' regulatory networks. In the present study, the authors aimed to use bioinformatics analysis to identify the key miRNAs and potential target genes, as well as studying the underlying mechanisms for MIBC. They collected several human MIBC tissues to generate a miRNA expression analysis by microarray analysis comparing with normal bladder tissues, identifying 104 differentially expressed miRNAs (102 were downregulated and 2 were upregulated) and predicted 11,884 putative target genes of the dysregulated miRNAs. To understand the function of dysregulated miRNAs in the development of MIBC, networks among miRNAs and genes, gene ontologies and pathways were built. The subsequent bioinformatics analysis indicated that the mitogen‑associated protein kinase (MAPK) signaling pathway, apoptosis and pathways in cancer and the cell cycle, were significantly enriched Overall, these results provided comprehensive information on the biological function of dysregulated miRNAs in the development of MIBC. The identification of miRNAs and their putative targets may offer new diagnostic and therapeutic strategies for human muscle invasive bladder cancer.

Figure 1.

Differential expression of miRNAs between normal bladder and MIBC tissues. Heatmap of 104 microRNAs differentially expressed between three normal bladder and three MIBC tissues. MIBC, muscle invasive bladder cancer.

Figure 2.

Significantly changed GO/pathways of predicted target genes of dysregulated miRNAs. (A) Significantly changed GOs of predicted target genes. The y-axis shows GO category and the x-axis shows -log10 (P-value). (B) Significantly changed pathways of predicted target genes. The γ-axis shows significantly changed pathways. GO, gene ontology.

Figure 3.

Pathway network (Path-net). Significantly changed pathways were connected in a Path-net to show the interaction network among these pathways. Each pathway in the network was measured by counting the upstream and downstream pathways. The blue circle represents pathways involving upregulated miRNAs, while the yellow circle represents pathways involving both upregulated and downregulated miRNAs. The size of the circle represents the degree value and the lines show the interaction between pathways. A higher degree of pathway indicates that it plays a more important role in the signaling network. miRNAs, microRNAs.

Figure 4.

miRNAs-gene-network. According to the interactions between miRNAs and the intersected target genes, miRNAs-gene-network was constructed to illustrate the key regulatory functions of the identified miRNAs and their target genes. The blue circles represent genes, while blue square nodes represent downregulated miRNAs. The size of the circle or square node represents the degree value. A higher degree of gene/miRNAs indicates that it plays a more important role in the signaling network. miRNA/miR, microRNA.

Figure 5.

miRNAs-GO-network. The miRNAs-GO-network was generated according to the relationship of significant biological functions and miRNAs. The yellow and blue circles represent GOs, red square nodes represent upregulated miRNAs, and blue square nodes represent downregulated miRNAs. The size of the circle or square node represents the degree value. A higher degree of GO/miRNAs indicates that it plays a more important role in the signaling network. GO, gene ontology; miRNA/miR, microRNA.

Figure 6.

Validation of candidate miRNAs. Among the top rated nine miRNAs screened from our miRNA microarray, four miRNAs involving (A) hsa-miR-145-5p, (B) hsa-miR-497-5p, (C) hsa-miR-29a-3p and (D) hsa-miR-204-5p were also significantly altered in GSE40355. miRNA, microRNA.