Integrated analysis of microRNA regulatory network in nasopharyngeal carcinoma with deep sequencing

Abstract

Background: MicroRNAs (miRNAs) have been shown to play a critical role in the development and progression of nasopharyngeal carcinoma (NPC). Although accumulating studies have been performed on the molecular mechanisms of NPC, the miRNA regulatory networks in cancer progression remain largely unknown. Laser capture microdissection (LCM) and deep sequencing are powerful tools that can help us to detect the integrated view of miRNA-target network.

Methods: Illumina Hiseq2000 deep sequencing was used to screen differentially expressed miRNAs in laser-microdessected biopsies between 12 NPC and 8 chronic nasopharyngitis patients. The result was validated by real-time PCR on 201 NPC and 25 chronic nasopharyngitis patients. The potential candidate target genes of the miRNAs were predicted using published target prediction softwares (RNAhybrid, TargetScan, Miranda, PITA), and the overlay part was analyzed in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) biological process. The miRNA regulatory network analysis was performed using the Ingenuity Pathway Analysis (IPA) software.

Results: Eight differentially expressed miRNAs were identified between NPC and chronic nasopharyngitis patients by deep sequencing. Further qRT-PCR assays confirmed 3 down-regulated miRNAs (miR-34c-5p, miR-375 and miR-449c-5p), 4 up-regulated miRNAs (miR-205-5p, miR-92a-3p, miR-193b-3p and miR-27a-5p). Additionally, the low level of miR-34c-5p (miR-34c) was significantly correlated with advanced TNM stage. GO and KEGG enrichment analyses showed that 914 target genes were involved in cell cycle, cytokine secretion and tumor immunology, and so on. IPA revealed that cancer was the top disease associated with those dysregulated miRNAs, and the genes regulated by miR-34c were in the center of miRNA-mRNA regulatory network, including TP53, CCND1, CDK6, MET and BCL2, and the PI3K/AKT/ mTOR signaling was regarded as a significant function pathway in this network.

Conclusion: Our study presents the current knowledge of miRNA regulatory network in NPC with combination of bioinformatics analysis and literature research. The hypothesis of miR-34c regulatory pathway may be beneficial in guiding further studies on the molecular mechanism of NPC tumorigenesis.

Fig. 1

MiRNA differential expression. a Laser capture microdessection of H&E-stained slides (×10). b Heatmap of normalized miRNA reads that are differentially expressed between NPC and controls (sequencing-read count ratio ≥ 2.0, P < 0.05). Samples are well divided into control and NPC patient groups

Fig. 2

Validation of deregulated miRNAs by qRT-PCR. a An independent validation cohort included 65 NPC patients and 20 healthy control subjects. b Dynamic expression of supregulated miRNA. c Dynamic expressions of downregulated miRNA. MicroRNA abundance was normalised to U6 RNA. Statistical analysis was performed using the t-tests. *P < 0.01

Fig. 3

MiRNAs regulatory networks by IPA. a The miRNA-mRNA interaction network. miR-34 family targeted most genes and the relationship between it and 5 highlighted genes (CCND1, TP53, BCL2, MET, CDK6) was experimentally confirmed. b TP53-centered network showed TP53 could be treated as diagnostic marker of NPC and was involved in cell cycle regulation and mTOR signaling. c CCND1- centered network indicated CCND1 was involved in both p53 signaling and PI3K/AKT/mTOR signaling. d BCL2-centered network showed BCL2 acted significantly in molecular mechanisms of cancer and functioned through apotosis signaling

Fig. 4

MiR-34c levels during NPC progression and target genes validation. a The expression level of miR-34c-5p in human NPC specimens compared with control biopsy samples. b miR-34c-5p expression was higher in stage I, whereas stages II-IV had lower levels. c The protein expression levels of MET, CCND1, CDK6 and BCL2 in miR-34c mimic transfected CNE-2 cells were lower than the controls. Western blot was independently repeated at least three times. MicroRNA abundance was normalised to U6 RNA. Statistical analysis was performed using the t-tests (a, c) and the one-way ANOVA (b)

Fig. 5

MiR-34c regulatory network. Blue fonts represent tumor supressor factors while red fonts represent oncogenic factors. The interaction between miRNAs and mRNAs demonstrate miR-34c-5p might regulates NPC cellular growth and migration, apoptosis via these genes and functions through PI3K/AKT/mTOR signaling. → Direct Stimulatory Modification, → → Multistep Stimulatory Modification, −Direct Inhibitory Modification, −Tentative Associated Modification