Interrelationship between miRNA and splicing factors in pancreatic ductal adenocarcinoma

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers because of diagnosis at late stage and inherent/acquired chemoresistance. Recent advances in genomic profiling and biology of this disease have not yet been translated to a relevant improvement in terms of disease management and patient's survival. However, new possibilities for treatment may emerge from studies on key epigenetic factors. Deregulation of microRNA (miRNA) dependent gene expression and mRNA splicing are epigenetic processes that modulate the protein repertoire at the transcriptional level. These processes affect all aspects of PDAC pathogenesis and have great potential to unravel new therapeutic targets and/or biomarkers. Remarkably, several studies showed that they actually interact with each other in influencing PDAC progression. Some splicing factors directly interact with specific miRNAs and either facilitate or inhibit their expression, such as Rbfox2, which cleaves the well-known oncogenic miRNA miR-21. Conversely, miR-15a-5p and miR-25-3p significantly downregulate the splicing factor hnRNPA1 which acts also as a tumour suppressor gene and is involved in processing of miR-18a, which in turn, is a negative regulator of KRAS expression. Therefore, this review describes the interaction between splicing and miRNA, as well as bioinformatic tools to explore the effect of splicing modulation towards miRNA profiles, in order to exploit this interplay for the development of innovative treatments. Targeting aberrant splicing and deregulated miRNA, alone or in combination, may hopefully provide novel therapeutic approaches to fight the complex biology and the common treatment recalcitrance of PDAC.

Keywords: PDAC; interaction; miRNA; splicing deregulation; splicing modulation.

Figure 1.

Clinical role of miRNAs in early PDAC detection, diagnosis, metastatic prediction, survival and treatment monitoring. The scheme shows different miRNAs, tissue and blood-derived, which could serve as biomarkers for discriminating the different stages of the disease, as well as for early diagnosis and metastasis prediction. Furthermore, some miRNAs could be associated with prognosis and monitoring of PDAC patients.

Figure 2.

The mechanism of splicing mediated by splicing factors. The splicing is initiated by binding of U1 at 5’SS and U2 at 3’SS, bending the intron segment. Both SFs (U1 and U2) then recruit another SF which induced loop formation, cleaved the intron segment and ligated the exons [14,15]. Splicing is regulated by splicing factors which bind the primary transcript at several regulatory sites. Several essential regulatory sites and splicing factors are presented in the right part. SRSF2 is particularly important in initiating splicing by facilitating U1 and U2 binding to the primary transcript while SF3B1 mediates U2 binding to BPS. After U1 and U4 detached from spliceosome, only SRSF2 and SF3B1 remain in the complex while the other SFs detached [59]. ESE: Exonic splicing enhancer; ESS: Exonic splicing suppressor; ISE and ISS: Intronic splicing enhancer/suppressor; 5’SS and 3’SS: 5’ or 3’ splice site; BPS: Branch point site; Py-tract: Polypyrimidine-tract.

Figure 3.

The interaction of relevant splicing factors in PDAC and their associated miRNA. PTBP1 and HnRNPK are considered as the relevant targets in pancreatic cancer and have demonstrated their interaction with miRNAs in PDAC (miR-124 and miR-223, respectively). Despite lack of evidence in PDAC, cervical cancer experiment demonstrated that SF3B1 inhibition resulted in extensive change in miRNA expression and potentially brings more profound effects than PTBP1 and HnRNPK [63,70,89,91].

Figure 4.

Bioinformatic pipeline for splicing factor and miRNA modulation discovery with RNA-seq data. Raw data or mapped BAM files are used as input for alternative splicing (AS) tools such as MISO, rMATs and SUPPA2 to identify differential alternative splicing events. Next, a motif analysis is performed to identify the splicing factor (SF) specific for that RNA isoform. Lastly, miRNA-target databases are used to retrieve possible miRNA targeting SF. Additional miRNA profile can be useful to detect miRNA modulation through SFs.