MicroRNA–mRNA interactions underlying colorectal cancer molecular subtypes

Abstract

Colorectal cancer (CRC) transcriptional subtypes have been recently identified by gene expression profiling. Here we describe an analytical pipeline, microRNA master regulator analysis (MMRA), developed to search for microRNAs potentially driving CRC subtypes. Starting from a microRNA–mRNA tumour expression data set, MMRA identifies candidate regulator microRNAs by assessing their subtype-specific expression, target enrichment in subtype mRNA signatures and network analysis-based contribution to subtype gene expression. When applied to a CRC data set of 450 samples, assigned to subtypes by 3 different transcriptional classifiers, MMRA identifies 24 candidate microRNAs, in most cases downregulated in the stem/serrated/mesenchymal (SSM) poor prognosis subtype. Functional validation in CRC cell lines confirms downregulation of the SSM subtype by miR-194, miR-200b, miR-203 and miR-429, which share target genes and pathways mediating this effect. These results show that, by combining statistical tests, target prediction and network analysis, MMRA effectively identifies microRNAs functionally associated to cancer subtypes.

Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma

“…To evaluate this pipeline as a tool to uncover subtype regulators, we performed cross-validation using two independent transcriptional data sets: the TCGA data with 529 GBM cases (Brennan et al, 2013) and the study by Phillips et al of 100 glioma cases (Phillips et al, 2006). When compared to the Master regulator algorithm (MRA, a technique to predict TF (Carro et al, 2010) and miRNA (Cantini et al, 2015) regulators of cancer), aSICS was approximately twice as sensitive at any fixed level of false detections ( p  ≤ 0.001) (Fig. 1d).…”

“…In our exploration of additional cancers (Supplement), we selected three cancers for which there is clear evidence of molecular subtypes: ovarian adenocarcinoma (TCGA ‘OV’ cohort) Network et al (2011), a recent re-analysis of colorectal cancer from the (TCGA ‘COAD’ cohort) (Cantini et al, 2015) and breast cancer (TCGA ‘BRCA’ cohort) (Network et al, 2012), using the provided subtyping data in each respective publication. aSICS was run as above using the same settings as for GBM.…”

“…First, unlike previous tools for detection of subtype regulators (Cantini et al, 2015, Carro et al, 2010), it combines multiple types of data into a single network model, thus allowing us to efficiently screen for associations across several classes of molecular events along with sample data on mRNA and miRNA expression, DNA copy number aberrations (CNA) of genes and miRNAs, DNA methylation, loss of heterozygosity (LOH), point mutations, and clinical information. Secondly, the procedure is based on robust estimation of partial correlations to detect the coupling between two variables after correction for all the other variables.…”

Exosomes as the Promising Biomarker for Epstein-Barr Virus (EBV)-Associated Cancers

“…Cancer-derived exosomes carry RNAs particularly miRNAs that are implicated in cancer pathogenesis such as in breast and colorectal cancers [39,40]. In EBV-related cancers, the pathogenic role of RNAs such as miRNAs, mRNAs and Epstein-Barr virus-encoded small RNAs (EBERs) has drawn considerable attention in the past few years [17,41].…”

The pathomorphologist’s role in the era of personalised therapy regarding the case of colorectal tumours

“…This is a class of small, endogenous, single-stranded RNA, which play a role as post-transcriptive regulators taking part in carcinogenesis, invasion and progression of colorectal cancer [45,46]. The work published in 2014 by Stiegelbauer et al presents the characteristics of the miRNA studied so far together with their predictive value in the therapy of the patients with colorectal cancer.…”