Integrated miRNA and mRNA expression profiling of the inflammatory breast cancer subtype

Abstract

Background: MicroRNAs (miRNAs) are key regulators of gene expression. In this study, we explored whether altered miRNA expression has a prominent role in defining the inflammatory breast cancer (IBC) phenotype.

Methods: We used quantitative PCR technology to evaluate the expression of 384 miRNAs in 20 IBC and 50 non-IBC samples. To gain understanding on the biological functions deregulated by aberrant miRNA expression, we looked for direct miRNA targets by performing pair-wise correlation coefficient analysis on expression levels of 10 962 messenger RNAs (mRNAs) and by comparing these results with predicted miRNA targets from TargetScan5.1.

Results: We identified 13 miRNAs for which expression levels were able to correctly predict the nature of the sample analysed (IBC vs non-IBC). For these miRNAs, we detected a total of 17,295 correlated miRNA-mRNA pairs, of which 7012 and 10 283 pairs showed negative and positive correlations, respectively. For four miRNAs (miR-29a, miR-30b, miR-342-3p and miR-520a-5p), correlated genes were concordant with predicted targets. A gene set enrichment analysis on these genes demonstrated significant enrichment in biological processes related to cell proliferation and signal transduction.

Conclusions: This study represents, to the best of our knowledge, the first integrated analysis of miRNA and mRNA expression in IBC. We identified a set of 13 miRNAs of which expression differed between IBC and non-IBC, making these miRNAs candidate markers for the IBC subtype.

Figure 1

Hierarchical clustering of 20 IBC and 50 non-IBC samples according to the expression pattern of the 50 most varying miRNAs. Expression values for these 50 miRNAs are represented in a matrix format, with rows indicating miRNAs and columns indicating samples. High expression values are colour-coded red and low expression values are colour-coded blue. Three robust sample clusters were identified, which were significantly associated with ER expression and histological grade. In particular, the combined first two (blue and yellow) sample clusters were enriched for ER⁺ breast tumours (80% of samples) when compared with the third (red) sample cluster (50% of samples) (P χ²=0.028). Notably, in the first (blue) sample cluster, 20% of samples were poorly differentiated compared with 60% of samples in the second (yellow) sample cluster (P χ²=0.004), suggestive of a subdivision of ER⁺ breast tumours according to the luminal A and luminal B subtype. No association of sample clustering with the difference between IBC and non-IBC was observed: 30, 25 and 45% of IBC samples grouped together in the first (blue), second (yellow) and third (red) sample cluster, respectively (P χ²=0.082). (The colour reproduction of this figure is available on the html full text version of the manuscript.)

Figure 2

Distribution of miRNA expression values in (A) inflammatory breast tumours and (B) non-inflammatory breast tumours.

Figure 3

Kaplan–Meier survival analysis of miR-520a-5p target gene expression in breast cancer with distant metastasis-free (A and C) and overall survival (B and D) as outcome. Kaplan–Meier curves are shown for the data sets of van de Vijver et al (2002) (A and B) and of Desmedt et al (2007) (C and D).