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. 2011 Feb 22;6(2):e16915.
doi: 10.1371/journal.pone.0016915.

miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors

Espen Enerly  1 , Israel Steinfeld, Kristine Kleivi, Suvi-Katri Leivonen, Miriam R Aure, Hege G Russnes, Jo Anders Rønneberg, Hilde Johnsen, Roy Navon, Einar Rødland, Rami Mäkelä, Bjørn Naume, Merja Perälä, Olli Kallioniemi, Vessela N Kristensen, Zohar Yakhini, Anne-Lise Børresen-Dale
Affiliations

miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors

Espen Enerly et al. PLoS One. .

Abstract

Introduction: Few studies have performed expression profiling of both miRNA and mRNA from the same primary breast carcinomas. In this study we present and analyze data derived from expression profiling of 799 miRNAs in 101 primary human breast tumors, along with genome-wide mRNA profiles and extensive clinical information.

Methods: We investigate the relationship between these molecular components, in terms of their correlation with each other and with clinical characteristics. We use a systems biology approach to examine the correlative relationship between miRNA and mRNAs using statistical enrichment methods.

Results: We identify statistical significant differential expression of miRNAs between molecular intrinsic subtypes, and between samples with different levels of proliferation. Specifically, we point to miRNAs significantly associated with TP53 and ER status. We also show that several cellular processes, such as proliferation, cell adhesion and immune response, are strongly associated with certain miRNAs. We validate the role of miRNAs in regulating proliferation using high-throughput lysate-microarrays on cell lines and point to potential drivers of this process.

Conclusion: This study provides a comprehensive dataset as well as methods and system-level results that jointly form a basis for further work on understanding the role of miRNA in primary breast cancer.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: ZY and RN are employed by Agilent Technologies. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Subtype specific differential miRNA expression.
(A) Hierarchical clustering of the 100 most variable (largest variance values) miRNAs across 101 tumors with dendrograms illustrating similarities between samples/genes. The yellow box indicates members of the miR-17-92 clusters that have higher expression in the basal-like subtype. The color in the bars beneath the heatmap illustrates the different subtypes as defined by centroid correlation and EMT classes . Black boxes represent ER- and TP53 mutant samples. (B) miRNAs differentially expressed between basal-like and luminal-A subtypes. miRNAs ordered by significance of differential expression between 15 basal-like and 41 luminal-A samples. The 26 most significant of 77 miRNAs (p<0.0001) are shown in the tables with number of misclassified samples (TNoM), TNoM p-value, and t-test p-value. For visualization expression values of each miRNA were linearly stretched to a scale of -2.0 to 2.0.
Figure 2
Figure 2. miRNAs differentially expressed between TP53 WT and mutated samples.
miRNAs ordered by significance of differential expression between TP53 WT and mutant samples. Color boxes illustrate molecular subtype, and estrogen receptor status in black and white, while gray boxes represent unclassified samples. The 16 most significant of 44 miRNAs (p<0.0001) are shown in the tables with number of misclassified samples (TNoM), TNoM p-value, and t-test p-value. For visualization expression values of each miRNA were linearly stretched to a scale of -2.0 to 2.0.
Figure 3
Figure 3. Expression of miR-150 and its mRNA correlates.
(A) The samples are ordered according to the expression levels of miR-150 for that sample. The absolute signal intensities of miR-150 are presented in the top panel. The top 50 correlated genes are sorted from top to bottom. The color in the bar beneath the heatmap indicates the different subtypes. As can be seen many luminal-A samples have low levels of miR-150 expression though no clear cut can be deduced to separate the luminal-A samples from the rest of the subtype samples. (B) Graphical representation of GO-term enrichment of genes positively correlated to miR-150. The strongest enrichment is seen for the “immune response” (p<1.2E-147) term. The graph is color coded according to degree of enrichment. Figure obtained using the GOrilla web tool .
Figure 4
Figure 4. Proliferation associated miRNAs.
The panels show miRNAs that are both positively and negatively associated with proliferation in in vivo profiling from tumors. (A) Immunohistochemistry staining of Ki67 of tumors scored as highly proliferative (HP, sample 267) and low proliferative (LP, sample 570). The right panel shows the signal distribution in the 101 samples for three selected miRNAs, miR-142-3p, miR-19a, and 449a, with signal intensities for sample 570 (red dot) and 627 (green dot) highlighted. (B) Volcano plot of all miRNAs with TNoM p-value of differential expression against fold change differences in low (24 samples) versus high (35 samples) proliferation groups. Pink dots represent significant miRNAs (p<0.001). Left part contains miRNAs that are up-regulated in highly proliferative samples and right part down-regulated miRNAs. (C) The plot shows the scores for each miRNA, where each miRNA is represented by a dot. On the Y-axis differential expression score is –log(p-value) if miRNA is up-regulated and log(p-value) if miRNA is down regulated, thus assigning positive and negative scores according to differential expression between the high and low proliferative groups. Significance of differential expression is calculated using TNoM as described in Materials and Methods. “Cell-Cycle” (CC) score is –log(p-value) if the CC genes are enriched within the genes positively correlated to the miRNA, and log(p-value) if the CC genes are enriched within the genes negatively correlated to the miRNA. p-value for CC enrichment is calculated using the mHG statistic as described in Materials and Methods.
Figure 5
Figure 5. Proliferation assay in miRNA transfected cell-lines.
The panels show miRNAs that are both positively and negatively associated with proliferation in transfected cell-lines. (A) list of miRNAs that showed a significant effect on proliferation in cell-lines with a corresponding differential expression in tumors. (* indicates opposite effect in MCF-7 and BT-474). (B) Lysate microarray (LMA) screening of MCF-7 and BT-474 cells transfected with 20 nM human Pre-miR™ miRNA Precursor library v2. Ki67 readout after 48 and 72 hours. The two miRNAs from panel A with strongest positive and negative effect on proliferation for each cell line are shown (see Table S7 for complete list)
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