Abstract
Different miRNA profiling protocols and technologies introduce differences in the resulting quantitative expression profiles. These include differences in the presence (and measurability) of certain miRNAs. We present and examine a method based on quantile normalization, Adjusted Quantile Normalization (AQuN), to combine miRNA expression data from multiple studies in breast cancer into a single joint dataset for integrative analysis. By pooling multiple datasets, we obtain increased statistical power, surfacing patterns that do not emerge as statistically significant when separately analyzing these datasets. To merge several datasets, as we do here, one needs to overcome both technical and batch differences between these datasets. We compare several approaches for merging and jointly analyzing miRNA datasets. We investigate the statistical confidence for known results and highlight potential new findings that resulted from the joint analysis using AQuN. In particular, we detect several miRNAs to be differentially expressed in estrogen receptor (ER) positive versus ER negative samples. In addition, we identify new potential biomarkers and therapeutic targets for both clinical groups. As a specific example, using the AQuN-derived dataset we detect hsa-miR-193b-5p to have a statistically significant over-expression in the ER positive group, a phenomenon that was not previously reported. Furthermore, as demonstrated by functional assays in breast cancer cell lines, overexpression of hsa-miR-193b-5p in breast cancer cell lines resulted in decreased cell viability in addition to inducing apoptosis. Together, these observations suggest a novel functional role for this miRNA in breast cancer. Packages implementing AQuN are provided for Python and Matlab: https://github.com/YakhiniGroup/PyAQN.
Highlights
MicroRNAs are endogenous, small non-coding RNAs (~22 nucleotides) that bind to target-specific sites most often found in the 3’-untranslated regions (UTRs) of target messenger RNAs
This work demonstrates a practical approach to the joint-analysis of multiple miRNA expression profiling datasets acquired with different measurement technologies
We further investigated this miRNA, hsa-miR-193b-5p, and experimentally show, in cell lines, that its expression level impacts the viability of tumor cells
Summary
MicroRNAs (miRNAs) are endogenous, small non-coding RNAs (~22 nucleotides) that bind to target-specific sites most often found in the 3’-untranslated regions (UTRs) of target messenger RNAs (mRNAs). Through this binding, miRNAs regulate gene expression by conferring inhibition of mRNA translation or mRNA degradation [1]. MiRNA expression profiling is an important tool for studying tumor biology and classification and serves as a basis for potential diagnostic and prognostic assessments [2,3,4]. Absolute expression differences are not necessarily linearly correlated with downstream effects of the expressed miRNA, subtle but consistent differences may be of greater biological importance. In one of the first genome-wide characterization studies of miRNA expression in breast cancer we identified 63 miRNAs differentially expressed between the two main clinically diverse groups of breast cancer, estrogen receptor (ER) positive and the ER negative tumors [11]
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