Abstract

MicroRNAs (miRNAs) have key roles in breast cancer progression, and their expression levels are heterogeneous across individual breast cancer patients. Traditional methods aim to identify differentially expressed miRNAs in populations rather than in individuals and are affected by the expression intensities of miRNAs in different experimental batches or platforms. Thus it is urgent to conduct miRNA differential expression analysis at an individual level for further personalized medicine research. We proposed a straightforward method to determine the differential expression of each miRNA in an individual patient by utilizing the reversal expression order of miRNA pairs between two conditions (cancer and normal tissue). We applied our method to breast cancer miRNA expression profiles from The Cancer Genome Atlas and two other independent data sets. In total, 292 miRNAs were differentially expressed in individual breast cancer patients. Using the differential expression profile of miRNAs in individual patients, we found that the deregulations of miRNA tend to occur in specific breast cancer subtypes. We investigated the coordination effect between the miRNA and its target, based on the hypothesis that one gene function can be changed by copy number alterations of the corresponding gene or deregulation of the miRNA. We revealed that patients exhibiting an upregulation of hsa-miR-92b and patients with deletions of PTEN did not tend to overlap, and hsa-miR-92b and PTEN coordinately regulated the pathway of ‘cell cycle' and so on. Moreover, we discovered a new prognostic signature, hsa-miR-29c, whose downregulation was associated with poor survival of breast cancer patients.

Highlights

  • MicroRNAs are short, endogenous non-coding RNAs that regulate gene expression by promoting mRNA degradation or repressing mRNA translation

  • Some studies have proposed new methods, such as the rank product,[3] which use the relative order of gene expression values within each sample, considering that the relative order is more robust against batch effects and insensitive to data normalization.[4,5]

  • The results showed that average values of sensitivity, specificity and F-score were 93.28, RESULTS Identification of reversal miRNA pairs in The Cancer Genome Atlas (TCGA) miRNA training data set

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Summary

Introduction

MicroRNAs (miRNAs) are short (approximately 22 nt), endogenous non-coding RNAs that regulate gene expression by promoting mRNA degradation or repressing mRNA translation. Using the miRNA reversal pairs derived from the training data set, we determined whether the 292 miRNAs were differentially expressed in individual patients of a testing data set containing 17 pair-wise breast cancer and normal samples (Table 1).

Results
Conclusion
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