Abstract

BackgroundMicroRNAs are a class of short regulatory RNAs that act as post-transcriptional fine-tune regulators of a large host of genes that play key roles in many cellular processes and signaling pathways. A useful step for understanding their functional role is characterizing their influence on the protein context of the targets. Using miRNA context-specific influence as a functional signature is promising to identify functional associations between miRNAs and other gene signatures, and thus advance our understanding of miRNA mode of action.ResultsIn the current study we utilized the power of regularized regression models to construct functional associations between gene signatures. Genes that are influenced by miRNAs directly(computational miRNA target prediction) or indirectly (protein partners of direct targets) are defined as functional miRNA gene signature. The combined direct and indirect miRNA influence is defined as context-specific effects of miRNAs, and is used to identify regulatory effects of miRNAs on curated gene signatures. Elastic-net regression was used to build functional associations between context-specific effect of miRNAs and other gene signatures (disease, pathway signatures) by identifying miRNAs whose targets are enriched in gene lists. As a proof of concept, elastic-net regression was applied on lists of genes downregulated upon pre-miRNA transfection, and successfully identified the treated miRNA. This model was then extended to construct functional relationships between miRNAs and disease and pathway gene lists. Integrating context-specific effects of miRNAs on a protein network reveals more significant miRNA enrichment in prostate gene signatures compared to miRNA direct targets. The model identified novel list of miRNAs that are associated with prostate clinical variables.ConclusionsElastic-net regression is used as a model to construct functional associations between miRNA signatures and other gene signatures. Defining miRNA context-specific functional gene signature by integrating the downstream effect of miRNAs demonstrates better performance compared to the miRNA signature alone (direct targets). miRNA functional signatures can greatly facilitate miRNA research to uncover new functional associations between miRNAs and diseases, drugs or pathways.

Highlights

  • MicroRNAs are a class of short regulatory RNAs that act as post-transcriptional fine-tune regulators of a large host of genes that play key roles in many cellular processes and signaling pathways

  • Identifying and characterizing reference biological concepts, for example miRNet separately to determine the initial variables (miRNAs) targets, overrepresented in a list of genes that results from biological experiments is a powerful methodology to characterize the function hidden in the gene list

  • [26], we showed that using the indirect targets of miRNA to represent the miRNA gene signature is effective to reveal the treating miRNAs from a set of downregulated genes upon pre-miRNA treatment

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Summary

Introduction

MicroRNAs are a class of short regulatory RNAs that act as post-transcriptional fine-tune regulators of a large host of genes that play key roles in many cellular processes and signaling pathways. Identifying and characterizing reference biological concepts, for example miRNA targets, overrepresented in a list of genes that results from biological experiments is a powerful methodology to characterize the function hidden in the gene list. This area of research which is known as gene enrichment analysis has gained a considerable body of research. Most of the 68 tools belong to the second group as they use statistical tests like fisher and hypergeometric tests to assess the overrepresentation of particular term Though these tools are well established as standard tools for enrichment analysis, we find these tools lack modular concept of gene lists. Integrating the interactions between gene sets to assess the overrepresentation is a promising direction to follow to gain system level understanding of gene enrichment analysis

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