Integration of MicroRNA, mRNA, and Protein Expression Data for the Identification of Cancer-Related MicroRNAs

Abstract

MicroRNAs (miRNAs) are responsible for the regulation of target genes involved in various biological processes, and may play oncogenic or tumor suppressive roles. Many studies have investigated the relationships between miRNAs and their target genes, using mRNA and miRNA expression data. However, mRNA expression levels do not necessarily represent the exact gene expression profiles, since protein translation may be regulated in several different ways. Despite this, large-scale protein expression data have been integrated rarely when predicting gene-miRNA relationships. This study explores two approaches for the investigation of gene-miRNA relationships by integrating mRNA expression and protein expression data. First, miRNAs were ranked according to their effects on cancer development. We calculated influence scores for each miRNA, based on the number of significant mRNA-miRNA and protein-miRNA correlations. Furthermore, we constructed modules containing mRNAs, proteins, and miRNAs, in which these three molecular types are highly correlated. The regulatory interactions between miRNA and genes in these modules have been validated based on the direct regulations, indirect regulations, and co-regulations through transcription factors. We applied our approaches to glioblastomas (GBMs), ranked miRNAs depending on their effects on GBM, and obtained 52 GBM-related modules. Compared with the miRNA rankings and modules constructed using only mRNA expression data, the rankings and modules constructed using mRNA and protein expression data were shown to have better performance. Additionally, we experimentally verified that miR-504, highly ranked and included in the identified modules, plays a suppressive role in GBM development. We demonstrated that the integration of both expression profiles allows a more precise analysis of gene-miRNA interactions and the identification of a higher number of cancer-related miRNAs and regulatory mechanisms.

Aberrant expression of a five‐microRNA signature in breast carcinoma as a promising biomarker for diagnosis

“…Integrative computational bioinformatics approaches have been utilized as an effective tool to detect the potential outlier miRNAs in cancer . Furthermore, as a rational approach, preliminary detection of candidate miRNAs derived from large‐scale expression profiling data and low‐throughput experimental verification for the selected outlier miRNAs can be used to choose candidate miRNAs . In the present study, according to literature reviews and data mining for miRNAs involved in the pathogenesis of breast cancer, we selected the candidate miRNAs; to begin with, the candidate miRNAs not only should have been highly dysregulated in human BC but also they must have been easily detectable.…”

“…20,21 Furthermore, as a rational approach, preliminary detection of candidate miRNAs derived from large-scale expression profiling data and low-throughput experimental verification for the selected outlier miRNAs can be used to choose candidate miRNAs. 22,23 In the present study, according to literature reviews and data mining for were culled to be analyzed and confirmed by RT-qPCR.…”

Molecular Crosstalking among Noncoding RNAs: A New Network Layer of Genome Regulation in Cancer

“…Moreover, let-7a/b could indirectly suppress the β -catenin pathway, which in a different way could be activated by lncRNA FTHIP3 [111], but also is regulated by molecular axis miR-21-GAS5-miR-135b [112]. This unexpected crosstalking between ncRNA signaling could shed a light on expression relationships among ncRNAs and mRNAs, which have been frequently reported in cancer literature, but to date have not been satisfactorily explained [157–159]. This scenario is made more complex by the tissue-specific expression pattern of all ncRNAs, which could effectively influence the occurrence of specific interactions among ncRNAs.…”

Integrative Analysis Identifies Genetic Variants Associated With Autoimmune Diseases Affecting Putative MicroRNA Binding Sites

“…Therefore, levels of miRNAs and the cognate target mRNA would correlate only in certain tissues. In addition, under certain circumstances, the effect of an miRNA on its target can only be observed when the protein level is measured (Li et al, 2015 ; Seo et al, 2017 ).…”