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.
Highlights
MicroRNAs are small non-coding RNAs, 20–24 nucleotides long, which can suppress target gene expression post-transcriptionally by recognizing the complementary target sites in the 3’ untranslated region (3’-UTR) of mRNAs [1]
We used mRNA, miRNA, and protein expression datasets of GBM obtained from The Cancer Genome Atlas (TCGA) [23]. mRNA expression datasets containing 192 tumor samples and 10 unmatched normal samples, miRNA expression data, containing 192 tumor samples and 10 unmatched normal samples, and protein expression datasets containing 192 tumor samples were included in this study
MRNA and protein expression profiles of the same genes can differ, as a consequence of different regulatory processes, such as RNA secondary structure, the effects of the regulatory proteins, and codon bias [13,14,15], which can affect the levels of translation
Summary
MicroRNAs (miRNAs) are small non-coding RNAs, 20–24 nucleotides long, which can suppress target gene expression post-transcriptionally by recognizing the complementary target sites in the 3’ untranslated region (3’-UTR) of mRNAs [1]. MiRNAs perfectly or partially complement target mRNA sequences, leading to mRNA degradation or the suppression of translation [2]. The relationships between miRNAs and the target genes are complex, since multiple miRNAs target multiple mRNAs [3, 4]. The importance of miRNAs for cancer development and progression has been demonstrated. The elucidation of their oncogenic or tumor suppressive functions and the identification of miRNAs that may represent potential targets for cancer therapy are, crucial tasks
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