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Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis.

ABSTRACT: MicroRNAs (miRNAs) comprise a gene-regulatory network through sequence complementarity with target mRNAs. Previous studies have shown that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by target mRNA destabilization. However, it has not yet been fully assessed whether this scheme is widely applicable to more realistic conditions with multiple miRNA fluctuations. By combining two analytical frameworks for detecting the enrichment of gene sets, Gene Set Enrichment Analysis (GSEA) and Functional Assignment of miRNAs via Enrichment (FAME), we developed GSEA-FAME analysis (GFA), which enables the prediction of miRNA activities from mRNA expression data using rank-based enrichment analysis and weighted evaluation of miRNA-mRNA interactions. This cooperative approach delineated a better widespread correlation between miRNA expression levels and predicted miRNA activities in cancer transcriptomes, thereby providing proof-of-concept of the mRNA-destabilization scenario. In an integrative analysis of The Cancer Genome Atlas (TCGA) multidimensional data including profiles of both mRNA and miRNA, we also showed that GFA-based inference of miRNA activity could be used for the selection of prognostic miRNAs in the development of cancer survival prediction models. This approach proposes a next-generation strategy for the interpretation of miRNA function and identification of target miRNAs as biomarkers and therapeutic targets.

SUBMITTER: Suzuki HI

PROVIDER: S-EPMC3597654 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis.

Suzuki Hiroshi I HI Mihira Hajime H Watabe Tetsuro T Sugimoto Koichi K Miyazono Kohei K

Nucleic acids research 20121228 5

MicroRNAs (miRNAs) comprise a gene-regulatory network through sequence complementarity with target mRNAs. Previous studies have shown that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by target mRNA destabilization. However, it has not yet been fully assessed whether this scheme is widely applicable to more realistic conditions with multiple miRNA fluctuations. By combining two analytical frameworks for detecting the enrichment of gene sets, Gene ...[more]

PMID: 23275554

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Project description:Background and objectiveAfter radical prostatectomy (RP), prostate cancer (PCa) patients may experience biochemical recurrence (BCR) and clinical recurrence, which remains a dominant issue in PCa treatment. The purpose of this study was to identify a protein-coding gene classifier associated with microRNA (miRNA)-mediated regulation to provide a comprehensive prognostic index to predict PCa recurrence after RP.MethodsCandidate classifiers were constructed using two machine-learning algorithms (a least absolute shrinkage and selector operation [LASSO]-based classifier and a decision tree-based classifier) based on a discovery cohort (n = 156) from The Cancer Genome Atlas (TCGA) database. After selecting the LASSO-based classifier based on the prediction accuracy, both an internal validation cohort (n = 333) and an external validation cohort (n = 100) were used to examined the classifier using survival analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox proportional hazards regression analyses. Functional enrichment analysis of co-expressed genes was carried out to explore the underlying moleculer mechanisms of the genes included in the classifier.ResultsWe constructed a three-gene classifier that included FAM72B, GNE, and TRIM46, and we identified four upstream prognostic miRNAs (hsa-miR-133a-3p, hsa-miR-222-3p, hsa-miR-1301-3p, and hsa-miR-30c-2-3p). The classifier exhibited a remarkable ability (area under the curve [AUC] = 0.927) to distinguish PCa patients with high and low Gleason scores in the discovery cohort. Furthermore, it was significantly associated with clinical recurrence (p < 0.0001, log rank statistic = 20.7, AUC = 0.733) and could serve as an independent prognostic factor of recurrence-free survival (hazard ratio: 1.708, 95% CI: 1.180-2.472, p < 0.001). Additionally, it was a predictor of BCR according to BCR-free survival analysis (p = 0.0338, log rank statistic = 4.51).ConclusionsThe three-gene classifier associated with miRNA-mediated regulation may serve as a novel prognostic biomarker for PCa patients after RP.

Dataset Information

Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis.

Publications

Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis.

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