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Integrated analysis of microRNA and mRNA expression: adding biological significance to microRNA target predictions.

van Iterson M, Bervoets S, de Meijer EJ, Buermans HP, 't Hoen PA, Menezes RX, Boer JM - Nucleic Acids Res. (2013)

Bottom Line: The strongest negatively associated mRNAs as measured by the test were prioritized.We applied our integration method to a well-defined muscle differentiation model.Using the same study, we showed the advantages of the global test over Pearson correlation and lasso.

View Article: PubMed Central - PubMed

Affiliation: Center for Human and Clinical Genetics and Leiden University Medical Center, Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, 2300 ZC Leiden, The Netherlands, Netherlands Bioinformatics Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands, Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands and Department of Pediatric Oncology, Erasmus Medical Center - Sophia Children's Hospital, Dr. Molewaterplein 60, 3015 GJ Rotterdam, The Netherlands.

ABSTRACT
Current microRNA target predictions are based on sequence information and empirically derived rules but do not make use of the expression of microRNAs and their targets. This study aimed to improve microRNA target predictions in a given biological context, using in silico predictions, microRNA and mRNA expression. We used target prediction tools to produce lists of predicted targets and used a gene set test designed to detect consistent effects of microRNAs on the joint expression of multiple targets. In a single test, association between microRNA expression and target gene set expression as well as the contribution of the individual target genes on the association are determined. The strongest negatively associated mRNAs as measured by the test were prioritized. We applied our integration method to a well-defined muscle differentiation model. Validation of our predictions in C2C12 cells confirmed predicted targets of known as well as novel muscle-related microRNAs. We further studied associations between microRNA-mRNA pairs in human prostate cancer, finding some pairs that have been recently experimentally validated by others. Using the same study, we showed the advantages of the global test over Pearson correlation and lasso. We conclude that our integrated approach successfully identifies regulated microRNAs and their targets.

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Comparison of global test, correlation and lasso for the prioritization of miRNAs and their targets. The x-axis represents the global test z-score (global test statistic transformed to z-scores) for each miRNA. The miRNAs are ordered according to the global test statistic, with increasing significance from left to right, and the vertical line separates not significant (left) from significant (right) associations, all according to the global test. The vertical stacks of points represent the global test statistics separately for each target, colored according to significance (black if significant, i.e. P-value < 0.001 after multiple testing correction using Benjamini–Hochberg’s FDR; grey otherwise). The size of each point reflects the absolute correlation coefficient. Red squares indicate miRNA–mRNA pairs selected by lasso, so that in each vertical column of squares the red ones represent mRNA targets with a non-zero lasso-regression coefficient.
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gkt525-F5: Comparison of global test, correlation and lasso for the prioritization of miRNAs and their targets. The x-axis represents the global test z-score (global test statistic transformed to z-scores) for each miRNA. The miRNAs are ordered according to the global test statistic, with increasing significance from left to right, and the vertical line separates not significant (left) from significant (right) associations, all according to the global test. The vertical stacks of points represent the global test statistics separately for each target, colored according to significance (black if significant, i.e. P-value < 0.001 after multiple testing correction using Benjamini–Hochberg’s FDR; grey otherwise). The size of each point reflects the absolute correlation coefficient. Red squares indicate miRNA–mRNA pairs selected by lasso, so that in each vertical column of squares the red ones represent mRNA targets with a non-zero lasso-regression coefficient.

Mentions: Approaches recently proposed to jointly analyse miRNA and target mRNAs have used Pearson correlation and lasso (13,14). In addition to a qualitative comparison summarized in Supplementary Table S10, we performed a comprehensive quantitative comparison between these methods and the global test, illustrated with the prostate cancer data set (see section ‘Prioritization of microRNAs and their targets: quantitative comparison of global test, correlation and lasso’ of the Supplementary Material). We showed that the global test yields better prioritization of miRNAs by taking all of their targets into account to produce a P-value (Figure 5). In contrast, lasso aims at sparsity, so may ignore target mRNAs with relatively large association with the miRNA under study. Pearson correlation only yields results per pair, so objective prioritization of relevant miRNAs would involve stricter multiple testing correction, and thus less power.Figure 5.


Integrated analysis of microRNA and mRNA expression: adding biological significance to microRNA target predictions.

van Iterson M, Bervoets S, de Meijer EJ, Buermans HP, 't Hoen PA, Menezes RX, Boer JM - Nucleic Acids Res. (2013)

Comparison of global test, correlation and lasso for the prioritization of miRNAs and their targets. The x-axis represents the global test z-score (global test statistic transformed to z-scores) for each miRNA. The miRNAs are ordered according to the global test statistic, with increasing significance from left to right, and the vertical line separates not significant (left) from significant (right) associations, all according to the global test. The vertical stacks of points represent the global test statistics separately for each target, colored according to significance (black if significant, i.e. P-value < 0.001 after multiple testing correction using Benjamini–Hochberg’s FDR; grey otherwise). The size of each point reflects the absolute correlation coefficient. Red squares indicate miRNA–mRNA pairs selected by lasso, so that in each vertical column of squares the red ones represent mRNA targets with a non-zero lasso-regression coefficient.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3753644&req=5

gkt525-F5: Comparison of global test, correlation and lasso for the prioritization of miRNAs and their targets. The x-axis represents the global test z-score (global test statistic transformed to z-scores) for each miRNA. The miRNAs are ordered according to the global test statistic, with increasing significance from left to right, and the vertical line separates not significant (left) from significant (right) associations, all according to the global test. The vertical stacks of points represent the global test statistics separately for each target, colored according to significance (black if significant, i.e. P-value < 0.001 after multiple testing correction using Benjamini–Hochberg’s FDR; grey otherwise). The size of each point reflects the absolute correlation coefficient. Red squares indicate miRNA–mRNA pairs selected by lasso, so that in each vertical column of squares the red ones represent mRNA targets with a non-zero lasso-regression coefficient.
Mentions: Approaches recently proposed to jointly analyse miRNA and target mRNAs have used Pearson correlation and lasso (13,14). In addition to a qualitative comparison summarized in Supplementary Table S10, we performed a comprehensive quantitative comparison between these methods and the global test, illustrated with the prostate cancer data set (see section ‘Prioritization of microRNAs and their targets: quantitative comparison of global test, correlation and lasso’ of the Supplementary Material). We showed that the global test yields better prioritization of miRNAs by taking all of their targets into account to produce a P-value (Figure 5). In contrast, lasso aims at sparsity, so may ignore target mRNAs with relatively large association with the miRNA under study. Pearson correlation only yields results per pair, so objective prioritization of relevant miRNAs would involve stricter multiple testing correction, and thus less power.Figure 5.

Bottom Line: The strongest negatively associated mRNAs as measured by the test were prioritized.We applied our integration method to a well-defined muscle differentiation model.Using the same study, we showed the advantages of the global test over Pearson correlation and lasso.

View Article: PubMed Central - PubMed

Affiliation: Center for Human and Clinical Genetics and Leiden University Medical Center, Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, 2300 ZC Leiden, The Netherlands, Netherlands Bioinformatics Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands, Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands and Department of Pediatric Oncology, Erasmus Medical Center - Sophia Children's Hospital, Dr. Molewaterplein 60, 3015 GJ Rotterdam, The Netherlands.

ABSTRACT
Current microRNA target predictions are based on sequence information and empirically derived rules but do not make use of the expression of microRNAs and their targets. This study aimed to improve microRNA target predictions in a given biological context, using in silico predictions, microRNA and mRNA expression. We used target prediction tools to produce lists of predicted targets and used a gene set test designed to detect consistent effects of microRNAs on the joint expression of multiple targets. In a single test, association between microRNA expression and target gene set expression as well as the contribution of the individual target genes on the association are determined. The strongest negatively associated mRNAs as measured by the test were prioritized. We applied our integration method to a well-defined muscle differentiation model. Validation of our predictions in C2C12 cells confirmed predicted targets of known as well as novel muscle-related microRNAs. We further studied associations between microRNA-mRNA pairs in human prostate cancer, finding some pairs that have been recently experimentally validated by others. Using the same study, we showed the advantages of the global test over Pearson correlation and lasso. We conclude that our integrated approach successfully identifies regulated microRNAs and their targets.

Show MeSH
Related in: MedlinePlus