Coordinate MicroRNA-Mediated Regulation of Protein Complexes in Prostate Cancer

<div><p>MicroRNAs are a class of small non-coding regulatory RNA molecules that regulate mRNAs post-transcriptionally. Recent evidence has shown that miRNAs target entire functionally related proteins such as protein complexes and biological pathways. However, characterizing the influence of miRNAs on genes whose encoded proteins are part of protein complexes has not been studied in the context of disease. We propose an entropy-based framework to identify miRNA-mediated dysregulation of functionally related proteins during prostate cancer progression. The proposed framework uses experimentally verified miRNA-target interactions, functionally related proteins and expression data to identify miRNA-influenced protein complexes in prostate cancer, and identify genes that are dysregulated as a result. The framework constructs correlation matrixes between functionally related proteins and miRNAs that have targets in the complex, and assesses the changes in the Shannon entropy of the modules across different stages of prostate cancer. Results reveal that SMAD4 and HDAC containing protein complexes are highly affected and disrupted by miRNAs, particularly miRNA-1 and miRNA-16. Using biological pathways to define functionally related proteins reveals that NF-kB-, RAS-, and Syndecan-mediated pathways are dysregulated due to miRNA-1- and miRNA-16-mediated regulation. These results suggest that miRNA-1 and miRNA-16 are important master regulators of miRNA-mediated regulation in prostate cancer. Moreover, results reveal that miRNAs with high-influence on the disrupted protein complexes are diagnostic and prognostic biomarker candidates for prostate cancer progression. The observation of miRNA-mediated protein complex regulation and miRNA-mediated pathway regulation, with partial experimental verification from previous studies, demonstrates that our framework is a promising approach for the identification of novel miRNAs and protein complexes related to disease progression.</p></div

Pathway Enrichment Map of dyregulated protein complexes.

<p>Pathway Enrichment Map of dyregulated protein complexes. We extracted the protein members of the dysregulated protein complexes and found enriched pathways using DAVID online tool.To visualize enrichment map of pathways, we used Enrichment Map Cytoscape plugin <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084261#pone.0084261-Merico1" target="_blank">[47]</a> to visualize enriched pathways. Nodes in this figure represents enriched pathways, links between nodes represent the fraction of overlap between them. The darker the node the more enriched the pathway is, and the thicker the link, the more significant the overlap is.</p

SVM classification with 10-fold cross validation to classify samples into normal, primary or metastasis using protein or miRNA expression profiles.

<p>SVM classification with 10-fold cross validation to classify samples into normal, primary or metastasis using protein or miRNA expression profiles.</p

Heatmap of the 23 protein and 21 miRNA from Taylor data.

<p>We extracted the expression of 21 miRNAs and 23 proteins from Taylor data and then used hierarchical clustering to cluster samples. Results show that Mets samples are fully segregated from the normal and PCa samples. Normal samples tend to be grouped together and have a distinct expression profile.</p

Table 2. Canonical pathways influenced by miRNAs in prostate cancer.

<p>Table 2. Canonical pathways influenced by miRNAs in prostate cancer.</p

Network of dysregulated complexes and influenced proteins.

<p>Dysregulated protein complexes have a downstream effect on other proteins that affect their associations with miRNAs. We identified miRNA-target interactions influenced by the dysregulated protein complexes and then built a network between dysregulated complexes and proteins. Green diamonds represent the components of the dysregulated complexes. Circles represent proteins whose miRNA interactions are influenced by the dysregulated protein. An edge between a circle and a diamond proteins means that the dysregulation of diamond protein have a downstream effect on the ability of miRNAs to target circle proteins. Yellow circle proteins are proteins that are influenced by more than one protein complex.</p

Kaplan-Meier plots for the 23 proteins from the Taylor and Swedish data.

<p>A. Samples were grouped into two groups based on the expression of the 23 proteins from the Taylor mRNA data and then logrank test was applied to assess separation significance (p = 0.005). B. Samples from the Swedish prostate cohort were grouped into three groups using the expression of the 23 proteins. The resulted three groups are significantly separated which shows the prognostic power of the 23 proteins (low risk vs. high risk (p = 0.008), low risk vs. intermediate risk (p = 0.16), high risk vs. intermediate risk ( p = 0.02)). C. Samples from the Swedish breast cohort were grouped into two groups based on the expression of the 23 proteins. The two groups have distinct death specific association (p = 0.004). D.Samples from the Swedish breast cohort were grouped into two groups based on the expression of the 23 proteins. The two groups have distinct cancer recurrence profile (p = 0.008).</p

Prognostic proteins and prognostic miRNAs that were extracted from the 84 and 85 protein and miRNA lists respectively based on univariate regression analysis.

<p>Prognostic proteins and prognostic miRNAs that were extracted from the 84 and 85 protein and miRNA lists respectively based on univariate regression analysis.</p

Significant miRNA-coordinated protein complexes using experimentally validated miRNA-target interactions set .

<p>The table showed the corrected p-value, the size of the complex and the number of miRNAs targeting the complex.</p

Correlation between protein complex size and the significance of the SVD based entropy.

<p>Using the experimental miRNA-target interaction to assess the significance of miRNA-mediated dysregulation of protein complexes, we analyzed the relationship between the complex size and the p.value generated by our framework.We found that complexes f size 2, 3 and 7 have the most significant pvalue, and complexes of size less than 10 are not very significant.</p