The applications of systems biology to understand complex disease driven by the fact that complex diseases like cancer are attributed to dysregulation of multiple components of the cellular system [24
]. Prostate cancer is the most widely spread cancer in male in western countries. One of the challenging in studying prostate cancer is the heterogeneity of the system. Several genes are attributed to initiate and develop prostate cancer, in addition to role of miRNAs in initiating and progressing prostate cancer [4
]. Several miRNAs profiling studies have been conducted to identify miRNAs that are differentially expressed in tumor versus normal tissues [10
]. Identifying prognostic miRNAs that can help to predict patient outcome or the stage of disease is another important aspect to understand diseases progression. Identifying miRNA-mRNA function modules is another important task in miRNA genetics. One of the least studied factors affect the functionality of miRNAs is competing for target. Recent study showed that targets that compete for miRNAs pose a regulatory effect on each other by limiting the availability of miRNA [16
]. Using this notion, Sumazin et al. [17
] generated a miRNA-mediated network among RNA molecules. Here it is worth mentioning that miRNAs mediate all RNA molecules that harbour a binding site for the miRNA. This study motivated us to analyze the systematic function of miRNAs in prostate cancer by analyzing the influence of each miRNA on the other miRNAs through the target. miRNAs that share MRE of several targets and their expression conditionally dependent on the target are anticipated to regulate each other.
In this work I analyze the functional role of miRNAs in prostate cancer by integrating expression data of targets and miRNAs and miRNA-target networks. Several studies that integrated expression data with miRNA-target networks lead to identifying miRNA-target modules that may play a role in prostate cancer [14
]. However, in this work I integrated expression data using conditional mutual information to assess the conditional dependence between pairs of miRNA and their common target(s). miRNAs modulate each other through their common targets that affect miRNA availability. The association between miRNAs depends on the number of common targets and the significance of the conditional dependence on the target.
One of the challenges I faced in this study is constructing the miRNA-miRNA interaction network using all possible targets as mediators that is computationally very expensive. To reduce computational cost, I started with the miRNA-target network and we only computed conditional dependency between one miRNA and the rest of miRNAs given the expression of the targets. I used both experimentally verified and computationally predicted miRNA-target interactions to identify the miRNA-miRNA networks. Both networks showed that miRNA-1 is a hub miRNA in both networks. This indicates that it has regulatory effect over other miRNAs through its targets. Based on the two networks (Figures and ), 11 miRNAs were identified as hub miRNAs and further analyzed their function and prognostic role. Analyzing their function showed that they play a role in several biological processes including cell proimmigration, cell death, and metabolic biosynthesis (). Analyzing their prognostic role revealed that the 11 miRNAs act as diagnostic and prognostic biomarkers. The low expression of the 11 miRNAs showed to be associated with cancer recurrence (). Several miRNAs among the 11 miRNas are already in clinical trials (miR-16, miR-222, miR-221) [3
]. Here it is worth mentioning that the 11 hub miRNAs are not the top differentially downregulated miRNAs but they are powerful diagnostic biomarkers.
The results in this work caught the attention to the significance of miR-1. Therefore, I further investigated its role in prostate cancer and argue that it is the guardian of the miRNA-mediated gene expression control. microRNA-1(miR-1) is reported to be one of the most consistently downregulated microRNAs in human prostate tumors [25
]. Recent study showed that miR-1 is further reduced in distant metastasis tumors and is a candidate predictor of disease recurrence. miR-1 is encoded by the miR-1-133 cluster which has two copies (at 18q11 and 20q13) in the human genome producing identical mature miR sequences for miR-1 and miR-133. It was recently reported that miR-1, miR-133, and miR-206, which is a functional homolog of miR-1, are among the most frequently downregulated miRs in solid human cancers. Recent study reexpressed miR-1 in human prostate cancer cell lines and their results revealed that miR-1 is a novel candidate marker for disease recurrence in prostate cancer and exhibits a tumor suppressor activity that affects multiple pathways, leading to higher order chromosomal and epigenetic alterations globally similar to those of histone deacetylase inhibitors. Our results found that miRNA-1 targets 240 genes from ExpNet and 527 in PredNet. Both lists showed that they are enriched with phosphoproteins (5.3 × e−6
) and acetylation proteins (3.7 × e−7
). 3′UTR-mediated miRNA interactions show consistent results that miRNA-1 is a hub miRNA using different miRNA-target interactions with different cutoff values (Figures and ). I found that miRNA-1 is hub in primary prostate cancer network and a hub in the other miRNA-miRNA networks revealed that miRNA-1 is a key regulator of genes and a master coordinator of other miRNAs. Epigenetic analysis showed that promoter hypermethylation may be the reason behind the reduced expression of miRNA1-133 cluster including miRNA-1 [25
Next I asked if the miRNA interactions are biased toward other factors that may influence the association among miRNAs. First, correlation between miRNAs was shown to influence the interactions among the 11 miRNAs. I found that the 11 miRNAs are correlated but they are not correlated with other miRNAs, which indicates that these miRNAs have something common between them and not with other miRNAs. The second factor is the number of common targets that might influence the network. So I calculated the association between miRNAs based on the number of common targets between them and found that the 11 miRNAs are not significantly connected and they are not among the hub genes. This indicates that the number of common targets between miRNAs did not influence the interactions between miRNAs.
Lastly, the expression of primary prostate samples was used to identify the miRNA-miRNA interactions based on expression of primary cancer samples alone. Interestingly, I found that miR-1 is still the most connected miRNA and other miRNAs (miR-155, miR-16) are hubs. This indicates that these miRNAs play a significant role in cancer initiation and not metastasis.