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Meta-Analysis
. 2017 Jun 26;12(6):e0179543.
doi: 10.1371/journal.pone.0179543. eCollection 2017.

Meta-analysis of miRNA expression profiles for prostate cancer recurrence following radical prostatectomy

Elnaz Pashaei  1 , Elham Pashaei  1 , Maryam Ahmady  2 , Mustafa Ozen  3 , Nizamettin Aydin  1
Affiliations
Meta-Analysis

Meta-analysis of miRNA expression profiles for prostate cancer recurrence following radical prostatectomy

Elnaz Pashaei et al. PLoS One. .

Abstract

Background: Prostate cancer (PCa) is a leading reason of death in men and the most diagnosed malignancies in the western countries at the present time. After radical prostatectomy (RP), nearly 30% of men develop clinical recurrence with high serum prostate-specific antigen levels. An important challenge in PCa research is to identify effective predictors of tumor recurrence. The molecular alterations in microRNAs are associated with PCa initiation and progression. Several miRNA microarray studies have been conducted in recurrence PCa, but the results vary among different studies.

Methods: We conducted a meta-analysis of 6 available miRNA expression datasets to identify a panel of co-deregulated miRNA genes and overlapping biological processes. The meta-analysis was performed using the 'MetaDE' package, based on combined P-value approaches (adaptive weight and Fisher's methods), in R version 3.3.1.

Results: Meta-analysis of six miRNA datasets revealed miR-125A, miR-199A-3P, miR-28-5P, miR-301B, miR-324-5P, miR-361-5P, miR-363*, miR-449A, miR-484, miR-498, miR-579, miR-637, miR-720, miR-874 and miR-98 are commonly upregulated miRNA genes, while miR-1, miR-133A, miR-133B, miR-137, miR-221, miR-340, miR-370, miR-449B, miR-489, miR-492, miR-496, miR-541, miR-572, miR-583, miR-606, miR-624, miR-636, miR-639, miR-661, miR-760, miR-890, and miR-939 are commonly downregulated miRNA genes in recurrent PCa samples in comparison to non-recurrent PCa samples. The network-based analysis showed that some of these miRNAs have an established prognostic significance in other cancers and can be actively involved in tumor growth. Gene ontology enrichment revealed many target genes of co-deregulated miRNAs are involved in "regulation of epithelial cell proliferation" and "tissue morphogenesis". Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that these miRNAs regulate cancer pathways. The PPI hub proteins analysis identified CTNNB1 as the most highly ranked hub protein. Besides, common pathway analysis showed that TCF3, MAX, MYC, CYP26A1, and SREBF1 significantly interact with those DE miRNA genes. The identified genes have been known as tumor suppressors and biomarkers which are closely related to several cancer types, such as colorectal cancer, breast cancer, PCa, gastric, and hepatocellular carcinomas. Additionally, it was shown that the combination of DE miRNAs can assist in the more specific detection of the PCa and prediction of biochemical recurrence (BCR).

Conclusion: We found that the identified miRNAs through meta-analysis are candidate predictive markers for recurrent PCa after radical prostatectomy.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. P-value (or FDR) vs number of detected miRNAs for individual analysis as well as meta-analysis.
In each individual dataset, moderated-t statistics was used to generate p-values while adaptive weight and Fisher's methods were utilized to combine these p-values for meta-analysis. This figure is generated using the “MetaDE” package in R.
Fig 2
Fig 2. The heat map of the actual expression profiles for the 15 up- and 22 downregulated DE microRNAs obtained from the meta-analysis.
The heat map is generated using the “MetaDE” package in R. The expression profiles greater than the mean are colored in red and those below the mean are colored in green. 0: Non-recurrence; 1: Recurrence.
Fig 3
Fig 3. Network interrelation of DE microRNAs identified in the meta-analysis.
Orange squares show TF. The circles show the targets of DE microRNAs. Green and red lozenges show up regulated and down regulated microRNAs in various types of diseases. The network was generated using a MIROB web tool to explore DE microRNAs relationships and collective functions.
Fig 4
Fig 4. The most significant enriched KEGG pathway for the DE microRNAs identified from meta-analysis.
The microRNAs in the red box indicates co-deregulated microRNA genes in our list. The DE microRNAs identified from meta-analysis were mapped to “microRNAs in cancer” pathway (KEGG-ID: hsa05206) by using the KEGG mapper web tool.
Fig 5
Fig 5. Common pathway analysis for DE microRNAs identified from meta-analysis.
This analysis revealed that TCF3, MYC, MAX, CYP26A1 and SREBF1 are significantly interacting with candidate miRNA genes.
Fig 6
Fig 6. Receiver operating characteristics (ROC) analysis of 37-miRNA signature in biochemical disease recurrence vs. the non-recurrence samples using each GEO datasets.
The DE miRNAs are depicted in Table 2. AUC; area under the ROC curve.
Fig 7
Fig 7. ROC analysis of the best subset of the DE miRNAs in biochemical disease recurrence vs. the non-recurrence samples using each GEO datasets.
The best subset of DE miRNAs is shown in the first column of Table 3 which has been found by using soft computing technique (PSO/ logistic regression).
Fig 8
Fig 8. A comparison between expression of co-deregulated microRNAs in recurrent vs. non-recurrent PCa samples.
Those miRNAs that were selected for analysis are depicted above the box plots (Table 3). Lines within the boxes indicate median values; whiskers—min and max for miRNA values. BCR+/ -, biochemical disease recurrence status (positive, negative).
See this image and copyright information in PMC

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The authors received no specific funding for this work.