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Open AccessArticle

In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer

1
Institute of Molecular Bioimaging and Physiology, National Research Council, 20090 Segrate (Mi), Italy
2
Interuniversity Institute of Bioinformatics in Brussels (IB)2, 1050 Brussels, Belgium
3
Machine Learning Group (MLG), Department d'Informatique, Universite libre de Bruxelles (ULB), 1050 Brussels, Belgium
4
Department of Informatics, Systems and Communication, University of Milan-Bicocca, 20125 Milan, Italy
5
SYSBIO Centre of Systems Biology (SYSBIO), 20126 Milan, Italy
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(3), 910; https://doi.org/10.3390/ijms19030910
Received: 28 February 2018 / Revised: 15 March 2018 / Accepted: 16 March 2018 / Published: 19 March 2018
(This article belongs to the Special Issue The Role of MicroRNAs in Human Diseases)
Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development. Despite efforts to profile PC, the alterations (gene, CNA, and miRNA) and biological processes that correlate with disease development and progression remain partially elusive. Many gene signatures proposed as diagnostic or prognostic tools in cancer poorly overlap. The identification of co-expressed genes, that are functionally related, can identify a core network of genes associated with PC with a better reproducibility. By combining different approaches, including the integration of mRNA expression profiles, CNAs, and miRNA expression levels, we identified a gene signature of four genes overlapping with other published gene signatures and able to distinguish, in silico, high Gleason-scored PC from normal human tissue, which was further enriched to 19 genes by gene co-expression analysis. From the analysis of miRNAs possibly regulating this network, we found that hsa-miR-153 was highly connected to the genes in the network. Our results identify a four-gene signature with diagnostic and prognostic value in PC and suggest an interesting gene network that could play a key regulatory role in PC development and progression. Furthermore, hsa-miR-153, controlling this network, could be a potential biomarker for theranostics in high Gleason-scored PC. View Full-Text
Keywords: prostate cancer; microRNA/miRNA; copy number alterations; co-expressed genes prostate cancer; microRNA/miRNA; copy number alterations; co-expressed genes
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MDPI and ACS Style

Cava, C.; Bertoli, G.; Colaprico, A.; Bontempi, G.; Mauri, G.; Castiglioni, I. In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer. Int. J. Mol. Sci. 2018, 19, 910.

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