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The count of the nucleotides in a cloned, short genomic sequence has become an important criterion to annotate such a sequence as a miRNA molecule. While the majority of human mature miRNA sequences consist of 22 nucleotides, there exists discrepancy in the characteristic lengths of the miRNA sequences. There is also a lack of systematic studies on such length distribution and on the biological factors that are related to or may affect this length. In this paper, we intend to fill this gap by investigating the sequence structure of human miRNA molecules using statistics tools. We demonstrate that the traditional discrete probability distributions do not model the length distribution of the human mature miRNAs well, and we obtain the statistical distribution model with a decent fit. We observe that the four nucleotide bases in a miRNA sequence are not randomly distributed, implying that possible structural patterns such as dinucleotide (trinucleotide or higher order) may exist. Furthermore, we study the relationships of this length distribution to multiple important factors such as evolutionary conservation, tumorigenesis, the length of precursor loop structures, and the number of predicted targets. The association between the miRNA sequence length and the distributions of target site counts in corresponding predicted genes is also presented. This study results in several novel findings worthy of further investigation that include: (1) rapid evolution introduces variation to the miRNA sequence length distribution; (2) miRNAs with extreme sequence lengths are unlikely to be cancer-related; and (3) the miRNA sequence length is positively correlated to the precursor length and the number of predicted target genes. [ABSTRACT FROM AUTHOR]

Recent studies have shown the contribution of miRNAs to cancer pathogenesis. Prostate cancer is the most commonly diagnosed cancer in men. Unlike other major types of cancer, no single gene has been identified as being mutated in the majority of prostate tumors. This implies that the expression profiling of genes, including the non-coding miRNAs, may substantially vary across individual cases of this cancer. The within-class variability makes it possible to reconstruct or infer disease-specific miRNA-mRNA correlation and regulatory modular networks using high-dimensional microarray data of prostate tumor samples. Furthermore, since miRNAs and tumor suppressor genes are usually tissue specific, miRNA-mRNA modules could potentially differ between primary prostate cancer (PPC) and metastatic prostate cancer (MPC). We herein performed an in silico analysis to explore the miRNA-mRNA correlation network modules in the two tumor subtypes. Our analysis identified 5 miRNA-mRNA module pairs (MPs) for PPC and MPC, respectively. Each MP includes one positiveconnection (correlation) module and one negative-connection (correlation) module. The number of miRNAs or mRNAs (genes) in each module varies from 2 to 8 or from 6 to 622. The modules discovered for PPC are more informative than those for MPC in terms of the implicated biological insights. In particular, one negative-connection module in PPC fits well with the popularly recognized miRNA-mediated post-transcriptional regulation theory. That is, the 3'UTR sequences of the involved mRNAs (∼620) are enriched with the target site motifs of the 7 modular miRNAs, has-miR-106b, -191, -19b, -92a, - 92b, -93, and -141. About 330 GO terms and KEGG pathways, including TGF-beta signaling pathway that maintains tissue homeostasis and plays a crucial role in the suppression of the proliferation of cancer cells, are over-represented (adj.p<0.05) in the modular gene list. These computationally identified modules provide remarkable biological evidence for the interference of miRNAs in the development of prostate cancers and warrant additional follow-up in independent laboratory studies. [ABSTRACT FROM AUTHOR]