Conclusion and future prospects
Mutations in BRAF monensin manufacturer have been important in understanding the roles that BRAF and other members of the RAS/RAF cascade play in disease progression, and detail mechanistic study can help to approach effective BRAF inhibitors for cancer diseases. However, growing concerns over drug resistance to molecular targeted therapies (Chakraborty et al., 2013), including BRAFand MEK inhibitors, have stimulated investigators to uncover additional molecular targets for the treatment of cancers as well as for RASopathies, in the hope of providing personalised medicine. The goal of personalised medicine is to explore different techniques associated with cancer therapy and the management of various malignancies in RASopathies. Although the regulation and inhibition of the RAS/MAPK pathway have been widely studied in cancer research, the natural history and predisposition of individuals with NS, CFC and LEOPARD syndromes, characterised as a result of disruption of the RAS/MAPK cascade, have not been understood. Additionally, the molecular phenomena of the RAS/MAPK cascade underlying overlapping features and varied degrees of penetrance in NS, CFC and LEOPARD (RAS/MAPK) syndromes are largely unknown.
In a mouse model, inhibition of mTOR, which regulates mRNA translation and ribosome synthesis, intriguingly indicated the reverse heart defects for NS multiple lentigines. Additionally, HMG-CoA reductase inhibitors and MEK inhibitors ameliorated the phenotype of the NS and NF-1 mouse models (mutations in SOS1 and RAF1) (Gelb and Tartaglia, 2011; Bauer and Stratakis, 2005; Wu et al., 2011). Such findings suggest that the complete manipulation of RAS/MAPK activity can help to correct the aberrant activation of the RAS-RAF signalling that is responsible for variable clinical phenotypes in the form of RAS–MAPK syndromes, and associated cancer risks.
MicroRNAs (miRNAs) are a class of endogenous, evolutionary conserved, single strand non-coding RNAs with approximately 22 nucleotides (nts), which involved in the regulation of gene expression by translational repression and mRNA destabilization (Ambros, 2004; Ambros and Chen, 2007; Kloosterman and Plasterk, 2006). Mature miRNAs are generated from the stem portion of single stranded stem-loop precursors (pre-miRNAs), which is processed by ribonuclease III-like enzyme from primary miRNA (pri-miRNA) transcript. Pre-miRNAs are exported into the cytoplasm where cleavage of the loop by the RNase Dicer generates a duplex of two about 22 nt long mature miRNA (miRNA and miRNA-star) duplex. And then mature miRNAs are incorporated into the RNA-induced silencing complex (RISC) and guide RISC to complementary miRNA targets. Finally, the RISC inhibits translation elongation or triggers the degradation of target mRNAs (Bartel, 2005; Kim et al., 2009; Liu et al., 2008; Mallanna and Rizzino, 2010). Due to miRNAs playing various regulatory roles in gene regulation, several studies have indicated that they take part in a wide variety of biological processes including organ development, cell proliferation and death, apoptosis and fat metabolism, cell differentiation, signal transduction, fat metabolism and adaptive immune responses as well as diseases (Bartel, 2004; Belver et al., 2010; Ladomery et al., 2011; Rogers and Chen, 2013; Sun and Lai, 2013).
Most of the known miRNAs are highly evolutionarily conserved from species to species, ranging from insects to humans in animal kingdom (Daido et al., 2014; Maher et al., 2006; Niwa and Slack, 2007; Takane et al., 2010; Tanzer and Stadler, 2004). Conservation among species became one of the most important properties of miRNAs. So, this feature will facilitate us to perform the computational search for miRNAs based on the highly conserved sequence in the mature miRNAs and long hairpin structures in miRNA precursors (Mishra and Lobiyal, 2011; Ren et al., 2012; Saetrom et al., 2006). There are several significant advantages of identifying miRNAs, because it is accurate, fast, and inexpensive compared to the experimental method. For this reason, computational approaches provide an ideal way for identifying miRNAs in animals by using expressed sequence tags (EST) and genome survey sequence (GSS) databases, especially in organisms in which genome sequences are not available. Using this method, a large number of miRNAs have been successfully identified in some plant and animal species (Akter et al., 2014; Barozai, 2012b; Dong et al., 2012; Luo and Zhang, 2009; Paul and Chakraborty, 2013; van der Burgt et al., 2009; Yousef et al., 2009).
Conclusion and future prospects