Quantum Language of MicroRNA: Application for New Cancer Therapeutic Targets
MicroRNA (miRNA) is the noncoding gene: therefore, the miRNA gene inheritably controls protein gene expression through transcriptional and post-transcriptional levels. Aberrant expression of miRNA genes causes various human diseases, especially cancers. Although cancer is a complex disease, cancer/miRNA implication has yet been grasped from the perspective of miRNA profile in bed side. Since miRNA is the mobile genetic element, the clinical verification of miRNA in microvesicle of blood is too much straggle to predict potential cancer/miRNA associations without bioinformatical computing. Further, experimental investigation of miRNA/cancer pathways is expensive and time-consuming. While the accumulated data (big data) of miRNA profiles has been on line as the databases in cancers, using the database algorithms for miRNA target prediction have reduced required time for conventional experiments and have cut the cost. Computational prediction of miRNA/target mRNA has shown numerous significant outcomes that are unobtainable only by experimental approaches. However, ID of miRNA in the annotation is an arbitrary number and the ID is not related with miRNA its functions. Therefore, it has not been physicochemically shown why multiple miRNAs in blood or tissues are useful for diagnosis and porgnosis of human diseases or why function of single miRNA in cancer is rendered to oncomir or tumopr suppressor. In addition, it is less cleared why environmental factors, such as temperature, radiation, therapeutic anti-cancer immune or chemical agents can alter the expression of miRNAs in the cell. The ceRNA theory would not be enough for the investigation of such subjects. Given miRNA/target prediction tools, to elucidate such issues with computer simulation we have previously introduced the quantum miRNA/miRNA interaction as a new scoring using big database. The quantum score was implicated in miRNA synergisms in cancer and participated in the miRNA/target interaction on human diseases. On the other hand, ribosomal RNA (rRNA) is the dominant RNA species of the cells. It is well known that ribosomopathies, such as Diamond-Blackfan anemia, dyskeratiosis congenital, Shwachman-Diamond syndrome, 5q-myelodysplastic syndrome, Treacher Collins syndrome, cartilage-hair hypoplasia, North American Indian childhood cirrhosis, isolated congenital asplenia, Bowen-Conradi syndrome and cancer are caused by altered expression of ribosomal proteins or rRNA genes. We have proposed the hypothesis that the interaction among miRNAs from rRNA and/or other cellular miRNAs would be involved into cancer as the ribosomopathy. Subsequently, we found rRNA-derived miRNAs (rmiRNAs) by using the sequence homology search (miPS) with miRNA database (miRBase). Further, the pathway related with cancer between rmiRNA/target protein gene was predicted by miRNA entangling target sorting (METS) algorithm. In this chapter, we describe about the usage of in silico miRNA identification program, miRNA/target prediction search through the database and quantum language of miRNA by the METS, and the ontology analysis. In particular, the METS algorithm according to the quantum value would be useful simulator to discover a new therapeutic target aganist cancer. It may also partly contribute to the elucidation of complex mechanisms and development of agents of anti-cancer.
Key wordsAluRNA Big data Biomarker Cancer microRNA Quantum Ribosomal RNA
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