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.