Essay Sample: Circ-RNA for Diagnostic Cancer

From recent studies, circRNAs have become a crucial diagnostic maker for diseases including cancer. The expression of the clinical and profile significance in the hepatoblastoma of the cricRNAs is still unknown. From the already known 869 cricRNAs in hepatoblastoma samples, only ciric_0015756 is mainly up-regulated in the metastatic hepatoblastoma cells and hepatoblastoma specimens (Cao et al., 2017). The primary role of ciric_0015756 is to regulate hepatoblastoma cell function by acting as a 1250-3p sponge (Liu et al., 2018). Moreover, circRNAs is a bay product of RNA splicing without functional significance. circRNAs are usually generated from the intronic and exonic sequences of the host gene.

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Therefore, from such identification, it is possible to predict the function of circRNAs through the functional analysis if the parental genes. Moreover, the GO enrichment analysis of the circ-RNAs indicates that producing these genes differently expressed circRNAs that are enriched in the cytoplasm, chromosome organization, and the organic cyclic compound binding. The circRNAs play their function in the cytoplasm (Liu et al., 2018). On the other, hand chromosome dysfunction is tightly associated with gene expression dysfunction. Similarly, the cyclic compound binding of the circRNAs is, on the other side, associated with the signaling pathway inactivation and activation.

Importantly, the tight junction in the signaling pathway is mainly ranked as the to a signaling pathway that is affected by the circ-RNAs mediated regulatory network. Moreover, the tight junction signaling has mostly been implicated in the pathogenesis of several cancers because the pathogenesis of cancer is associated with signaling pathway dysfunction and abnormal gene expression (Liu et al., 2018). Then it is not a surprise that circRNAs would be associated with or be involved with the pathogenesis of hepatoblastoma. Additionally, circRNAs plays a crucial role in the fine-tuning of miRNA-mediated gene expression through sequestering miRNA. For instance, ciRS-7 has several miRNA-7 binding sites, therefore, acting as an endogenous miRNA sponge (Cao et al., 2017).

Consequently, circ-ITCH acts as a miRNA sponge and plays an inhibitory role in esophageal squamous cell carcinoma. On the other hand, the curcHIPK3 acts as the sponge to 9 miRNA that mainly has 18 potential binding sites. Primarily, circRNAs act as miRNA sponges in order to regulate the expression in the targeted genes of miRNAs. Therefore, it is no surprise that the network of circRNA-miRNA-mRNA is involved in the development and progression of hepatoblastoma. Mostly, circ_0015756 silencing decreases hepatoblastoma cell invasion, viability, and proliferation (Cao et al., 2017). Moreover, circRNA is believed to negatively contribute substantially to competing endogenous RNA and negatively regulate miRNAs.

Quantitation by RT-PCR, Sequencing, WB

The analysis if mRNA expression in the non-tumoral and tumoral samples is crucial in the diagnosis and treatment of cancer. Until recently, scientists had been relying on Northern blot for qualification of RNA. Although the discovery of the reverse transcriptase enzyme resulted to the development of the opposite transcription polymerase chain reaction (RT-PCR) technique which has displaced the Northern blot as the method of choice for the RNA qualification and detection (Costa et al., 2013). RT-PCR is mainly used qualitatively to detect the gene expression through quantitatively measure the amplification of cDNA and through the creation of complementary DNA transcripts from RNA (Zou et al., 2016). The process primarily consists of three main steps which are detection and qualification of amplified products that are referred to as amplicons.

Moreover, RT-PCR is also used to quantify mRNA bother absolute and relative terms. This has become one of the most common way or method that is used in gene quantification. This is because it has an extensive dynamic range, can easily be highly sequence-specific, and had high sensitivity (Eissler et al., 2017). Primarily, the scope of the RT-PCR approach makes it applicable across a vast selection of experimental conditions and also allows experimental comparisons between the abnormal and normal tissues.

Even now RT-PCR is considered the best avenues for measuring the number of copies if the specific cDNA targets (Zou et al., 2016). Moreover, the extensive use of this strategy has resulted in the birth and growth of various protocols for the generation of quantitative data using archived, frozen, and fresh FFPE samples, single cells, whole-tissue biopsies, cultured tissue cells, microdissected samples, thermal cyclers, and reporting methods (Costa et al., 2013). Consequently, the application of RT-PCR has indicated that the levels of RNA transcripts predict and stratify patient outcomes in several diseases.

The problem with this method is that is that time is consumed in the lab for the variability of the results and gene analysis. Additionally, comparing expression levels most of the time it is difficult and requires complicated and normalized approaches (Zou et al., 2016). To address these problems scientist have developed new methods that can analyze several genes at the same time. Such technologies include Serial Analysis of Gene (SAGE), cap analysis of gene expression (CAGE), high-density oligo microarrays gene expression, (SAGE), and massively parallel signature sequencing (MPSS) (Costa et al., 2013). Importantly, using these technologies, it is possible to get the mRNA expression data for many genes from one experimentation.

Moreover, these techniques come with their limitations. The main problems are high background due to cross-hybridization, the limit in the detection of RNA splice patterns, and limited dynamic range of detection (Eissler et al., 2017). Primarily, these disadvantages limit the use of these approaches in the daily clinical practice and translation investigation.

Biomedical Network Analysis

The diversity of network is relevant and essential in the field of biomedicine. Primarily, social networks models of human interaction can help in explaining the pathways through which diseases are transmitted (McGillivray et al., 2018). The layers of the neurons in the brain a crucial in the process of sensory data and the architecture of the neuronal networks inspired by the artificial neural networks there are used in the identification of data including biomedical data sets. Several networks of the interacting molecules are the foundation of the human disease and health that form the functional base for the several higher-order of biological networks (Bian et al., 2014). Additionally, networks are an essential and robust framework for understanding the molecular interaction because they are a breadth if network analysis approaches that are developed through diverse disciplines. For instance, the network analysis such as HotNet uses an algorithm to annotate function in molecular networks (Bian et al., 2014). Similarly, machine learning techniques such as DeepBind, on the other hand, uses refined approaches to generate accurate network topology predictions for genomic sequences.

Molecular networks, on the other hand, can function in approaches that are not familiar to human perspective, and therefore it becomes challenging to develop intuitions (Bian et al., 2014). Primarily, the network analysis of the large-scale molecular data has been used in the identification of the critical proteins and pathways in GRNs which include molecular pathways that are affected by cancer (McGillivray et al., 2018). Moreover, the molecular networks evolve and change over time with as strong and surprising dynamic complexity. This has contributed to advances in molecular alteration but which has multiple affected genes in essential pathways.

References

Bian, J., Xie, M., Topaloglu, U., Hudson, T., Eswaran, H., & Hogan, W. (2014). Social network analysis of biomedical research collaboration networks in a CTSA institution. Journal of biomedical informatics, 52, 130-140.

Cao, S., Wei, D., Li, X., Zhou, J., Li, W., Qian, Y., Wang, Z., Li, G., Pan, X., ... Lei, D. (2017). Novel circular RNA expression profiles reflect the progression of patients with hypopharyngeal squamous cell carcinoma. Oncotarget, 8(28), 45367-45379.

Costa, C., Gimenez-Capitan, A., Karachaliou, N., & Rosell, R. (2013). Comprehensive molecular screening: from the RT-PCR to the RNA-seq. Translational lung cancer research, 2(2), 87-91.

Eissler, Y., Pavlov, M. S., Conejeros, P., Espinoza, J. C., & Kuznar, J. (2017). Detection and quantification of Chilean strains of infectious pancreatic necrosis virus by real-time RT-PCR assays using segment B as a target. Submission article platform-Latin American Journal of Aquatic Research, 39(3).

Liu, B. H., Zhang, B. B., Liu, X. Q., Zheng, S., Dong, K. R., & Dong, R. (2018). Expression Profiling Identifies Circular RNA Signature in Hepatoblastoma. Cellular Physiology and Biochemistry, 45(2), 706-719.

McGillivray, P., Clarke, D., Meyerson, W., Zhang, J., Lee, D., Gu, M., ... & Gerstein, M. (2018). Network analysis as a grand unifier in biomedical data science. Annual Review of Biomedical Data Science, 1, 153-180.

Zou, X. H., Chen, W. B., Xiang, Z. H. A. O., Zhu, W. F., Lei, Y. A. N. G., Wang, D. Y., & Shu, Y. L. (2016). Evaluation of a single-reaction method for whole genome sequencing of influenza A virus using next generation sequencing. Biomedical and Environmental Sciences, 29(1), 41-46.