RNA genome Database of Multi-stage Liver disease
NEORNAT Inc. used classified tissue into 9 stages of Korean liver disease patients for healthy normal, hepatitis with low and high-grade fibrosis, cirrhosis, low and high-grade dysplastic nodule, and hepatocellular carcinoma (Edmonson Grade 1,2,3), and we search and research disease markers and new hepatocarcinogenesis mechanisms using established the Korean liver disease RNA genome database using Next-generation RNA sequencing.
|Hardware||4 high-performance big data analysis servers based on Linux|
|Software||Established a core pipeline for RNome analysis, and have data analysis tools such as BGW (Biomedical Genomics Workbench)|
|Open database||Genomic big data analysis of TCGA, ICGC, NCBI Gene Expression Omnibus, etc., RNA information database (cBioportal, COSMIC), miRNA target prediction database (TargetScan, miRWALK, miRGator, 4miRs), untranslated RNA function prediction database (starBase, InCeDB), miRcode, spongeScan)|
RNA-based & RNA-targeted Therapeutics
RNA affects the stability and expression of genes through gene editing and modification in vivo.
In particularly, microRNAs (miRNAs) or small interfering RNAs (siRNAs) are directly involved in gene expression. Therefore, if miRNA- or siRNA-affected genes are related to immune activity, they will affect the regulation of immune activity. Also, miRNAs and siRNAs play a key role in the carcinogenesis process when miRNAs or siRNAs affect oncogenes.
Based on these diverse and important RNA functions, we are researching and developing RNA-related anticancer drugs that contribute to cancer development by comprehensively understanding the changes in the RNA network in an integrated way. In addition, various structural modifications and drug delivery techniques are applied to enhance the stability and tissue selectivity of RNA materials. With the above efforts, pipelines such as NRT-YHD, NRT-KSY, and NRT-NMJ are under development.
In vivo validation system
NEORNAT Inc. is evaluating the pipeline while maintaining and managing animal models for the evaluation of anticancer drug efficacy.
1. Liver cancer model
We manage and maintains a liver cancer animal model in which a well-known oncogene called Ras is specifically overexpressed in the liver and spontaneously develops liver cancer. Liver cancer begins in adulthood (about 15 weeks of age) in male mice, and most individuals develop liver cancer. Because the mechanism is very similar to the occurrence of liver cancer in humans, Neona's liver cancer treatment development pipeline uses this model to evaluate the liver cancer treatment efficacy. (Journal of Hepatology 43 (2005) 836-844)
2. Lung cancer model
The established the lung cancer animal model induces lung cancer in the mouse lung by injecting a human lung cancer cell line to athymic nude mice into the tail vein. In contrast with the xenograft cancer animal model, it is possible to directly develop cancer in the mouse lung to evaluate the therapeutic effect and tissue delivery system ability of lung cancer.
3. Pancreatic cancer model
The pancreatic cancer animal model uses a xenograft animal model of subcutaneous transplantation of human pancreatic cancer cell lines into athymic nude mice.