Digital Diagnostics (Jun 2023)
Analysis of retinoblastoma-associated genes using bioinformatic methods
Abstract
BACKGROUND: Retinoblastoma is a common neoplasia that affects the visual organ in young children. The mortality rate is approximately 15%. In 91% of cases, surgery with enucleation is required, which significantly reduces the patients quality of life. Early diagnosis of the disease may help to correct approaches to treatment of retinoblastoma, significantly increasing the chances of preserving vision. This is important since approximately 95% of retinoblastoma cases are diagnosed before the age of 5. Using bioinformatic methods, a comprehensive analysis of the patterns and connections between the retinoblastoma-associated genes was conducted, which may further form the basis of molecular genetic testing for diagnosing this oncology. AIM: The study aimed to comprehensively analyze the genes and their products associated with retinoblastoma to reveal patterns of oncologic development. METHODS: Obtaining and sorting the list of genes using the OMIM and COSMIC databases (https://omim.org/; https://cancer.sanger.ac.uk/cosmic). Calculating gene ontology categories using DAVID and PANTHER services (https://david.ncifcrf.gov/; http://pantherdb.org/). Reconstructing the gene network using the GeneMANIA service (https://genemania.org/). Analyzing the three-dimensional (3D) structure of proteins using the PDB (RCSB) database (https://www.rcsb.org/). RESULTS: After sorting retinoblastoma-associated genes, the OMIM.org database generated a list of 139 elements. After sorting and comparison with the results of a similar query in the COSMIC database, RB1, KRAS, SYK, MYCN, and BCOR retinoblastoma-associated key genes were identified. The resulting list was analyzed for gene ontology categories using DAVID and PATHER services. The most significant categories for retinoblastoma genes were cell cycle regulators, in particular regulators of the transition from G to S phase and regulators of transcription from the RNA polymerase II promoter. Gene network structure analysis for retinoblastoma genes using the GeneMANIA service showed the existence of dense and linked gene clusters with cell cycle and transcriptional regulator genes at the center. Using the PDB database, 3D structures of key gene expression products were obtained. CONCLUSIONS: The development of molecular genetic testing of retinoblastoma for the activity of expression of associated genes and their products in the prenatal and/or postnatal period is required to improve the retinoblastoma monitoring system. The results of the study may serve as input data for this testing.
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