Iarema P.O.1, Turkina V.A.1, Mayorova A.A.1, Orlov Y.L.1*
1I.M. Sechenov First Moscow State Medical University (Sechenov University)
y.orlov [at] sechenov.ru
Abstract
Computer analysis of disease susceptibility genes using online bioinformatics tools and open databases allows the identification of potential target genes for therapy. In the course of this study we reconstructed the gene network for genes associated with glioma. The relevance of the work is due to the fact that gliomas are the most common primary brain tumors. Gliomas originate from glial cells that support and protect nerve cells in the brain and spinal cord. Despite surgical removal, gliomas are still prone to recurrence because they grow rapidly in the brain, are resistant to chemotherapy, and are very aggressive (Byun Y.H. et al, 2022).
The task was to collect a list of glioma genes, analyze gene ontologies, reconstruct the gene network, and analyze the spatial structures of the associated proteins.
The following online bioinformatics tools were used: STRING-DB (https://string-db.org/) for gene network construction, MalaCards (https://www.malacards.org/), OMIM database (https://omim.org/). The search was performed using the keyword “glioma”. AlphaFold (https://alphafold.ebi.ac.uk/), PDB (https://www.rcsb.org/) resources were used to model and visualize 3D protein structures. PANTER (http://www.pantherdb.org/) and DAVID (https://david.ncifcrf.gov/summary.jsp) resources were used to analyze gene ontologies. The list of genes for analysis consisted of 176 genes.
The most significant categories for glioma genes according to DAVID are: binding of identical proteins, negative regulation of biological processes, regulation of programmed cell death, regulation of cell death, and cell population proliferation.
The gene network was reconstructed using the STRING-DB resource (https://string-db.org/). MicroRNA genes were not recognized by the program. The graph included 150 genes. The study of the gene network structure showed high connectivity of genes within certain clusters. The EGFR and TP53 genes, which are known and well-studied oncogenes, had the greatest number of connections, as well as STAT3, KRAS, PIK3CA, IDH1, KDR. Construction of the glioma gene network showed that some elements of the graph are sufficiently linked, while others are only partially linked so that the search for target proteins for glioma treatment can be facilitated.
Three-dimensional structures of KRAS and PIK3CA proteins were constructed using AlphaFold software (https://alphafold.ebi.ac.uk/). PAE viewer (http://www.subtiwiki.uni-goettingen.de/v4/paeViewerDemo) was used to check the validity of the predicted protein structure. The structure of KRAS protein was found to be similar to that of 7ROV protein obtained from PDB (https://www.rcsb.org/) and the structure of PIK3CA protein was found to be similar to that of 4YKN protein.
