Unravelling the genomic maze: Bioinformatics unleashes insights into Sotos syndrome (Cerebral Gigantism)

Abstract

The overgrowth condition known as Sotos syndrome is distinguished by its characteristic facial gestalt, macrocephaly, excessive development during childhood, varying degrees of learning problems, and a variety of other abnormalities. Due to abnormally high height, occipitofrontal circumference (OFC), advanced bone age, neonatal problems such as hypotonia and feeding issues, and facial gestalt, the diagnosis is typically recognized after birth. The current work aims to identify potential therapeutic treatments through bioinformatics analysis, focusing on key genes and pathways implicated in the disease. Text mining techniques were employed to identify 41 genes associated with Sotos syndrome, 37 of which were enriched with Gene Ontology (GO) terms and 24 with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Using protein-protein interaction (PPI) network analysis, two gene modules were extracted using the Molecular Complex Detection (MCODE) algorithm, highlighting 15 hub genes as central candidates. Furthermore, leveraging drug-gene interaction databases and network pharmacology tools, 23 FDA-approved drugs were identified that target 11 of these core hub genes, suggesting potential therapeutic avenues for Sotos syndrome. Only bioinformatics tools were used in this study further in-vitro and in-vivo studies are required because phenotypic differences will vary from person to person depending on the expressivity of the gene. In future this approach may help to collaborate with clinical researchers to integrate bioinformatics findings with real-world clinical data. This will enhance understanding of clinical relevance of the identified genes and pathways and validate bioinformatics predictions with patient-derived samples and clinical histories.

Keywords

• Sotos syndrome
• Drug discovery
• Text mining
• Hub genes
• String
• Cytoscape