Comprehensive in silico analysis of genetic landscape and pathways involved in Stickler syndrome

Abstract

Stickler syndrome is a collection of hereditary conditions that impact connective tissue, mainly collagen, and can cause a variety of symptoms, such as joint and bone abnormalities, hearing loss, and visual impairments. Previous studies suggest that mutations in the collagen-encoding genes are a primary cause of SS. These mutations can be inherited from parents to offspring and may vary significantly in terms of severity and symptoms. Besides these mutations, the complex genetic maze underlying SS remains poorly understood, limiting the development of targeted therapeutic and biomarker options. In this study we aimed to identify key genes and molecular pathways potentially involved in SS using bioinformatics approaches, and to explore putative therapeutic directions. In our text mining analysis, we identified 24 distinct genes associated with SS in Homo sapiens, out of which 22 were chosen as candidate genes for enrichment analysis, based on their Gene Ontology (GO) annotations and participation in pertinent biological pathways. Cytoscape-based construction of the protein–protein interaction network revealed a single functional module comprising 22 nodes and 46 edges, from which nine hub genes were identified. Enrichment analysis demonstrated that these genes were predominantly involved in extracellular matrix organization, collagen fibril organization, skeletal system development, and extracellular structural organization, all of which play a critical role in the pathogenesis of SS. Furthermore, drug-gene interaction analysis suggested six of the nine hub genes may be linked to FDA-approved compounds. Our results provide a systematic framework for prioritizing genes and pathways which may pave the way for future studies aimed at biomarker discovery and therapeutic exploration in SS.