Computational Identification of Key Genes and Pathways in Fibromyalgia: A Protein Network Analysis Approach

Fibromyalgia is a chronic syndrome afflicting millions of people, predominantly women, manifesting with debilitating symptoms such as widespread musculoskeletal pain, fatigue, sleep disturbances and cognitive deficits, often accompanied by depression and anxiety. Its aetiology remains elusive and the absence of specific biomarkers makes early diagnosis and effective treatment complex challenges, with significant socioeconomic impact. Herein, the molecular landscape of FM is explored through an innovative computational approach based on protein interaction network analysis. By integrating data from the STRING and DISEASES databases, a network of 1,498 proteins was built and analysed using advanced techniques, including a customized genetic algorithm, topological analysis and clustering. From this network 173 key proteins emerged, including SLC6A4, COMT and HTR2A, genes known for their role in neurotransmitter regulation, crucial in the pathogenesis of FM, and DRD2, a dopaminergic receptor identified as a significant new player. Using clustering algorithms, functional modules enriched in biological pathways related to neurotransmitter metabolism and signalling have been identified, with an emphasis on the serotonergic and dopaminergic systems. Over-representation analysis further clarified the biological significance of these clusters, highlighting potential therapeutic targets and a crosstalk between molecular systems that could explain the complexity of symptoms. This work not only confirms the central role of neurotransmitter dysregulation in FM, but offers a deeper insight through the discovery of DRD2 and its interaction with known genes, opening new perspectives on the multifactorial nature of the disease. By demonstrating the value of biological network-based approaches, this research lays the foundation for experimentally validate the results and translate them into clinical applications.