Identification of Genetic Biomarkers Associated with Multisite Chronic Pain Using Polygenic Analysis and Protein-Protein Interaction Networks

Abstract

Multisite chronic pain is a complex and prevalent health problem affecting various aspects of life. Identifying genetic biomarkers related to this disorder can aid in more effective diagnosis and treatment of patients. This review study aimed to investigate genetic biomarkers of multisite chronic pain using polygenic analysis and protein-protein interaction networks. To this end, more than 600 articles from PubMed, Scopus, Web of Science, and Google Scholar databases were searched within the time frame 2021–2024. Inclusion criteria involved a focus on genetic biomarkers of chronic pain and the use of polygenic analysis or protein-protein interaction networks. Exclusion criteria included animal studies, review articles without original data, and studies focused solely on clinical aspects. Ultimately, 11 articles were selected for the final review. Findings indicated that proteins S100A6, DOCK9, and ferritin were associated with an increased risk of multisite chronic pain, while PTN9 and NEUG were linked to a decreased risk. The JAK2/STAT3, ErbB, and Rap1 signaling pathways were also identified as potential therapeutic targets. Additionally, genes WLS, CHPT1, CASP5, and STAT1 showed increased expression in patients with chronic neuropathic pain. Mendelian randomization, machine learning, and transcriptomic analysis methods were effective in identifying these biomarkers. Employing multi-biomarker approaches combined with psychosocial factors resulted in higher predictive power.