Proteomic studies of common chronic pain conditions - a systematic review and associated network analyses

The lack of biomarkers indicating involved nociceptive and/or pain mechanisms makes diagnostic procedures problematic. Clinical pain research has begun to use proteomics. This systematic review covers proteomic studies of chronic pain cohorts and in relation to clinical variables. Searches in three databases identified 96 studies from PubMed, 161 from Scopus and 155 from Web of Science database. Finally, 27 relevant articles were included. Network analyses based on the identified proteins were performed. Small pain cohorts were investigated and the number of studies per diagnosis and tissue is small. The use of proteomics in chronic pain research is exploratory and larger proteomic studies are needed. It will be necessary to standardize the descriptions of the pain cohorts investigated. There is a need to identify the mechanisms underlying the whole clinical presentation of specific chronic pain conditions. Multivariate methods capable of handling and identifying intercorrelated protein patterns must be applied. Rather than focusing on a few proteins, future studies should use network analyses to investigate interactions and biological processes. Proteomics in combination with bioinformatics have a huge potential to identify previously unknown panels of proteins involved in chronic pain and relevant when devising new pain control strategies.缺乏能够提示相关伤害感受及/或疼痛机制的生物标志物，使得疼痛的诊断过程面临困境。临床疼痛研究已开始应用蛋白质组学（proteomics）。本系统综述涵盖了针对慢性疼痛队列的蛋白质组学研究，以及与临床变量相关的研究内容。通过对三个学术数据库的检索，共从PubMed、Scopus及Web of Science中分别获取到96、161和155项相关研究，最终纳入27篇符合入选标准的相关文献。研究人员针对筛选得到的蛋白质开展了网络分析。当前研究所涉及的疼痛队列规模普遍较小，且针对特定诊断类型与组织样本的研究数量有限。蛋白质组学在慢性疼痛研究中的应用尚处于探索阶段，亟需开展更大规模的蛋白质组学研究。有必要对所研究的疼痛队列的描述规范进行统一。亟需明确特定慢性疼痛病症完整临床表现背后的潜在病理机制。需采用能够处理并识别相互关联蛋白质表达模式的多变量分析方法。未来的研究不应仅聚焦于少数蛋白质，而应通过网络分析探究蛋白质间的相互作用及相关生物学过程。蛋白质组学与生物信息学（bioinformatics）的结合，在识别参与慢性疼痛发生、且对制定新型疼痛控制策略具有参考价值的既往未知蛋白质组合方面具备巨大应用潜力。