Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University , Linköping, Sweden.
Expert Rev Proteomics. 2020 Jun;17(6):483-505. doi: 10.1080/14789450.2020.1797499. Epub 2020 Aug 10.
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.
缺乏表明涉及伤害感受和/或疼痛机制的生物标志物使得诊断程序变得复杂。临床疼痛研究已开始使用蛋白质组学。
本系统评价涵盖了慢性疼痛队列的蛋白质组学研究以及与临床变量的关系。在三个数据库中进行的搜索从 PubMed 中确定了 96 项研究，从 Scopus 中确定了 161 项研究，从 Web of Science 数据库中确定了 155 项研究。最后，纳入了 27 篇相关文章。对所确定的蛋白质进行了基于网络的分析。
研究了较小的疼痛队列，每个诊断和组织的研究数量较少。蛋白质组学在慢性疼痛研究中的应用是探索性的，需要进行更大规模的蛋白质组学研究。有必要对所研究的疼痛队列进行标准化描述。有必要确定特定慢性疼痛状况的整个临床表现背后的机制。必须应用能够处理和识别相互关联的蛋白质模式的多变量方法。未来的研究不应仅关注少数几种蛋白质，而应使用网络分析来研究相互作用和生物学过程。蛋白质组学与生物信息学相结合具有巨大的潜力，可以识别以前未知的与慢性疼痛相关的蛋白质组，并且在制定新的疼痛控制策略时非常重要。
