Machine-learned analysis of global and glial/opioid intersection–related DNA methylation in patients with persistent pain after breast cancer surgery

Abstract

BackgroundGlial cells in the central nervous system play a key role in neuroinflammation and subsequent central sensitization to pain. They are therefore involved in the development of persistent pain. One of the main sites of interaction of the immune system with persistent pain has been identified as neuro-immune crosstalk at the glial-opioid interface. The present study examined a potential association between the DNA methylation of two key players of glial/opioid intersection and persistent postoperative pain.MethodsIn a cohort of 140 women who had undergone breast cancer surgery, and were assigned based on a 3-year follow-up to either a persistent or non-persistent pain phenotype, the role of epigenetic regulation of key players in the glial-opioid interface was assessed. The methylation of genes coding for the Toll-like receptor 4 (TLR4) as a major mediator of glial contributions to persistent pain or for the μ-opioid receptor (OPRM1) was analyzed and its association with the pain phenotype was compared with that conferred by global genome-wide DNA methylation assessed via quantification of the methylation in the retrotransposon LINE1.ResultsTraining of machine learning algorithms indicated that the global DNA methylation provided a similar diagnostic accuracy for persistent pain as previously established non-genetic predictors. However, the diagnosis can be based on a single DNA based marker. By contrast, the methylation of TLR4 or OPRM1 genes could not contribute further to the allocation of the patients to the pain-related phenotype groups.ConclusionsWhile clearly supporting a predictive utility of epigenetic testing, the present analysis cannot provide support for specific epigenetic modulation of persistent postoperative pain via methylation of two key genes of the glial-opioid interface.

Factors associated with persistent opioid use 6–12 months after primary total knee arthroplasty

“…While variations in gene expression of COMT have been associated with persistent pain following cardiac [24] and breast cancer surgery [25,26], this association was not identified in the present study. Neither pain nor persistent opioid use was associated with COMT or OPRM1 genotypes.…”

Epigenetic Alterations in Prescription Opioid Misuse: New Strategies for Precision Pain Management

“…Even if related to cancer pain, the experiment of Kringel and coworkers (2019) should also be considered because of the prospective design, which may provide more definitive answers to questions of causality. The study confirmed the predictive utility of epigenetic testing in a sample of 140 women who had undergone breast cancer surgery [35]. DNA methylation quantification was performed in the retrotransposon LINE1, a retrotransposon used as a marker of global DNA methylation being in approximately half a million copies across the human genome [66].…”

“…Analyzing the identified papers, three main categories were evidenced and grouped in Tables 1-3, respectively: (i) studies investigating epigenetic changes in animal models ( 16), (ii) studies involving human subjects (3), and (iii) studies related to intergenerational or transgenerational prescribed opioid effects (5). Among the three studies on human subjects, one was related to cancer pain patients [35]. Nevertheless, it was included because considered relevant.…”

“…No association between the DNA methylation and persistent postoperative pain was evidenced when explored in two key players of glial/opioid intersection: the toll-like receptor 4 (TLR4) and the µ-opioid receptor (OPRM1). However, the global level of DNA methylation was shown to be predictive for persistent pain, comparing 70 women with persistent pain diagnosis with 70 matched controls who had not developed persistent postsurgical pain [35].…”

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach

“…Past studies have successfully used ML strategies to predict chronic pain symptoms and development (e.g., post-surgical and rheumatoid arthritis) [27][28][29][30][31][32]. Lötsch and colleagues [27] used a ML approach to successfully categorize patients into persistent pain versus non-persistent pain groups after breast cancer surgery; the most relevant predictors included age, body mass index, depression, and anxiety.…”