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Chronic pain (CP) is an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage lasting longer than 3 months. CP is the main reason why people seek medical care and exerts an enormous economic burden. Genome-wide expression analysis has revealed that diverse essential genetic elements are altered in CP patients. Although many possible mechanisms of CP have been revealed, we are still unable to meet all the analgesic needs of patients. In recent years, non-coding RNAs (ncRNAs) have been shown to play essential roles in peripheral neuropathy and axon regeneration, which is associated with CP occurrence and development. Multiple key ncRNAs have been identified in animal models of CP, such as microRNA-30c-5p, ciRS-7, and lncRNA MRAK009713. This review highlights different kinds of ncRNAs in the regulation of CP, which provides a more comprehensive understanding of the pathogenesis of the disease. It mainly focuses on the contributions of miRNAs, circRNAs, and lncRNAs to CP, specifically peripheral neuropathic pain (NP), diabetic NP, central NP associated with spinal cord injury, complex regional pain syndrome, inflammatory pain, and cancer-induced pain. In addition, we summarize some potential ncRNAs as novel biomarkers for CP and its complications. With an in-depth understanding of the mechanism of CP, ncRNAs may provide novel insight into CP and could become new therapeutic targets in the future.

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.