Yang Liu,1 Shikang Hao,2 Hongyu Hao,3 Guona Zheng,1 Jie Bing,1 Lin Kang,1 Jia Li,4 Huanfen Zhao,1,* Han Hao5,* 

1Department of Pathology, Hebei General Hospital, Shijiazhuang, People’s Republic of China; 2The First Clinical Medical School, Shanxi Medical University, Taiyuan, People’s Republic of China; 3Department of Neurology, Hebei General Hospital, Shijiazhuang, People’s Republic of China; 4Outpatient Department, Hebei Medical University, Shijiazhuang, People’s Republic of China; 5Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, Ministry of Education, The Key Laboratory of New Drug Pharmacology and Toxicology, Center of Innovative Drug Research and Evaluation, Hebei Medical University, Shijiazhuang, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Han Hao; Huanfen Zhao, Email 18800790@hebmu.edu.cn; ZHAO552588@126.com

Background: Recent studies have shown necroptosis may play a role in the development of inflammation-associated pain. However, research on the correlation between necroptosis-related genes and neuropathic pain in the dorsal root ganglia (DRG) is limited. This study aims to identify a gene signature related to necroptosis in DRG that can predict neuropathic pain.
Methods: The mRNA expression profiles associated with neuropathic pain (GSE24982 and GSE30691) were acquired from the Gene Expression Omnibus (GEO) database. The Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) regressions were performed in GSE24982 database to constructed the necroptosis-related diferentially expressed genes (NRDEGs) signature related to neuropathic pain. Nomogram, Receiver Operating Characteristic (ROC), GSE30691 database analysis and basic experiments were used to verify the accuracy of the signature. Go and KEGG analysis, interaction network and immune infiltration were used to analyze the biological function of the signature.
Results: A predictive signature targeting rat DRG for neuropathic pain through a variety of methods to verify the accuracy was developed based on 3 NRDEGs (TLR4, CAPN2, RIPK3). Significantly enriched KEGG and GO pathways, drug target prediction and non-coding RNAs related to the signature holded promise for advancing our understanding of potential avenues for treatment and the mechanisms underlying neuropathic pain. Immune infiltration analysis revealed which types of immune cells related to the NRDEGs signature played an important role in the occurrence and development of neuropathic pain. Basic experiments provided crucial evidence that the 3 NRDEGs in DRG served as important regulators of neuropathic pain.
Conclusion: The prediction signature based on 3 key NRDEGs showed promise in predicting the presence of neuropathic pain, which may open up new avenues for the development of novel therapies for neuropathic pain.

Keywords: DRG, neuropathic pain, necroptosis, signature, prediction