Predicting Diagnostic Biomarkers Associated with Pyroptosis in Neuropathic Pain Based on Machine Learning and Experimental Validation

• Main Authors: Tian S (Author), Zheng H (Author), Wu W (Author), Wu L (Author)
• Format: Book
• Published: Dove Medical Press, 2024-02-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1178-7031
• Keywords: pyroptosis; machine learning; neuropathic pain; neuroinflammation; bioinformatic analysis; Pathology; RB1-214; Therapeutics. Pharmacology; RM1-950
• Source: Journal of Inflammation Research, Vol Volume 17, Pp 1121-1145 (2024)

Sheng Tian,1,2 Heqing Zheng,1,2 Wei Wu,1,2,* Lanxiang Wu1,2,* 1Department of Neurology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China; 2Institute of Neuroscience, Nanchang University, Nanchang, 330006, People's Republic of China*These authors contributed equally to this workCorrespondence: Wei Wu; Lanxiang Wu, Email [e-mail]; [e-mail]: Previous studies have shown that pyroptosis plays a vital role in the progress of neuropathic pain (NP), but the molecular mechanisms have not been fully elucidated. The aim of this study was to identify crucial pyroptosis-related genes (PRGs) in NP.Methods: We identified pyroptosis-related differentially expressed genes (PRDEGs) in NP by machine learning analysis of the GSE24982 and GSE60670 datasets. Furthermore, these PRDEGs were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and Friends analysis, respectively. Meanwhile, receiver operator characteristic (ROC) analysis was performed to assess the diagnostic value of PRDEGs in NP. Finally, we performed immune infiltration analysis of key PRDEGs using CIBERSORTR R package.Results: We found that 5 PRDEGs by least absolute shrinkage and selection operator (LASSO) regression and random forest and verified by RT-qPCR. GO, KEGG and GSEA revealed that these PRDEGs were mainly enriched in regulation of neuron death, IL-4 signaling, IL-23 pathway, and NF-κB pathway. ROC analysis revealed that most of the PRDEGs performed well in diagnosing NP. We also revealed transcription factors, miRNA regulatory networks and drug interaction networks of PRDEGs. For immune infiltration analysis, PRDEGs were mainly correlated with dendritic cells, monocytes and follicular T helper cells, suggested that it might be involved in the regulation of neuroimmune-related signaling.Conclusion: A total of five PRDEGs were can be employed as NP biomarkers, particularly Tlr4, Il1b and Casp8, and provide additional evidence for a vital role of pyroptosis in NP.Keywords: pyroptosis, machine learning, neuropathic pain, neuroinflammation, bioinformatic analysis