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

Abstract

Purpose

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

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author (Lanxiang Wu) on reasonable request.

Ethics Approval Statement

This study was conducted according to the National Institutes of Health Guidelines for the use of laboratory animals. The experimental design was approved by Ethics Committee of Nanchang University.

Acknowledgments

Wei Wu and Lanxiang Wu are co-last authors for this study.

Disclosure

The authors declare that there are no conflicts of interest in this work.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant numbers: 82160227, 82360234), Natural Science Foundation of Jiangxi Province (Grant numbers:20224BAB206036), Jiangxi Provincial Department of Education Science and Technology Program Project (Grant numbers: GJJ210125), National Natural Science Foundation incubation program of the Second Affiliated Hospital of Nanchang University (Grant numbers: 2023YNFY12019) and Jiangxi Province Postgraduate Innovation Special Fund (Grant numbers: YC2022-B065).