# Identification of gene expression signatures associated with neuroinflammation in discogenic sciatica using machine learning and experimental validation

## TL;DR

This study identifies key genes linked to neuroinflammation in sciatica using machine learning and experimental validation, offering new insights into potential treatments.

## Contribution

The study identifies four novel neuroinflammatory genes (KLRK1, LRRK2, NLRP3, and PLG) associated with sciatica through machine learning and experimental validation.

## Key findings

- Differentially expressed genes linked to neuroinflammation were identified in sciatica patients.
- Four key genes (KLRK1, LRRK2, NLRP3, and PLG) were found to be significantly associated with sciatica risk.
- Immune cell composition analysis showed correlations between these genes and immune cell abundance in affected individuals.

## Abstract

Sciatica is a debilitating condition characterized by pain radiating along the sciatic nerve, often manifesting due to underlying neuroinflammatory processes. Understanding the molecular mechanisms linking neuroinflammation to sciatica is essential for developing targeted therapeutic interventions. Recent studies have suggested that specific neuroinflammatory genes may play a pivotal role in the pathophysiology of sciatica, offering a potential avenue for understanding this condition.

This study aimed to elucidate the contributions of neuroinflammatory genes to the development of sciatica. We used publicly available datasets GSE124272 and GSE150408 from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information. By thoroughly analyzing the expression matrices, we identified differentially expressed genes (DEGs) linked to neuroinflammatory pathways. Functional annotation was performed using Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA). To enhance predictive modeling, we employed Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods to assess neuroinflammatory gene expression. Lastly, we employed quantita-tive real-time PCR (qRT-PCR) to validate our results.

The analysis revealed that the identified DEGs are significantly enriched in multiple biological pathways relevant to neuroinflammatory responses in patients with sciatica. Notably, LASSO regression and SVM techniques identified four key neuroinflammatory genes: KLRK1, LRRK2, NLRP3, and PLG. A bar graph was generated to illustrate the predictive weights of these genes concerning sciatica risk, further complemented by immune cell composition analysis via CIBERSORTx, which underscored significant correlations between these genes and the abundance of various immune cell types in affected individuals.

Our findings substantiate the critical roles of KLRK1, LRRK2, NLRP3, and PLG in the neuroinflammation-associated pathogenesis of sciatica, providing pivotal insights into the biological underpinnings of this condition. These neuroinflammatory genes serve as promising targets for advancing therapeutic strategies for sciatica management.