Identification and Analysis of Neutrophil Extracellular Trap-Related Genes in Osteoarthritis by Bioinformatics and Experimental Verification

Abstract

Tiankuo Luan,1,* Xian Yang,2,* Ge Kuang,1 Ting Wang,3 Jiaming He,1 Zhibo Liu,3 Xia Gong,1 Jingyuan Wan,2 Ke Li4 1Department of Anatomy, Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Pharmacology, Chongqing Medical University, Chongqing, People’s Republic of China; 3Department of Orthopedics, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 4Department of Orthopedics, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jingyuan Wan; Ke Li, Email [email protected]; [email protected]: Osteoarthritis (OA) is a common joint disease with long-term pain and dysfunction that negatively affects the quality of life of patients. Neutrophil extracellular traps (NETs), consisting of DNA, proteins and cytoplasm, are released by neutrophils and play an important role in a variety of diseases. However, the relationship between OA and NETs is unclear.Methods: In our study, we used bioinformatics to explore the relationship between OA and NETs and the potential biological markers. GSE55235, GSE55457, GSE117999 and GSE98918 were downloaded from the Gene Expression Omnibus (GEO) database for subsequent analysis.After differential analysis of OA expression matrices, intersection with NET-related genes (NRGs) was taken to identify Differentially expressed NRGs (DE-NRGs) in OA processes. Evaluation of immune cell infiltration by ssGSEA and CIBERSORT algorithm. The GSVA method was used to analyze the activity changes of Neutrophils pathway, Neutrophil degranulation and Neutrophil granule constituents pathway.Results: Based on RandomForest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) learning algorithms, five core genes (CRISPLD2, IL1B, SLC25A37, MMP9, and TLR7) were identified to construct an OA-related nomogram model for predicting OA progression. ROC curve results for these genes validated the nomogram’s reliability. Correlation analysis, functional enrichment, and drug predictions were performed for the core genes. TLR7 emerged as a key focus due to its high importance ranking in RF and SVM-RFE analyses. Gene Set Enrichment Analysis (GSEA) revealed a strong association between TLR7 and the Neutrophil extracellular trap pathway. Expression of core genes was demonstrated in mice OA models and human OA samples. TLR7 expression in ATDC5 cell line was significantly higher than control after TNFα induction, along with increased IL6 and MMP13.Conclusion: TLR7 may be related to NETs and affects OA.Keywords: neutrophil extracellular traps, osteoarthritis, immune infiltration, TLR7, machine learning algorithms

Keywords

• neutrophil extracellular traps
• osteoarthritis
• immune infiltration
• tlr7
• machine learning algorithms