Identification of antimicrobial peptides as key therapeutic targets for necrosis by sodium overload in osteoarthritis using multiple machine learning approaches: cytological validation

doi:10.12307/2026.910

Abstract

Abstract: BACKGROUND: Necrosis by sodium overload (NECSO) plays a significant role in cardiovascular diseases, but its mechanism in osteoarthritis remains unclear.
OBJECTIVE: To screen key genes related to NECSO in osteoarthritis through bioinformatics and to explore their mechanisms in osteoarthritis.
METHODS: Osteoarthritis microarray datasets GSE117999 and GSE169077 were obtained from the GEO database. Differentially expressed genes were intersected with NECSO-related genes to identify osteoarthritis-NECSO differentially expressed genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses and protein-protein interaction network analysis were performed to obtain Hub genes. Immune cell infiltration analysis and weighted gene co-expression network analysis were conducted to further screen NECSO-related and immune-related differentially expressed genes in osteoarthritis. Key genes were screened using least absolute shrinkage and selection operator, extreme gradient boosting, and random forest methods to construct and validate a diagnostic model. The correlation between key NECSO genes and diagnostic genes was analyzed using the Pearson’s correlation analysis, and potential drugs targeting diagnostic genes were predicted. Finally, cell experiments were conducted for validation.
RESULTS AND CONCLUSION: (1) Nine osteoarthritis-NECSO differentially expressed genes were identified, mainly enriched in biological processes such as cellular serine hydrolase activity, serine-type peptidase activity, and serine-type endopeptidase activity, as well as the interleukin-17 signaling pathway. (2) Immune infiltration analysis revealed differential expression of plasma cells and resting dendritic cells, suggesting these two immune cells play certain roles in osteoarthritis pathogenesis. (3) Weighted gene co-expression network analysis identified three genes: lactoferrin, cathelicidin antimicrobial peptide, and S100 calcium-binding protein A8. Combined with immune infiltration analysis, one gene, alpha-S1-casein, was identified. (4) Protein-protein interaction analysis identified five Hub genes: matrix metalloproteinase 3, apolipoprotein D, cathelicidin antimicrobial peptide, S100 calcium-binding protein A8, and lactoferrin. (5) Machine learning screened out one diagnostic gene, cathelicidin antimicrobial peptide, which was correlated with Hub genes. (6) Five drugs targeting the diagnostic gene were predicted: PEG-conjugated Toll-like receptor 7/8 agonist NKTR-262, ropocamptide, clobetasol 17-butyrate, recombinant vesicular stomatitis virus expressing interferon-beta and tyrosinase-related protein 1, and giloralimab. (7) Cell experiments showed significantly higher expression of cathelicidin antimicrobial peptide protein in chondrocytes treated with interleukin-1β compared with the blank group. These findings suggest that the cathelicidin antimicrobial peptide gene has diagnostic value for osteoarthritis, and NECSO-related genes play a role in its pathogenesis.

Key words: osteoarthritis, necrosis by sodium overload, CAMP, bioinformatics, machine learning, diagnostic model, immune infiltration, cell experiment

CLC Number:

Wang Huaijing, Guo Jinrong, Wan Dongping, Mei Qijie, Yuan Jingzhao, Xu Wenfei, Zeng Chao, Zheng Haijun, Yuan Changshen, Duan Kan. Identification of antimicrobial peptides as key therapeutic targets for necrosis by sodium overload in osteoarthritis using multiple machine learning approaches: cytological validation[J]. Chinese Journal of Tissue Engineering Research, 2026, 30(36): 9413-9422.

Figures/Tables 9

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