ArticleJOR spine2025
The Significance of MAPK Signaling Pathway in the Diagnosis and Subtype Classification of Intervertebral Disc Degeneration.
Article in JOR spine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Research on the mechanism of Duhuo in treating intervertebral disc degeneration based on bioinformatics and network pharmacology.Medicine · 2026Article
- Potential Role of Mast Cells in Intervertebral Disc Ageing, Herniation Resolution, and Degeneration: Evidence and Lessons Learned from Studies of Mast Cells in Other Connective Tissues.International journal of molecular sciences · 2026Review
- Astragalus Polysaccharide Enhances O-GlcNAcylation Through OGT to Improve Intervertebral Disc Degeneration in Rats.Journal of cellular and molecular medicine · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Intervertebral disc degeneration (IDD) is a human aging disease related mainly to inflammation, cellular senescence, RNA/DNA methylation, and ECM. The mitogen-activated protein kinase (MAPK) signaling pathway is engaged in multiple biological functions by phosphorylating specific serine and threonine residues on target proteins through phosphorylation cascade effects, but the role and specific mechanisms of the MAPK signaling pathway in IDD are still unclear. Methods: We identified 20 MAPK-related differential genes by differential analysis of the GSE124272 and GSE150408 datasets from the GEO database. To explore the biological functions of these differential genes in humans, we performed GO and KEGG analyses. Additionally, we applied PPI networks, LASSO analysis, the RF algorithm, and the SVM-RFE algorithm to identify core MAPK-related genes. Finally, we conducted further validation using clinical samples. Results: We ultimately identified and validated four pivotal MAPK-related genes, namely, KRAS, JUN, RAP1B, and TNF, using clinical samples, and constructed the ROC curves to evaluate the predictive accuracy of the hub genes. A nomogram model was subsequently developed based on these four hub MAPK genes to predict the prevalence of IDD. Based on these four hub genes, we classified IDD patients into two MAP clusters by applying the consensus clustering method and identified 1916 DEGs by analyzing the differences between the two clusters. Further analysis using the same approach allowed us to identify two gene clusters based on these DEGs. We used a PCA algorithm to determine the MAPK score for each sample and discovered that MAPK cluster A and gene cluster A had higher scores, suggesting greater sensitivity to MAPK signaling pathway-associated agents in the subtype. We displayed the differing expression levels of four hub MAPK-related genes across the two clusters and their relationship with immune cell infiltration to highlight the distinctions between clusters A and B. Conclusion: In summary, four hub MAPK signaling pathway-related genes, KRAS, JUN, RAP1B, and TNF, could be applied to the diagnosis and subtype classification of IDD and benefit the prevention and treatment of IDD.
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