ArticleFrontiers in immunology2025
Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Integrated Multi-Omics Analysis and Experimental Validation Identify UBE2Q2 as a Candidate Molecule Target in Intervertebral Disc Degeneration.Journal of cellular and molecular 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
- Injury thresholds and early-warning biomarkers for horizontal acceleration induced cervical damage in New Zealand rabbits.American journal of translational research · 2026Article
- Exploring the mechanisms underlying quercetin, a key component ofFrontiers in immunology · 2026Article
- Unveiling key genes for intervertebral disc degeneration prediction and potential drug discovery.Scientific reports · 2025Article
- Screening and identification of differentially expressed miRNA and mRNA for intervertebral disc degeneration on bioinformatics.European journal of medical research · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
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Abstract
Introduction: Aging is characterized by gradual structural and functional changes in the body over time, with intervertebral disc degeneration (IVDD) representing a key manifestation of spinal aging and a major contributor to low back pain (LBP). Methods: This study utilized bioinformatics and machine learning approaches to identify aging-related biomarkers associated with IVDD in whole blood samples. By analyzing GEO datasets alongside aging-related databases such as GeneCards, HAGR, and AgeAnno, we identified 15 aging-related differentially expressed genes (AIDEGs). Correlation and immune infiltration analyses were conducted on these AIDEGs, and diagnostic models were developed using WGCNA, logistic regression, random forest, support vector machine, k-nearest neighbors, and LASSO regression to identify key genes. Results: Among these, Discussion: These results highlight the diagnostic potential of AIDEGs and provide a new perspective for early intervention and treatment strategies in IVDD.
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