ArticleBMC musculoskeletal disorders2023
Diagnostic model based on key autophagy-related genes in intervertebral disc degeneration.
Article in BMC musculoskeletal disorders, 2023. 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, 6 citations in OpenAlex.
- Proteostasis Dysfunction and Heat Shock Protein Networks in Intervertebral Disc Degeneration: Molecular Mechanisms and Therapeutic Opportunities.Current issues in molecular biology · 2026Review
- m6A-mediated silencing of RNF41 by METTL3/YTHDC1 disrupts autophagy to drive intervertebral disc degeneration.Cell biology and toxicology · 2025Article
- Apoptotic Pathway in Intervertebral Disc Degeneration: From Molecular Pathways to Clinical Interventions.Diagnostics (Basel, Switzerland) · 2025Review
- Identification and Experimental Verification of Potential Immune Cell-Associated Gene Biomarkers in Human Intervertebral Disc Degeneration.Journal of pain research · 2025Article
- Chronotherapy and intervertebral disc degeneration: understanding the role of circadian rhythm in degenerative processes.Frontiers in cell and developmental biology · 2025Review
- Structure, function, and pathology of PHF23.Frontiers in cell and developmental biology · 2025Review
- Comprehensive analysis of disulfidptosis-related genes and the immune microenvironment in heart failure.Frontiers in cell and developmental biology · 2024Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundCurrent research on autophagy is mainly focused on intervertebral disc tissues and cells, while there is few on human peripheral blood sample. therefore, this study constructed a diagnostic model to identify autophagy-related markers of intervertebral disc degeneration (IVDD).
methodsGSE150408 and GSE124272 datasets were acquired from the Gene Expression Omnibus database, and differential expression analysis was performed. The IVDD-autophagy genes were obtained using Weighted Gene Coexpression Network Analysis, and a diagnostic model was constructed and validated, followed by Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). Meanwhile, miRNA-gene and transcription factor-gene interaction networks were constructed. In addition, drug-gene interactions and target genes of methylprednisolone and glucosamine were analyzed.
resultsA total of 1,776 differentially expressed genes were identified between IVDD and control samples, and the composition of the four immune cell types was significantly different between the IVDD and control samples. The Meturquoise and Mebrown modules were significantly related to immune cells, with significant differences between the control and IVDD samples. A diagnostic model was constructed using five key IVDD-autophagy genes. The area under the curve values of the model in the training and validation datasets were 0.907 and 0.984, respectively. The enrichment scores of the two pathways were significantly different between the IVDD and healthy groups. Eight pathways in the IVDD and healthy groups had significant differences. A total of 16 miRNAs and 3 transcription factors were predicted to be of great value. In total, 84 significantly related drugs were screened for five key IVDD-autophagy genes in the diagnostic model, and three common autophagy-related target genes of methylprednisolone and glucosamine were predicted.
conclusionThis study constructs a reliable autophagy-related diagnostic model that is strongly related to the immune microenvironment of IVD. Autophagy-related genes, including PHF23, RAB24, STAT3, TOMM5, and DNAJB9, may participate in IVDD pathogenesis. In addition, methylprednisolone and glucosamine may exert therapeutic effects on IVDD by targeting CTSD, VEGFA, and BAX genes through apoptosis, as well as the sphingolipid and AGE-RAGE signaling pathways in diabetic complications.
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