ArticleLipids in health and disease2024
Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms.
Article in Lipids in health and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 13 citations in OpenAlex.
- TNFRSF10B Implicated in Osteoarthritis Protection via the Alpha-Tocopherol-to-Sulfate Ratio: A Multiomics and Mendelian Randomization Study.International journal of molecular sciences · 2026Article
- Integrated Transcriptomics and Machine Learning Reveal Lipid Metabolism Related Genes in Ischemic Stroke.Journal of molecular neuroscience : MN · 2026Article
- RBP4-a risk factor in lipid metabolism-serves as an early diagnostic and prognostic marker of knee osteoarthritis progression.Arthritis research & therapy · 2026Article
- Advancement on localized targeting via intra-articular administration of lipid-based vesicular nanoparticles for osteoarthritis management.Journal of nanobiotechnology · 2026Review
- Article
- From algorithm to verification: based on network toxicology and machine learning, the immunomodulatory role of IGFBP1/MKI67/C9 in perfluorooctanoic acid-induced osteoarthritis was discovered, and a diagnostic model was constructed.Frontiers in immunology · 2026Article
- Identification and Experimental Validation of Key Lipid Metabolism-Related Genes in Intracerebral Hemorrhage Based on Machine Learning.Journal of inflammation research · 2026Article
- Metabolic pathways linking air pollution to osteoarthritis: Insights from a prospective cohort.PloS one · 2026Article
- Clinical Efficacy of Acupuncture for Knee Osteoarthritis and Associated Changes in Serum Lipidomics: A Prospective Observational Cohort Study.Journal of pain research · 2026Article
- Identification and validation of palmitoylation metabolism related biomarkers in osteoarthritis using transcriptomic and single cell RNA sequencing analyses.Scientific reports · 2025Article
- Network toxicology and immune-metabolic dysregulation: linking per- and polyfluoroalkyl substances exposure to osteoarthritis pathogenesis.Toxicology research · 2025Article
- Combining lipidomics and machine learning to identify lipid biomarkers for nonsyndromic cleft lip with palate.JCI insight · 2025Article
- Serum phospholipids and sphingolipids are linked to early-stage osteoarthritis by lipidomic profiling.Arthritis research & therapy · 2025Article
- Phospholipids and Sphingolipids in Osteoarthritis.Biomolecules · 2025Review
- Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches.Frontiers in cardiovascular medicine · 2025Article
- Lipidomic Signature of Plasma and Synovial Fluid in Patients with Osteoarthritis: Putative Biomarkers Determined by UHPLC-QTOF-ESI+MS.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOsteoarthritis and lipid metabolism are strongly associated, although the precise targets and regulatory mechanisms are unknown.
methodsOsteoarthritis gene expression profiles were acquired from the GEO database, while lipid metabolism-related genes (LMRGs) were sourced from the MigSB database. An intersection was conducted between these datasets to extract gene expression for subsequent differential analysis. Following this, functional analyses were performed on the differentially expressed genes (DEGs). Subsequently, machine learning was applied to identify hub genes associated with lipid metabolism in osteoarthritis. Immune-infiltration analysis was performed using CIBERSORT, and external datasets were employed to validate the expression of these hub genes.
resultsNine DEGs associated with lipid metabolism in osteoarthritis were identified. UGCG and ESYT1, which are hub genes involved in lipid metabolism in osteoarthritis, were identified through the utilization of three machine learning algorithms. Analysis of the validation dataset revealed downregulation of UGCG in the experimental group compared to the normal group and upregulation of ESYT1 in the experimental group compared to the normal group.
conclusionsUGCG and ESYT1 were considered as hub LMRGs in the development of osteoarthritis, which were regarded as candidate diagnostic markers. The effects are worth expected in the early diagnosis and treatment of osteoarthritis.
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