Identification of Potential Biomarkers for Osteoarthritis
Abstract
Background: We aimed to identify biomarkers associated with Osteoarthritis (OA) and evaluate their predictive capabilities.
Methods: Four synovial tissue datasets (GSE1919, GSE12021, GSE55235, GSE55457) and one peripheral blood mononuclear rcells (PBMC) dataset (GSE48556) were obtained. GSE55235 and GSE55457 were merged to conduct differential expression analysis and train machine learning algorithms. Predictive models were trained using a subset of genes and then validated on the other datasets. In addition, PBMC dataset was used to train predictive models using the same subset of genes, with the synovial tissue datasets serving as validation datasets. Finally, immune infiltration analysis was performed in the merged synovial tissue dataset using CIBERSORT.
Results: RPA3, LAMA5, SAT1, and UCP2 were used to train machine learning algorithms. Predictive models performed well in synovial tissue datasets but faced challenges in the PBMC dataset, as models achieved high sensitivity but moderate specificity. However, models trained on the PBMC dataset exhibited high sensitivity and specificity in the four external validation datasets. SAT1 exhibited the highest impact on the model performance. Immune infiltration analysis revealed significant differences in the expression of several immune cells, such as mast cells, between OA and control groups. In general, the four genes showed moderate to strong correlations with mast cells.
Conclusion: While promising, our findings point to the need for further studies to validate biomarkers and improve the models' predictive power across diverse sample types.
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26. Kim S, Myers L, Ravussin E, et al (2016). Single nucleotide polymorphisms linked to mitochondrial uncoupling protein genes UCP2 and UCP3 affect mitochondrial metabolism and healthy aging in female nonagenarians. Biogerontology, 17 (4):725-36.
27. Pelsma ICM, Claessen K, Slagboom PE, et al (2021). Variants of FOXO3 and RPA3 genes affecting IGF-1 levels alter the risk of development of primary osteoarthritis. Eur J Endocrinol, 184 (1):29-39.
28. Zubair IM, Kim B (2022). A Group Feature Ranking and Selection Method Based on Dimension Reduction Technique in High-Dimensional Data. IEEE Access, 10 125136-47.
29. Alvari G, Furlanello C, Venuti P (2021). Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD. J Clin Med, 10(8):1776.
30. Katschke KJ Jr, Rottman JB, Ruth JH, et al (2001). Differential expression of chemokine receptors on peripheral blood, synovial fluid, and synovial tissue monocytes/macrophages in rheumatoid arthritis. Arthritis Rheum, 44 (5):1022-32.
31. de Lange-Brokaar BJ, Ioan-Facsinay A, van Osch GJ, et al (2012). Synovial inflammation, immune cells and their cytokines in osteoarthritis: a review. Osteoarthritis Cartilage, 20 (12):1484-99.
32. Li YS, Luo W, Zhu SA, et al (2017). T Cells in Osteoarthritis: Alterations and Beyond. Front Immunol, 8:356.
33. Zhang Z, Ma X, Zha Z, et al (2022). The protective effects of allopurinol against IL-17A-induced inflammatory response in mast cells. Mol Immunol, 141:53-59.
2. Safiri S, Kolahi AA, Smith E, et al (2020). Global, regional and national burden of osteoarthritis 1990-2017: a systematic analysis of the Global Burden of Disease Study 2017. Ann Rheum Dis, 79 (6):819-828.
3. Vina ER, Kwoh CK (2018). Epidemiology of osteoarthritis: literature update. Curr Opin Rheumatol, 30 (2):160-167.
4. Young DA, Barter MJ, Soul J (2022). Osteoarthritis year in review: genetics, genomics, epigenetics. Osteoarthritis Cartilage, 30 (2):216-225.
5. Hu X, Ni S, Zhao K, et al (2022). Bioinformatics-Led Discovery of Osteoarthritis Biomarkers and Inflammatory Infiltrates. Front Immunol, 13: 871008.
6. Huber S, Günther S, Cambria E, et al (2022). Physiological stretching induces a differential extracellular matrix gene expression response in acetabular labrum cells. Eur Cell Mater, 44:90-100.
7. Lu Y, Zhang H, Pan H, et al (2023). Expression pattern analysis of m6A regulators reveals IGF2BP3 as a key modulator in osteoarthritis synovial macrophages. J Transl Med, 21:(1):339.
8. Evans CH, Robbins PD (1999). Potential treatment of osteoarthritis by gene therapy. Rheum Dis Clin North Am, 25:(2):333-44.
9. Libbrecht MW, Noble WS (2015). Machine learning applications in genetics and genomics. Nat Rev Genet, 16 (6):321-332.
10. Yoo C, Ramirez L, Liuzzi J (2014). Big data analysis using modern statistical and machine learning methods in medicine. Int Neurourol J, 18 (2):50-7.
11. Owusu-Adjei M, Ben Hayfron-Acquah J, Frimpong T, et al (2023). Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. PLOS Digit Health, 2 (11):e0000290.
12. Li H, Cui Y, Wang J, et al (2024). Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms. Lipids Health Dis, 23 (1):111.
13. Li Y, Dong B (2024). Exploring liquid-liquid phase separation-related diagnostic biomarkers in osteoarthritis based on machine learning algorithms and experiment. Immunobiology, 229 (5):152825.
14. Xia L, Gong N (2022). Identification and verification of ferroptosis-related genes in the synovial tissue of osteoarthritis using bioinformatics analysis. Front Mol Biosci, 29:9:992044.
15. Yongming L, Yizhe X, Zhikai Q, et al (2024). Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches. Biomarkers, 29(5):285-297.
16. Hou Y, Yang Z, Ma J, et al (2025). Screening of potential biomarkers of osteoarthritis: a bioinformatics analysis. Clin Rheumatol, 44 (1):453-463.
17. Byrne BM, Oakley GG (2019). Replication protein A, the laxative that keeps DNA regular: The importance of RPA phosphorylation in maintaining genome stability. Semin Cell Dev Biol, 86:112-120.
18. Dai Z, Wang S, Zhang W, et al (2017). Elevated Expression of RPA3 Is Involved in Gastric Cancer Tumorigenesis and Associated with Poor Patient Survival. Dig Dis Sci, 62 (9):2369-2375.
19. Qu C, Zhao Y, Feng G, et al (2017). RPA3 is a potential marker of prognosis and radioresistance for nasopharyngeal carcinoma. J Cell Mol Med, 21 (11):2872-2883.
20. Juge PA, Sparks JA, Gazal S, et al (2024). RPA3-UMAD1 rs12702634 and rheumatoid arthritis-associated interstitial lung disease in European ancestry. Rheumatol Adv Pract, 8(2):rkae059.
21. Sampaolo S, Napolitano F, Tirozzi A, et al (2017). Identification of the first dominant mutation of LAMA5 gene causing a complex multisystem syndrome due to dysfunction of the extracellular matrix. J Med Genet, 54 (10):710-720.
22. Wang YX, Zhao ZD, Wang Q, et al (2020). Biological potential alterations of migratory chondrogenic progenitor cells during knee osteoarthritic progression. Arthritis Res Ther, 22 (1):62.
23. Ou Y, Wang SJ, Li D, et al (2016). Activation of SAT1 engages polyamine metabolism with p53-mediated ferroptotic responses. Proc Natl Acad Sci U S A, 113(44):E6806-E6812.
24. Xu J, Ruan Z, Guo Z, et al (2024). Inhibition of SAT1 alleviates chondrocyte inflammation and ferroptosis by repressing ALOX15 expression and activating the Nrf2 pathway. Bone Joint Res, 13 (3):110-123.
25. Mukherjee S, Yun JW (2021). Novel regulatory roles of UCP1 in osteoblasts. Life Sci, 276:119427.
26. Kim S, Myers L, Ravussin E, et al (2016). Single nucleotide polymorphisms linked to mitochondrial uncoupling protein genes UCP2 and UCP3 affect mitochondrial metabolism and healthy aging in female nonagenarians. Biogerontology, 17 (4):725-36.
27. Pelsma ICM, Claessen K, Slagboom PE, et al (2021). Variants of FOXO3 and RPA3 genes affecting IGF-1 levels alter the risk of development of primary osteoarthritis. Eur J Endocrinol, 184 (1):29-39.
28. Zubair IM, Kim B (2022). A Group Feature Ranking and Selection Method Based on Dimension Reduction Technique in High-Dimensional Data. IEEE Access, 10 125136-47.
29. Alvari G, Furlanello C, Venuti P (2021). Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD. J Clin Med, 10(8):1776.
30. Katschke KJ Jr, Rottman JB, Ruth JH, et al (2001). Differential expression of chemokine receptors on peripheral blood, synovial fluid, and synovial tissue monocytes/macrophages in rheumatoid arthritis. Arthritis Rheum, 44 (5):1022-32.
31. de Lange-Brokaar BJ, Ioan-Facsinay A, van Osch GJ, et al (2012). Synovial inflammation, immune cells and their cytokines in osteoarthritis: a review. Osteoarthritis Cartilage, 20 (12):1484-99.
32. Li YS, Luo W, Zhu SA, et al (2017). T Cells in Osteoarthritis: Alterations and Beyond. Front Immunol, 8:356.
33. Zhang Z, Ma X, Zha Z, et al (2022). The protective effects of allopurinol against IL-17A-induced inflammatory response in mast cells. Mol Immunol, 141:53-59.
| Files | ||
| Issue | Vol 54 No 8 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v54i8.19583 | |
| Keywords | ||
| Orthopedics Osteoarthritis Differentially expressed genes Machine learning | ||
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How to Cite
1.
Shehaj F, Al-Mashhadani AJM, Li H. Identification of Potential Biomarkers for Osteoarthritis. Iran J Public Health. 2025;54(8):1742-1753.
