Generic placeholder image

Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Research Article

The Diagnostic Features of Peripheral Blood Biomarkers in Identifying Osteoarthritis Individuals: Machine Learning Strategies and Clinical Evidence

Author(s): Qiao Zhou, Jian Liu*, Ling Xin, Yuedi Hu and Yajun Qi

Volume 20, Issue 6, 2024

Published on: 31 August, 2023

Page: [928 - 942] Pages: 15

DOI: 10.2174/1573409920666230818092427

Price: $65

conference banner
Abstract

Background: People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic features and clinical value of peripheral blood biomarkers for osteoarthritis.

Objective: The goal of this project was to investigate the diagnostic features of peripheral blood and immune cell infiltration in osteoarthritis (OA).

Methods: Two eligible datasets (GSE63359 and GSE48556) were obtained from the GEO database to discern differentially expressed genes (DEGs). The machine learning strategy was employed to filtrate diagnostic biomarkers for OA. Additional verification was implemented by collecting clinical samples of OA. The CIBERSORT website estimated relative subsets of RNA transcripts to evaluate the immune-inflammatory states of OA. The link between specific DEGs and clinical immune-inflammatory markers was found by correlation analysis.

Results: Overall, 67 robust DEGs were identified. The nuclear receptor subfamily 2 group C member 2 (NR2C2), transcription factor 4 (TCF4), stromal antigen 1 (STAG1), and interleukin 18 receptor accessory protein (IL18RAP) were identified as effective diagnostic markers of OA in peripheral blood. All four diagnostic markers showed significant increases in expression in OA. Analysis of immune cell infiltration revealed that macrophages are involved in the occurrence of OA. Candidate diagnostic markers were correlated with clinical immune-inflammatory indicators of OA patients.

Conclusion: We highlight that DEGs associated with immune inflammation (NR2C2, TCF4, STAG1, and IL18RAP) may be potential biomarkers for peripheral blood in OA, which are also associated with clinical immune-inflammatory indicators.

Graphical Abstract

[1]
Sanchez-Lopez, E.; Coras, R.; Torres, A.; Lane, N.E.; Guma, M. Synovial inflammation in osteoarthritis progression. Nat. Rev. Rheumatol., 2022, 18(5), 258-275.
[http://dx.doi.org/10.1038/s41584-022-00749-9] [PMID: 35165404]
[2]
Yu, R.; Zhang, J.; Zhuo, Y.; Hong, X.; Ye, J.; Tang, S.; Zhang, Y. Identification of diagnostic signatures and immune cell infiltration characteristics in rheumatoid arthritis by integrating bioinformatic analysis and machine-learning strategies. Front. Immunol., 2021, 12, 724934.
[http://dx.doi.org/10.3389/fimmu.2021.724934] [PMID: 34691030]
[3]
Bhandari, N.; Walambe, R.; Kotecha, K.; Khare, S.P. A comprehensive survey on computational learning methods for analysis of gene expression data. Front. Mol. Biosci., 2022, 9, 907150.
[http://dx.doi.org/10.3389/fmolb.2022.907150] [PMID: 36458095]
[4]
Haubruck, P.; Pinto, M.M.; Moradi, B.; Little, C.B.; Gentek, R. Monocytes, macrophages, and their potential niches in synovial joints - therapeutic targets in post-traumatic osteoarthritis? Front. Immunol., 2021, 12(12), 763702.
[http://dx.doi.org/10.3389/fimmu.2021.763702] [PMID: 34804052]
[5]
Zhao, Y.; Xia, Y.; Kuang, G.; Cao, J.; Shen, F.; Zhu, M. Cross-tissue analysis using machine learning to identify novel biomarkers for knee osteoarthritis. Comput. Math. Methods Med., 2022, 2022, 1-21.
[http://dx.doi.org/10.1155/2022/9043300] [PMID: 35785145]
[6]
Hu, X.; Ni, S.; Zhao, K.; Qian, J.; Duan, Y. Bioinformatics-led discovery of osteoarthritis biomarkers and inflammatory infiltrates. Front. Immunol., 2022, 13, 871008.
[http://dx.doi.org/10.3389/fimmu.2022.871008] [PMID: 35734177]
[7]
Liang, Y.; Lin, F.; Huang, Y. Identification of biomarkers associated with diagnosis of osteoarthritis patients based on bioinformatics and machine learning. J. Immunol. Res., 2022, 2022, 1-11.
[http://dx.doi.org/10.1155/2022/5600190] [PMID: 35733917]
[8]
Le, T.; Aronow, R.A.; Kirshtein, A.; Shahriyari, L. A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells. Brief. Bioinform., 2021, 22(4), bbaa219.
[http://dx.doi.org/10.1093/bib/bbaa219] [PMID: 33003193]
[9]
Ramos, Y.F.M.; Bos, S.D.; Lakenberg, N.; Böhringer, S.; den Hollander, W.J.; Kloppenburg, M.; Slagboom, P.E.; Meulenbelt, I. Genes expressed in blood link osteoarthritis with apoptotic pathways. Ann. Rheum. Dis., 2014, 73(10), 1844-1853.
[http://dx.doi.org/10.1136/annrheumdis-2013-203405] [PMID: 23864235]
[10]
Irizarry, R.A.; Hobbs, B.; Collin, F.; Beazer-Barclay, Y.D.; Antonellis, K.J.; Scherf, U.; Speed, T.P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003, 4(2), 249-264.
[http://dx.doi.org/10.1093/biostatistics/4.2.249] [PMID: 12925520]
[11]
Kolde, R.; Laur, S.; Adler, P.; Vilo, J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics, 2012, 28(4), 573-580.
[http://dx.doi.org/10.1093/bioinformatics/btr709] [PMID: 22247279]
[12]
Kanehisa, M.; Furumichi, M.; Sato, Y.; Kawashima, M.; Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res., 2023, 51(D1), D587-D592.
[http://dx.doi.org/10.1093/nar/gkac963] [PMID: 36300620]
[13]
Reimand, J.; Isserlin, R.; Voisin, V.; Kucera, M.; Tannus-Lopes, C.; Rostamianfar, A.; Wadi, L.; Meyer, M.; Wong, J.; Xu, C.; Merico, D.; Bader, G.D. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc., 2019, 14(2), 482-517.
[http://dx.doi.org/10.1038/s41596-018-0103-9] [PMID: 30664679]
[14]
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B, 1996, 58(1), 267-288.
[http://dx.doi.org/10.1111/j.2517-6161.1996.tb02080.x]
[15]
Pan, X.Y.; Shen, H.B. Robust prediction of B-factor profile from sequence using two-stage SVR based on random forest feature selection. Protein Pept. Lett., 2009, 16(12), 1447-1454.
[http://dx.doi.org/10.2174/092986609789839250] [PMID: 20001907]
[16]
Butkiewicz, M.; Lowe, E., Jr; Mueller, R.; Mendenhall, J.; Teixeira, P.; Weaver, C.; Meiler, J. Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database. Molecules, 2013, 18(1), 735-756.
[http://dx.doi.org/10.3390/molecules18010735] [PMID: 23299552]
[17]
Zhang, B.; Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol., 2005, 4(1), e17.
[http://dx.doi.org/10.2202/1544-6115.1128] [PMID: 16646834]
[18]
Breiman, L. Random forests. Mach. Learn., 2001, 45(1), 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[19]
Jiang, F.; Kutia, M.; Sarkissian, A.J.; Lin, H.; Long, J.; Sun, H.; Wang, G. Estimating the growing stem volume of coniferous plantations based on random forest using an optimized variable selection method. Sensors (Basel), 2020, 20(24), 7248.
[http://dx.doi.org/10.3390/s20247248] [PMID: 33348807]
[20]
Nakao, H.; Imaoka, M.; Hida, M.; Imai, R.; Nakamura, M.; Matsumoto, K.; Kita, K. Determination of individual factors associated with hallux valgus using SVM-RFE. BMC Musculoskelet. Disord., 2023, 24(1), 534.
[http://dx.doi.org/10.1186/s12891-023-06303-2] [PMID: 37386376]
[21]
Katsoula, G.; Kreitmaier, P.; Zeggini, E. Insights into the molecular landscape of osteoarthritis in human tissues. Curr. Opin. Rheumatol., 2022, 34(1), 79-90.
[http://dx.doi.org/10.1097/BOR.0000000000000853] [PMID: 34750308]
[22]
Nedunchezhiyan, U.; Varughese, I.; Sun, A.R.; Wu, X.; Crawford, R.; Prasadam, I. Obesity, inflammation, and immune system in osteoarthritis. Front. Immunol., 2022, 13(13), 907750.
[http://dx.doi.org/10.3389/fimmu.2022.907750] [PMID: 35860250]
[23]
Visconti, V.V.; Cariati, I.; Fittipaldi, S.; Iundusi, R.; Gasbarra, E.; Tarantino, U.; Botta, A. DNA methylation signatures of bone metabolism in osteoporosis and osteoarthritis aging-related diseases: An updated review. Int. J. Mol. Sci., 2021, 22(8), 4244.
[http://dx.doi.org/10.3390/ijms22084244] [PMID: 33921902]
[24]
Videtič Paska, A.; Kouter, K. Machine learning as the new approach in understanding biomarkers of suicidal behavior. Bosn. J. Basic Med. Sci., 2021, 21(4), 398-408.
[PMID: 33485296]
[25]
Wu, L.; Guo, H.; Sun, K.; Zhao, X.; Ma, T.; Jin, Q. Sclerostin expression in the subchondral bone of patients with knee osteoarthritis. Int. J. Mol. Med., 2016, 38(5), 1395-1402.
[http://dx.doi.org/10.3892/ijmm.2016.2741] [PMID: 27665782]
[26]
Wang, J.; Fang, L.; Ye, L.; Ma, S.; Huang, H.; Lan, X.; Ma, J. miR-137 targets the inhibition of TCF4 to reverse the progression of osteoarthritis through the AMPK/NF-κB signaling pathway. Biosci. Rep., 2020, 40(6), BSR20200466.
[http://dx.doi.org/10.1042/BSR20200466] [PMID: 32432314]
[27]
Tian, J.; Gao, S.G.; Li, Y.S.; Cheng, C.; Deng, Z.H.; Luo, W.; Zhang, F.J. The β-catenin/TCF-4 pathway regulates the expression of OPN in human osteoarthritic chondrocytes. J. Orthop. Surg. Res., 2020, 15(1), 344.
[http://dx.doi.org/10.1186/s13018-020-01881-6] [PMID: 32819387]
[28]
Anazawa, Y.; Arakawa, H.; Nakagawa, H.; Nakamura, Y. Identification of STAG1 as a key mediator of a p53-dependent apoptotic pathway. Oncogene, 2004, 23(46), 7621-7627.
[http://dx.doi.org/10.1038/sj.onc.1207270] [PMID: 15361841]
[29]
Klatt, A.R.; Klinger, G.; Neumüller, O.; Eidenmüller, B.; Wagner, I.; Achenbach, T.; Aigner, T.; Bartnik, E. TAK1 downregulation reduces IL-1β induced expression of MMP13, MMP1 and TNF-alpha. Biomed. Pharmacother., 2006, 60(2), 55-61.
[http://dx.doi.org/10.1016/j.biopha.2005.08.007] [PMID: 16459052]
[30]
Klatt, A.R.; Klinger, G.; Paul-Klausch, B.; Renno, J.H.; Schmidt, J.; Malchau, G.; Wielckens, K. TAK1 mediates the collagen-II-dependent induction of the COX-2 gene and PGE2 release in primary human chondrocytes. Connect. Tissue Res., 2010, 51(6), 452-458.
[http://dx.doi.org/10.3109/03008201003668360] [PMID: 20604713]
[31]
Hedl, M.; Zheng, S.; Abraham, C. The IL18RAP region disease polymorphism decreases IL-18RAP/IL-18R1/IL-1R1 expression and signaling through innate receptor-initiated pathways. J. Immunol., 2014, 192(12), 5924-5932.
[http://dx.doi.org/10.4049/jimmunol.1302727] [PMID: 24842757]
[32]
Cherlin, S.; Lewis, M.J.; Plant, D.; Nair, N.; Goldmann, K.; Tzanis, E.; Barnes, M.R.; McKeigue, P.; Barrett, J.H.; Pitzalis, C.; Barton, A.; Cordell, H.J. Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response. Ann. Rheum. Dis., 2020, 79(11), 1446-1452.
[http://dx.doi.org/10.1136/annrheumdis-2020-217204] [PMID: 32732242]
[33]
Sun, Y.; Zuo, Z.; Kuang, Y. An Emerging target in the battle against osteoarthritis: Macrophage polarization. Int. J. Mol. Sci., 2020, 21(22), 8513.
[http://dx.doi.org/10.3390/ijms21228513] [PMID: 33198196]
[34]
Wang, L.; He, C. Nrf2-mediated anti-inflammatory polarization of macrophages as therapeutic targets for osteoarthritis. Front. Immunol., 2022, 13, 967193.
[http://dx.doi.org/10.3389/fimmu.2022.967193] [PMID: 36032081]
[35]
Rosshirt, N.; Trauth, R.; Platzer, H.; Tripel, E.; Nees, T.A.; Lorenz, H.M.; Tretter, T.; Moradi, B. Proinflammatory T cell polarization is already present in patients with early knee osteoarthritis. Arthritis Res. Ther., 2021, 23(1), 37.
[http://dx.doi.org/10.1186/s13075-020-02410-w] [PMID: 33482899]
[36]
Shiokawa, S.; Matsumoto, N.; Nishimura, J. Clonal analysis of B cells in the osteoarthritis synovium. Ann. Rheum. Dis., 2001, 60(8), 802-805.
[http://dx.doi.org/10.1136/ard.60.8.802] [PMID: 11454647]
[37]
Brauning, A.; Rae, M.; Zhu, G.; Fulton, E.; Admasu, T.D.; Stolzing, A.; Sharma, A. Aging of the immune system: Focus on natural killer cells phenotype and functions. Cells, 2022, 11(6), 1017.
[http://dx.doi.org/10.3390/cells11061017] [PMID: 35326467]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy