Bioinformatics analysis of differential gene expression in chondrocytes of knee osteoarthritis_West China Medical Journal

Authors：

WANG Haiming ^1,2 , ZHANG Chi ³ , WEI Xiangyang ¹ , WEI Quan ^4,5 ,  HE Chengqi ^4,5

1. Department of Rehabilitation Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, P. R. China;
2. Medical College of Zhengzhou University of Industrial Technology, Zhengzhou, Henan 451150, P. R. China;
3. Department of Rehabilitation Medicine, the Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, P. R. China;
4. Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
5. Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

HE Chengqi, Email: hxkfhcq@126.com

Keywords：

Osteoarthritis; Bioinformatics; Chondrocytes; Inflammatory factors; Cytokines

DOI：

10.7507/1002-0179.202103320

Video：

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Objective To bioinformatically analyze the gene chip data of chondrocytes from osteoarthritis patients from the Gene Expression Omnibus (GEO) database, and explore the molecular mechanisms of osteoarthritis.Methods We searched the GEO database (up to April 23rd, 2021) for data of chondrocytes and gene expression profiling in human knee osteoarthritis via the key words of “osteoarthritis OR cartilage OR chondrocyte*”. Then, we selected the samples by our inclusion criteria. The data were normalized before analysis. After differentially expressed genes were identified, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Search Tool for the Retrival of Interacting Genes/Proteinsm, R language, Perl language, Cytoscape software, and DAVID database were used to perform differentially expressed gene analysis, functional annotation, and enrichment analysis.Results The differentially expressed genes were mostly enriched in cell components and some extracellular regions, which participated in cell division, mitosis, cell proliferation and inflammatory response mainly via the regulation of protein kinase activity. The differentially expressed genes were mainly involved in the cell proliferation signaling pathway, mitogen-activated protein kinase signaling pathway, oocyte meiosis, cell cycle and so on.Conclusions Multiple signaling pathways are involved in the changes of chondrocytes in human knee osteoarthritis, mainly about cell cycle and protein metabolism genes/pathways. Inflammatory factors and cytokines may be the most important links in the pathogenesis of osteoarthritis.

Citation： WANG Haiming, ZHANG Chi, WEI Xiangyang, WEI Quan, HE Chengqi. Bioinformatics analysis of differential gene expression in chondrocytes of knee osteoarthritis. West China Medical Journal, 2021, 36(5): 623-631. doi: 10.7507/1002-0179.202103320 Copy

1.	Schiphof D, van den Driest JJ, Runhaar J. Osteoarthritis year in review 2017: rehabilitation and outcomes. Osteoarthritis Cartilage, 2018, 26(3): 326-340.
2.	Nelson AE. Osteoarthritis year in review 2017: clinical. Osteoarthritis Cartilage, 2018, 26(3): 319-325.
3.	Bennell KL, Hall M, Hinman RS. Osteoarthritis year in review 2015: rehabilitation and outcomes. Osteoarthritis Cartilage, 2016, 24(1): 58-70.
4.	Korostynski M, Malek N, Piechota M, et al. Cell-type-specific gene expression patterns in the knee cartilage in an osteoarthritis rat model. Funct Integr Genomics, 2018, 18(1): 79-87.
5.	Goode AP, Schwartz T, Kraus VB, et al. Inflammatory, structural, and pain biochemical biomarkers may reflect radiographic disc space narrowing: the johnston county osteoarthritis project. J Orthop Res, 2020, 38(5): 1027-1037.
6.	Watt FE. Osteoarthritis biomarkers: year in review. Osteoarthritis Cartilage, 2018, 26(3): 312-318.
7.	Mi B, Liu G, Zhou W, et al. Identification of genes and pathways in the synovia of women with osteoarthritis by bioinformatics analysis. Mol Med Rep, 2018, 17(3): 4467-4473.
8.	Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics, 2007, 23(19): 2507-2517.
9.	Nambiar PR, Boutin SR, Raja R, et al. Global gene expression profiling: a complement to conventional histopathologic analysis of neoplasia. Vet Pathol, 2005, 42(6): 735-752.
10.	Nambiar S, Mirmohammadsadegh A, Doroudi R, et al. Signaling networks in cutaneous melanoma metastasis identified by complementary DNA microarrays. Arch Dermatol, 2005, 141(2): 165-173.
11.	Wang H, Hu Y, Xie Y, et al. Prediction of microRNA and gene target in synovium-associated pain of knee osteoarthritis based on canonical correlation analysis. Biomed Res Int, 2019, 2019: 4506876.
12.	Irizarry RA, Bolstad BM, Collin F, et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res, 2003, 31(4): e15.
13.	Altman R, Asch E, Bloch D, et al. Development of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association. Arthritis Rheum, 1986, 29(8): 1039-1049.
14.	Dehne T, Karlsson C, Ringe J, et al. Chondrogenic differentiation potential of osteoarthritic chondrocytes and their possible use in matrix-associated autologous chondrocyte transplantation. Arthritis Res Ther, 2009, 11(5): R133.
15.	Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003, 4(2): 249-264.
16.	Mutch DM, Berger A, Mansourian R, et al. The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data. BMC Bioinformatics, 2002, 3: 17.
17.	Chen L, Zhang YH, Wang S, et al. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PLoS One, 2017, 12(9): e0184129.
18.	Huang DW, Sherman BT, Tan Q, et al. The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol, 2007, 8(9): R183.
19.	Szklarczyk D, Franceschini A, Kuhn M, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res, 2011, 39(Database issue): D561-D568.
20.	Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol, 2011, 696: 291-303.
21.	Muetze T, Lynn DJ. Using the contextual hub analysis tool (CHAT) in Cytoscape to identify contextually relevant network hubs. Curr Protoc Bioinformatics, 2017, 59: 8.24. 1-8.24. 13.
22.	Hamzeiy H, Suluyayla R, Brinkrolf C, et al. Visualization and analysis of microRNAs within KEGG pathways using VANESA. J Integr Bioinform, 2017, 14(1): 20160004.
23.	He P, Zhang Z, Liao W, et al. Screening of gene signatures for rheumatoid arthritis and osteoarthritis based on bioinformatics analysis. Mol Med Rep, 2016, 14(2): 1587-1593.
24.	Moon SM, Sa LE, Han SH, et al. Aqueous extract of codium fragile alleviates osteoarthritis through the MAPK/NF-κB pathways in IL-1β-induced rat primary chondrocytes and a rat osteoarthritis model. Biomed Pharmacother, 2018, 97: 264-270.
25.	Sun HY, Hu KZ, Yin ZS. Inhibition of the p38-MAPK signaling pathway suppresses the apoptosis and expression of proinflammatory cytokines in human osteoarthritis chondrocytes. Cytokine, 2017, 90: 135-143.
26.	许展仪, 朱根福. 基于GEO数据库分析膝骨关节炎软骨的关键差异基因. 世界最新医学信息文摘(连续型电子期刊), 2020, 20(27): 6-8.
27.	Liu Y, Jing J, Yu H, et al. Expression profiles of long non-coding RNAs in the cartilage of patients with knee osteoarthritis and normal individuals. Exp Ther Med, 2021, 21(4): 365-375.
28.	华芳, 张薇薇, 吕波等. 生物信息学分析骨关节炎滑膜炎症相关基因和分子途径. 山东大学学报(医学版), 2021, 59(3): 10-17.
29.	Chen Y, Sun Y, Pan X, et al. Joint distraction attenuates osteoarthritis by reducing secondary inflammation, cartilage degeneration and subchondral bone aberrant change. Osteoarthritis Cartilage, 2015, 23(10): 1728-1735.
30.	Geurts J, Patel A, Hirschmann MT, et al. Elevated marrow inflammatory cells and osteoclasts in subchondral osteosclerosis in human knee osteoarthritis. J Orthop Res, 2016, 34(2): 262-269.
31.	Scanzello CR. Role of low-grade inflammation in osteoarthritis. Curr Opin Rheumatol, 2017, 29(1): 79-85.
32.	Zeddou M. Osteoarthritis is a low-grade inflammatory disease: obesity’s involvement and herbal treatment. Evid Based Complement Alternat Med, 2019, 2019: 2037484.
33.	Gibson AL, Hui MC, Foote AT, et al. Wnt7a inhibits IL-1β induced catabolic gene expression and prevents articular cartilage damage in experimental osteoarthritis. Sci Rep, 2017, 7: 41823.
34.	Li ZM, Li M. Improvement in orthopedic outcome score and reduction in IL-1β, CXCL13, and TNF-α in synovial fluid of osteoarthritis patients following arthroscopic knee surgery. Genet Mol Res, 2017, 16(3).
35.	Wang SL, Zhang R, Hu KZ, et al. Interleukin-34 synovial fluid was associated with knee osteoarthritis severity: a cross-sectional study in knee osteoarthritis patients in different radiographic stages. Dis Markers, 2018, 2018: 2095480.
36.	Waly NE, Refaiy A, Aborehab NM. IL-10 and TGF-β: roles in chondroprotective effects of glucosamine in experimental osteoarthritis?. Pathophysiology, 2017, 24(1): 45-49.
37.	Barreto G, Senturk B, Colombo L, et al. Lumican is upregulated in osteoarthritis and contributes to TLR4-induced pro-inflammatory activation of cartilage degradation and macrophage polarization. Osteoarthritis Cartilage, 2020, 28(1): 92-101.

1. Schiphof D, van den Driest JJ, Runhaar J. Osteoarthritis year in review 2017: rehabilitation and outcomes. Osteoarthritis Cartilage, 2018, 26(3): 326-340.
2. Nelson AE. Osteoarthritis year in review 2017: clinical. Osteoarthritis Cartilage, 2018, 26(3): 319-325.
3. Bennell KL, Hall M, Hinman RS. Osteoarthritis year in review 2015: rehabilitation and outcomes. Osteoarthritis Cartilage, 2016, 24(1): 58-70.
4. Korostynski M, Malek N, Piechota M, et al. Cell-type-specific gene expression patterns in the knee cartilage in an osteoarthritis rat model. Funct Integr Genomics, 2018, 18(1): 79-87.
5. Goode AP, Schwartz T, Kraus VB, et al. Inflammatory, structural, and pain biochemical biomarkers may reflect radiographic disc space narrowing: the johnston county osteoarthritis project. J Orthop Res, 2020, 38(5): 1027-1037.
6. Watt FE. Osteoarthritis biomarkers: year in review. Osteoarthritis Cartilage, 2018, 26(3): 312-318.
7. Mi B, Liu G, Zhou W, et al. Identification of genes and pathways in the synovia of women with osteoarthritis by bioinformatics analysis. Mol Med Rep, 2018, 17(3): 4467-4473.
8. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics, 2007, 23(19): 2507-2517.
9. Nambiar PR, Boutin SR, Raja R, et al. Global gene expression profiling: a complement to conventional histopathologic analysis of neoplasia. Vet Pathol, 2005, 42(6): 735-752.
10. Nambiar S, Mirmohammadsadegh A, Doroudi R, et al. Signaling networks in cutaneous melanoma metastasis identified by complementary DNA microarrays. Arch Dermatol, 2005, 141(2): 165-173.
11. Wang H, Hu Y, Xie Y, et al. Prediction of microRNA and gene target in synovium-associated pain of knee osteoarthritis based on canonical correlation analysis. Biomed Res Int, 2019, 2019: 4506876.
12. Irizarry RA, Bolstad BM, Collin F, et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res, 2003, 31(4): e15.
13. Altman R, Asch E, Bloch D, et al. Development of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association. Arthritis Rheum, 1986, 29(8): 1039-1049.
14. Dehne T, Karlsson C, Ringe J, et al. Chondrogenic differentiation potential of osteoarthritic chondrocytes and their possible use in matrix-associated autologous chondrocyte transplantation. Arthritis Res Ther, 2009, 11(5): R133.
15. Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003, 4(2): 249-264.
16. Mutch DM, Berger A, Mansourian R, et al. The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data. BMC Bioinformatics, 2002, 3: 17.
17. Chen L, Zhang YH, Wang S, et al. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PLoS One, 2017, 12(9): e0184129.
18. Huang DW, Sherman BT, Tan Q, et al. The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol, 2007, 8(9): R183.
19. Szklarczyk D, Franceschini A, Kuhn M, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res, 2011, 39(Database issue): D561-D568.
20. Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol, 2011, 696: 291-303.
21. Muetze T, Lynn DJ. Using the contextual hub analysis tool (CHAT) in Cytoscape to identify contextually relevant network hubs. Curr Protoc Bioinformatics, 2017, 59: 8.24. 1-8.24. 13.
22. Hamzeiy H, Suluyayla R, Brinkrolf C, et al. Visualization and analysis of microRNAs within KEGG pathways using VANESA. J Integr Bioinform, 2017, 14(1): 20160004.
23. He P, Zhang Z, Liao W, et al. Screening of gene signatures for rheumatoid arthritis and osteoarthritis based on bioinformatics analysis. Mol Med Rep, 2016, 14(2): 1587-1593.
24. Moon SM, Sa LE, Han SH, et al. Aqueous extract of codium fragile alleviates osteoarthritis through the MAPK/NF-κB pathways in IL-1β-induced rat primary chondrocytes and a rat osteoarthritis model. Biomed Pharmacother, 2018, 97: 264-270.
25. Sun HY, Hu KZ, Yin ZS. Inhibition of the p38-MAPK signaling pathway suppresses the apoptosis and expression of proinflammatory cytokines in human osteoarthritis chondrocytes. Cytokine, 2017, 90: 135-143.
26. 许展仪, 朱根福. 基于GEO数据库分析膝骨关节炎软骨的关键差异基因. 世界最新医学信息文摘(连续型电子期刊), 2020, 20(27): 6-8.
27. Liu Y, Jing J, Yu H, et al. Expression profiles of long non-coding RNAs in the cartilage of patients with knee osteoarthritis and normal individuals. Exp Ther Med, 2021, 21(4): 365-375.
28. 华芳, 张薇薇, 吕波等. 生物信息学分析骨关节炎滑膜炎症相关基因和分子途径. 山东大学学报(医学版), 2021, 59(3): 10-17.
29. Chen Y, Sun Y, Pan X, et al. Joint distraction attenuates osteoarthritis by reducing secondary inflammation, cartilage degeneration and subchondral bone aberrant change. Osteoarthritis Cartilage, 2015, 23(10): 1728-1735.
30. Geurts J, Patel A, Hirschmann MT, et al. Elevated marrow inflammatory cells and osteoclasts in subchondral osteosclerosis in human knee osteoarthritis. J Orthop Res, 2016, 34(2): 262-269.
31. Scanzello CR. Role of low-grade inflammation in osteoarthritis. Curr Opin Rheumatol, 2017, 29(1): 79-85.
32. Zeddou M. Osteoarthritis is a low-grade inflammatory disease: obesity’s involvement and herbal treatment. Evid Based Complement Alternat Med, 2019, 2019: 2037484.
33. Gibson AL, Hui MC, Foote AT, et al. Wnt7a inhibits IL-1β induced catabolic gene expression and prevents articular cartilage damage in experimental osteoarthritis. Sci Rep, 2017, 7: 41823.
34. Li ZM, Li M. Improvement in orthopedic outcome score and reduction in IL-1β, CXCL13, and TNF-α in synovial fluid of osteoarthritis patients following arthroscopic knee surgery. Genet Mol Res, 2017, 16(3).
35. Wang SL, Zhang R, Hu KZ, et al. Interleukin-34 synovial fluid was associated with knee osteoarthritis severity: a cross-sectional study in knee osteoarthritis patients in different radiographic stages. Dis Markers, 2018, 2018: 2095480.
36. Waly NE, Refaiy A, Aborehab NM. IL-10 and TGF-β: roles in chondroprotective effects of glucosamine in experimental osteoarthritis?. Pathophysiology, 2017, 24(1): 45-49.
37. Barreto G, Senturk B, Colombo L, et al. Lumican is upregulated in osteoarthritis and contributes to TLR4-induced pro-inflammatory activation of cartilage degradation and macrophage polarization. Osteoarthritis Cartilage, 2020, 28(1): 92-101.

West China Medical Journal

Bioinformatics analysis of differential gene expression in chondrocytes of knee osteoarthritis

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