Exploring the relationship between cartilage-associated m6a gene and osteoarthritis development based on bioinformatics and machine learning

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Methods: OA-related microarrays were obtained from the GEO database. The m6a-related genes were extracted, and their differential gene expression was analyzed using R software. Appropriate gene screening methods were selected to obtain m6a disease signature genes; m6a clinical prediction models were established; decision curve analysis (DCA) was applied to verify the model’s accuracy. Typing was performed according to m6a expression, and genetic differences between typing and differences in immune infiltration were analyzed. The correlation between the differential genes and immune cells was also analyzed. Finally, the m6a differential genes were analyzed using Metascape. Results: Random forest tree screening was used to obtain the following m6a disease signature genes for cartilage in OA: METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, and FMR1. A strong correlation was found between the expression of disease-characterizing genes and clinical disease incidence, which was higher when the total score was between 200 and 230. Based on the m6a gene expression in cartilage, the samples were divided into groups A and B, and METTL3, FMR1, and YTHDC2 had significant genetic differences in the two groups. Among the immune cells, activated CD4T, activated dendritic, natural killer T, and plasma cells were significantly different in the two groups. A significant correlation was found between the high expression of immune cells and the three m6a genes in group B. Metascape functional pathway analysis revealed that OA is mainly related to cell development, differentiation, morphological changes, chemotaxis, and inflammatory response, mainly involving the FRA pathway. Conclusion: The expression of m6a disease-characterizing genes is significantly correlated with the clinical incidence of OA, and the abnormal expression of m6a-related genes in OA cartilage is an important factor that may cause cartilage damage mainly by affecting immune cells, thus releasing relevant pro-inflammatory factors causing damage to chondrocytes. osteoarthritis cartilage tissue m6a-related gene random forest tree disease signature gene immune cells bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 INTRODUCTION Osteoarthritis (OA) is a common chronic joint disease, presenting with redness, swelling, heat, pain, dysfunction, and joint deformity. OA, the leading cause of disability in the elderly, affects 30.8 million adults in the United States and 300 million individuals worldwide 1 , 2 . The main pathological manifestation of OA is inflammation, and inflammatory cells damage the cartilage, leading to severe articular cartilage degeneration, which induces a foreign body reaction in the joint and aggravates the clinical symptoms of OA 3 – 5 . The inflammatory response caused by immune cells plays a dominant role in disease development and progression of OA. Recently, ribonucleic acid (RNA) modifications have rapidly become one of the most widely researched topics in epitranscriptomics. Its emergence as new epitranscriptomic modifications has enriched the regulatory mechanisms of gene expression and provided new insights and strategies to explore the underlying pathogenesis of diseases 6 . The most prevalent RNA modification in coding and non-coding RNAs is n6-methyladenosine (m6a), the methylation of the sixth position of adenine bases in RNA molecules. Moreover, m6a is also an important post-transcriptional regulator 7 . The regulatory proteins involved in m6a modification can be divided into three categories: “writer,” “reader,” and “eraser.” Of these, the “writer” is a methyltransferase complex containing multiple subunits that cotranscriptionally target m6a to specific sites of installed mRNAs, including METTL3/14/16, RBM15/15B, ZC3H3, VIRMA, CBLL1, WTAP, and KIAA1429. “Writers” can be removed by “erasers,” RNA demethylases, that remove methyl groups in m6a, including fat mass and obesity-associated protein (FTO) and ALKB homolog 5 (ALKBH5). “Readers” are RNA-binding proteins that regulate downstream biological functions by recognizing target transcripts and preferentially recognizing and binding to modified sites, including YTHDF1/2/3, YTHDC1/2IGF2BP1/2/3, and HNRNPA2B1 8, 9 . Studies showed that m6a is involved in various biological processes, such as human development, metabolism, immune regulation, sex determination, circadian rhythm, and cardiovascular system homeostasis, through gene expression regulation 10 . Additionally, evidence 7 , 10 showed that m6a is involved in the pathogenesis of immune-related diseases by regulating innate and adaptive immune cells through multiple mechanisms, but relevant studies investigating osteoarthritic diseases from the perspective of m6a are limited. This study used a bioinformatic approach to search public databases for m6a-related gene expression in osteoarthritic cartilage. The random forest tree method was used to screen the osteoarthritic cartilage's m6a disease signature gene. In addition, the correlation between the m6a disease signature gene and OA occurrence and its correlation with immune cells were analyzed to investigate the possible contribution of m6a-related genes to OA occurrence by affecting immune cells. MATERIALS AND METHODs Data source GSE98918 11 and GSE117999 12 gene expression profile microarrays were downloaded from the GEO database ( https://www.ncbi.nlm.nih.gov/geo ). GSE117999 and GSE98918 data contained the meniscal tissues from 12 patients with OA and 12 without OA that were obtained by arthroscopic partial meniscectomy by Prof. Rai MF. Both GSE117999 and GSE98918 datasets are based on the GPL20844 platform. Data processing and m6a differential gene screening The data from GSE98918 and GSE117999 were collated using Perl scripts, and they were processed and merged by the SVA package of the R software (version 4.1.2) to obtain the intersecting genes. Subsequently, the limma package was used to routinely correct the combined batch data. After outputting the corrected results, the data were processed through the list of m6a-related genes to extract m6a-related gene expression. Moreover, the differences in m6a gene expression between the control and experimental groups were analyzed to extract the differential gene expression and construct box line plots and heat maps of gene differences. Correlation analysis between m6a genes The data were processed using the limma, ggplot2, ggpubr, and ggExtra package in the R software (version 4.1.2) for the differential expression of the m6a gene. After reading the list of m6a-related genes, the control group was removed for correlation analysis, and the filter criteria for correlation coefficient (corFilter = 0.3) and correlation test p-value (pvalueFilter = 0.01) were set. m6a disease signature gene model screening and signature gene screening The random forest tree and machine learning methods were used to screen for disease signature genes, and the more appropriate screening method was selected by comparing the residuals of the two methods. The R software (version 4.1.2) was used to analyze the differential expression of the m6a gene, construct random forest tree and machine learning models and predict their results, plot the inverse cumulative distribution of residuals, and detect the analysis with the receiver operating characteristic (ROC) curve. After comparison, random forest trees were selected to analyze the differential gene expressions. The randomForest package was used to filter the points with the lowest cross-validation error and obtain the number of points in the tree to obtain the disease signature genes and their expressions. Construction of a predictive model of m6a disease signature genes and incidence The important gene expression data from the random forest tree were processed using the rms and rmda package in R software (version 4.1.2) to obtain the gene list. YTHDF1, METTL3, CBLL1, YTHDC2, FMR1, and YTHDC1 genes were used to construct the column/line graphs, with the Y- and X-axes representing the gene type and gene expression, respectively. The disease incidence was set as 0.1, 0.3, 0.7, and 0.99 to obtain the final column/line graphs, and the model’s accuracy was evaluated by plotting the decision curve analysis (DCA). Differences in m6a typing and m6a-related genotyping with corresponding immune cell differences The m6a differential gene expression data were processed using ConsensusClusterPlus in R software (version 4.1.2) by deleting the control group samples and only retaining the experimental group samples. Data were subjected to cluster analysis, the typing results were output, and PCA analysis was performed on the typing results to determine their accuracy. Subsequently, the m6a gene expression differences between typing groups and immune cell differences, the correlation between m6a gene and immune cells, the correlation between individual gene expression corresponding to immune cells, and plotted box line and heat maps were analyzed. Genetic difference analysis and Metascape functional analysis after m6a typing The limma package in R software (version 4.1.2) was used to collate and analyze the two data, the corrected cartilage tissue gene expression and m6a typing results, set the filter criteria (logFC = 1; p = 0.05 after correction), and obtain the sample intersection of the two data. Difference analysis was performed by comparing groups according to m6a typing results, and the difference results were filtered to obtain genes with significant differences. The Metascape website ( https://metascape.org/gp/index.html ) was used to conduct enrichment analysis of significant differential genes to obtain network models of the function of differential genes and associated pathways. RESULTS Differences in m6a-related genes in osteoarthritic cartilage We obtained 25 m6a-related genes after data processing. The “writers” were METTL3, METTL14, METTL16, WTAP, ZC3H13, RBM15, and RBM15B. The “readers” were CBLL1, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, RBMX, and IGF2BP1. Moreover, the “erasers” were ALKBH5 and FTO. The gene expressions of METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, FMR1, and IGF2BP1 were significantly different between the normal and control groups. CBLL1, YTHDC1, and IGF2BP1 were highly expressed in the disease group, and the remaining m6a-related genes were lowly expressed (Fig. 1 and Fig. 2). Correlation between m6a genes We analyzed the correlation of m6a-related genes in the control group, and a significant correlation was found between “reader” CBLL1, “writer” METTL3, and “eraser” FTO. As CBLL1 and METTL3 expression increased, the FTO also increased (Fig. 3 a and b). m6a disease signature gene model screening and signature gene screening The random forest tree and machine learning methods were used to further screen the disease signature genes from the 25 m6a-related genes of OA cartilage. A comparison of the accuracy of the two methods showed residual values between 0.4 and 0.6 for the random forest tree method and > 0.9 for the machine learning method. The ROC curves for the AUC values of the random forest tree and machine learning methods were 0.997 and 0.938, respectively (Fig. 4 a and b). The random forest tree method obtained six genes with importance values of > 2, namely METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, and FMR1, with YTHDF1 having the highest importance value (Fig. 5 a and b). m6a disease signature gene and incidence prediction model with DCA We constructed a column/line plot with YTHDF1, METTL3, CBLL1, YTHDC2, FMR1, and YTHDC1 genes to identify if the disease incidence was higher when the total score was between 200 and 230 (Fig. 6a). Figure 6b shows that the model’s accuracy is high after plotting DCA. m6a typing and typing differences The samples were typed into groups 2–13 based on the m6a gene expression in the control group, and the accuracy of the PCA of the grouped samples was used to divide them into groups A and B (Fig. 7 a and b). Figure 7b shows the PCA plot, and the two typed samples did not intersect. The m6a gene expression between groups A and B yielded significant differences in METTL3, YTHDC2, and FMR1, with group B showing high expression of METTL3, CBLL1, YTHDC2, and FMR1 (Fig. 8). Between the two groups of immune cells, a significant difference was found in activated CD4T cells, activated dendritic cells, immature dendritic cells, NK cells, neutrophils, and plasma cells, with only the neutrophils showing low expression in group B (Fig. 9). The correlation between genes and immune cells can be seen in the heat map in Fig. 10, where METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, and FMR1 are positively correlated with most immune cells. A comparative analysis of the expression of METTL3, YTHDC2, and FMR1 in different immune cells showed significant differences between high and low expressions of METTL3 and YTHDC2. The high and low expressions of METTL3 were significantly different in immune cells, such as activated B cells, activated dendritic cells, NK cells, eosinophils, monocytes, neutrophils, and plasma cells. YTHDC2 was significantly different on multiple immune cells, such as plasma, activated dendritic, regulatory T, and activated CD4T cells, whereas FMR1 was only associated with activated CD4T cells (Fig. 11 a–c). Genetic difference and Metascape analyses after m6a typing The differential analysis of osteoarthritic chondrocytes based on m6a sample typing obtained 207 significant differential genes. Metascape analysis showed that differential gene functions are associated with cell development, differentiation, morphological changes, chemotaxis, and inflammatory response, mainly involving the FRA signaling pathway (Fig. 12 and Fig. 13). DISCUSSION OA is a total joint disease characterized by cartilage damage, subchondral bone changes, bone redundancy formation, and ligament and meniscal changes, with cartilage damage being the most prominent pathological change 13 . The mechanisms of OA-induced cartilage damage are complex, and the leading causes of cartilage damage may be genetic, epigenetic, environmental factors, immune cell responses, and inflammation-related cytokines 14 . Metascape functional pathway analysis showed that OA is mainly associated with functions that regulate cell development, differentiation, morphological changes, chemotaxis, and inflammatory responses, mainly involving the FRA pathway. FRA is a related antigen of the FOS protein family 15 , mainly Fra1 (Fosl1) and Fra2 (Fosl2). Present studies on Fra 16 have focused on alveolar macrophages, and Fra1 can regulate the expression of LPS-stimulated inflammatory factors, such as interleukin (IL)-10 and IL-1β, during lung injury. In contrast, a recent study 17 found that Fra1 is also present in macrophages in OA, effectively inhibiting Arg1 expression and tilting macrophage function toward a pro-inflammatory phenotype. When Fra1 expression is reduced, symptoms of arthritis are also reduced. The inflammatory response mainly involves the functional role of immune cells. The synovial tissue in the early stages of OA contains the same immune cells as in rheumatoid arthritis, such as macrophages, T cells, and NK cells 18 . Various inflammatory cytokines have been detected in the synovial fluid of patients with OA, which is considered one of the causes of cartilage damage and repair inhibition 19 , 20 . Using single-cell sequencing of osteoarthritic chondrocytes, another study 21 found that OA cartilage contains an inflammatory amplification population dominated by IL-1 and tumor necrosis factor (TNF) receptors I and II, which may be due to the infiltration by perichondrial immune cells. Several other studies found that immune cells can inhibit the collagen and proteoglycan synthesis in chondrocytes by releasing pro-inflammatory factors, such as TNF-α, IL-1β, IL-6, IL-8, and promote the production of proteases, such as matrix metalloproteinases and aggregases, leading to cartilage destruction 22 – 24 . The FRA pathway is associated with the inflammatory response to developing OA. Many connections are found between m6a-related genes and immune cells, such as dendritic cells, T cells, B cells, and macrophages. First, METTL3 is the catalytic subunit of the “writer” complex, essential for dendritic cell maturation and functional activation. METTL3 in dendritic cells promotes m6a formation in the signaling molecule transcript. Subsequently, the modified m6a transcript is recognized by the “reader” YTHDF1, which increases the transcription of its downstream information, thus, promoting dendritic cell activation and subsequent T cell responses 25 , 26 . Second, macrophages perform various functions, including removing damaged and dead cells and debris, presenting antigens to cells, and producing cytokines and other regulatory factors to modulate the immune response. Macrophages can be polarized into M1 and M2 phenotypes. The M1 macrophages produce interferon gamma (IFN-γ) to mediate pro-inflammatory activity, whereas the M2 macrophages produce the cytokine IL-4 to mediate anti-inflammatory activity 27 . METTL3 protein levels are specifically upregulated in mouse macrophages after M1 polarization, which may be related to the direct methylation of METTL3 encoding signal transducer and activator of transcription 1 (STAT1), stimulating STAT1 protein levels to drive M1 macrophage polarization for inflammatory responses 26 , 28 . Another study 29 showed that FTO induced the downregulation of STAT1 gene expression in M1-polarized macrophages by silencing, which might be related to the FTO knockdown of phosphorylation of related genes. In this study, the analysis of the m6a gene correlation revealed a positive correlation between METTL3 and FTO expression in OA, which may be related to the results of this experimental study. B cells are the primary cells mediating humoral immunity. They rely on B-cell receptors to recognize specific antigens and differentiate into plasma cells, which produce and secrete specific antibodies that bind to target antigens 30 , 31 . Another study showed 32 that m6a modification and its regulatory factors may be involved in early B-cell development and proliferation. METTL14 deletion significantly reduced m6a levels in mRNA, leading to the deletion of genes required for B cell development, ultimately inhibiting B-cell proliferation and transition from large to small pre-B cells. YTHDF2 mediated transcriptional repression, suggesting that the m6a-related gene expression in osteoarthritic cartilage is associated with immune cells, which can act directly or release pro-inflammatory factors to cause damage to chondrocytes. The results showed that 25 m6a-related genes could be extracted from cartilage tissues. METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, FMR1, and IGF2BP1 gene expressions were significantly different between the normal and disease groups. The screening of m6a disease characteristic genes finally obtained METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, and FMR1. The samples were divided into groups A and B based on the m6a gene expression in the control group. The significant differences in immune cells between the two groups were mainly observed in activated CD4T cells, activated dendritic cells, immature dendritic cells, NK cells, neutrophils, and plasma cells. To further understand this relationship, the immune cell differences expressed by high and low expressions of METTL3, YTHDC2, and FMR1 were significantly different in the expression of immune cells, such as activated B cells, activated dendritic cells, NK cells, eosinophils, monocytes, neutrophils, and plasma cells, whereas METTL3 was significantly different in the expression of immune cells, such as activated B cells, activated dendritic cells, NK cells, eosinophils, monocytes, neutrophils, and plasma cells. YTHDC2 showed significant differences in the expression of several immune cells, including plasma, activated dendritic, regulatory T, and activated CD4T cells, whereas FMR1 was only associated with activated CD4T cells, suggesting that METTL3 and YTHDC2 in osteoarthritic chondrocytes are most deeply associated with immune cells. Treatment of ATDC5 cells with IL1-β increased the abundance of METTL3 mRNA and the proportion of m6a-methylated mRNA in total mRNA. Although interference with METTL3 decreased the proportion of IL1-β-induced apoptosis, it inhibited the IL1-β-induced increase in inflammatory cytokine levels and NFkB signaling pathway in chondrocytes. The inhibition of METTL3 expression reduced the expression of matrix metalloproteinase 13 and collagen (Coll)X and increased the expression of Aggrecan and CollII to ameliorate extracellular matrix destruction 33 . A study 34 showed that METTL3 could also promote the cleavage of pri-miR-126-5p and the formation of mature miR-126-5p by binding to DGCR8, and miR-126-5p can promote the IL-1β induction to degenerate chondrocytes. Another study also demonstrated that METTL3-mediated m6a modification of ATG7 could regulate the autophagic GATA4 signaling pathway to promote chondrocyte senescence and OA progression 35 . Although research on the mechanism of action of YTHDC2 in OA is limited, studies on the effect of m6a-related genes on immune cells in lung cancer showed that most of the YTH family members, especially YTHDC2, are significantly associated with infiltration of CD4 + T cells, CD8 + T cells, macrophages, and neutrophils. Furthermore, YTHDC2 is a promising biomarker and potential therapeutic target for lung cancer detection 36 , 37 . Based on our study results, YTHDC2 may affect cartilage tissues through lymphocyte stimulation-related pro-inflammatory factors. In conclusion, the abnormal expression of m6a-related genes in cartilage is an important factor that may cause cartilage damage and OA. The m6a-related genes mainly affect immune cells and release related pro-inflammatory factors that damage cartilage cells, which provides a new idea to investigate the pathogenesis of OA from the perspective of m6a. LIMITATIONS This study has some limitations. On the one hand, GSE117999 and GSE98918 contain high-throughput data for RA in GEO database. Beside, we did not search other databases for high-throughput data on gout because most of the current databases, such as TCGA and Oncomine, mainly provide microarray data for cancer-related research. Therefore, we will perform additional high-throughput sequencing of RA in the future. On the other hand, results are only partially verified in the query literature and the rest of them still lack experimental verification, thus further molecular biology experiments will be conducted in the follow-up work to verify gene functions at the cell or sample level. Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE Not applicable. CONSENT FOR PUBLICATION All authors agree to the terms of the BioMed Central Copyright and License Agreement. DATA AVAILABILITY STATEMENT This data comes from geo public database(https://www.ncbi.nlm.nih.gov/geo)，the data presented in this study are available on request from the corresponding author. CONFLICT OF INTEREST DISCLOSURE The authors declare there is no conflict of interest. FUNDING INFORMATION National Science Fund subsidized project, Grant/Award Number: 81960803, Guangxi University of Chinese Medicine first-class discipline construction open subject, Grant/Award Number: 2019XK026, Construction Project of Clinical Key Specialties (Trauma Surgery) of Guangxi Zhuang Autonomous Region, Key Disciplines of Health Care in Guangxi Zhuang Autonomous Region AUTHOR CONTRIBUTIONS Shilei Song and Yueping Chen designed the study. Shilei Song analyzed the data.Feng Chen prepared the manuscript. All authors were responsible for critical revisions, and all authors read and approved the final version of this work. ACKNOWLEDGMENTS We thank the technical assistance of Technology and funding in the Ruikang Hospital Affiliated to Guangxi University of Traditional Chinese Medicine and Guangxi University of l Chinese Medicine. 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Biochem Biophys Res Commun. 2019,516(1):22-7. doi:10.1016/j.bbrc.2019.05.168. Xiao L, Zhao Q, Hu B, Wang J, Liu C, Xu H. METTL3 promotes IL‐1β–induced degeneration of endplate chondrocytes by driving m6A‐dependent maturation of miR‐126‐5p. J Cell Mol Med. 2020,24(23):14013-25. doi:10.1111/jcmm.16012. Chen X, Gong W, Shao X, Shi T, Zhang L, Dong J, et al. METTL3-mediated m6A modification of ATG7 regulates autophagy-GATA4 axis to promote cellular senescence and osteoarthritis progression. Ann Rheum Dis. 2022,81(1):85-97. doi:10.1136/annrheumdis-2021–221091. Hu K, Yao L, Yan Y, Zhou L, Li J. Comprehensive analysis of YTH domain family in lung adenocarcinoma: Expression profile, association with prognostic value, and immune infiltration. Dis Markers. 2021,2021. doi:10.1155/2021/2789481. Liu M, Zhao Z, Cai Y, Bi P, Liang Q, Yan Y, et al. YTH domain family: potential prognostic targets and immune-associated biomarkers in hepatocellular carcinoma. Aging (Albany NY). 2021,13(21):24205–18. doi:10.18632/aging.203674. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2187669","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":153328142,"identity":"83778dcd-5aa4-4c99-89d5-7b75099bffcc","order_by":0,"name":"Shilei Song","email":"","orcid":"","institution":"Ruikang Hospital Affiliated to Guangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shilei","middleName":"","lastName":"Song","suffix":""},{"id":153328143,"identity":"cc33d5e0-2adf-4c87-bfd0-537519767910","order_by":1,"name":"Yueping Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACCSjNBsQHEgz+y7GxNx8gWgvjgw8VzMZ8PMcSiNMCBMyGM84wJ86TyFHAq4N/dvOxx7xtNnl80u3XpHnb2NLbGHIYGH5UbMNtyZ1j6ca8bWnFbDJnyoBaeHLbGM4eYOw5cxunFgOJHDOgysOJbRI5aUCGRG4bY18CM2MbPi3534Aq/8O0GKSzMfMYENCSwwZUeQCoJf0w0PsJCWxsBLRI3Egzk5xzLrmYTSIHFMgHDNt42BIO4vML/4zkZxJvyuzy5GekPwBG5QF5+fmPDz74UYFbCwgw8TAwJDAw8BjARQ7gVQ8EjD/AWtgfEFI4CkbBKBgFIxQAALM8VSLDFTjvAAAAAElFTkSuQmCC","orcid":"","institution":"Ruikang Hospital Affiliated to Guangxi University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yueping","middleName":"","lastName":"Chen","suffix":""},{"id":153328144,"identity":"8d1b59dd-269c-42ac-97ba-b618e29e6ef9","order_by":2,"name":"Feng Chen","email":"","orcid":"","institution":"Ruikang Hospital Affiliated to Guangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2022-10-20 16:29:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2187669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2187669/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29397178,"identity":"01f7cebe-b796-42b3-b836-bdf8794baf9a","added_by":"auto","created_at":"2022-11-22 16:18:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":278747,"visible":true,"origin":"","legend":"\u003cp\u003eGene difference box diagram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eThe horizontal and vertical coordinates represent the m6a-related genes and gene expression, respectively. The control and experimental groups are shown in blue and red, respectively. The * symbol indicates a difference between these two groups (*, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/07bc25dba912f27b4c76f40c.png"},{"id":29398971,"identity":"20115143-bb0a-421e-b452-17e228233a36","added_by":"auto","created_at":"2022-11-22 16:34:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":279613,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant difference gene heat map.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eThe blue and red horizontal coordinates represent the control and experimental groups, respectively. The vertical coordinate is the m6a differential gene. High and low expressions are shown in red and blue, respectively. The darker the color, the higher the degree. The * symbol indicates a difference between these two groups (*, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/d959bfa8db44deff18e6af56.png"},{"id":29398417,"identity":"94cfdf1b-90cb-43b9-af8a-8aea85f242b8","added_by":"auto","created_at":"2022-11-22 16:26:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":168176,"visible":true,"origin":"","legend":"\u003cp\u003e(a-b)Scatter plot of m6a differential gene correlation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eThe horizontal and vertical coordinates are the m6a-related genes. The black dots inside represent the samples. The samples are simulated, and \u003cem\u003eR\u003c/em\u003e is the correlation coefficient. \u003cem\u003eR\u003c/em\u003e \u0026gt; 0 and \u003cem\u003eR\u003c/em\u003e \u0026lt; 0 indicate positive and negative correlations, respectively. p \u0026lt; 0.05 shows a significant difference between the two genes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/07dcc7984fdcbf28bc4056c5.png"},{"id":29397176,"identity":"9a329f68-85c9-42a4-ae01-d7f6457807e4","added_by":"auto","created_at":"2022-11-22 16:18:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154626,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Inverse cumulative distribution. (b) 3b ROC curve map of residuals.\u003c/p\u003e\n\u003cp\u003eNote: RF and SVM represent random forest tree and machine learning, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/4c3f7d5779de98cca1ed41f0.png"},{"id":29398418,"identity":"ed2e7af0-38ca-48e8-8b3b-8ab604c57e4f","added_by":"auto","created_at":"2022-11-22 16:26:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145046,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Random forest tree. (b) m6A gene importance map.\u003c/p\u003e\n\u003cp\u003eNote: The horizontal and vertical coordinates represent the number of trees and error values after cross-validation, respectively. The experimental and control groups are shown in red and green, respectively. The sample error is shown in black. The number of trees corresponding to the smallest error is selected as the optimal number.\u003c/p\u003e\n\u003cp\u003eThe horizontal and vertical coordinates represent the score of gene importance and gene name, respectively. A higher value indicates a more important gene, and the gene with a score \u0026gt;2 is selected as the characteristic gene of the disease.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/934045f55d8cbad2ffd33fea.png"},{"id":29397175,"identity":"c6faf96b-7287-4782-a926-0986d3a80234","added_by":"auto","created_at":"2022-11-22 16:18:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":136067,"visible":true,"origin":"","legend":"\u003cp\u003e(a)Nomogram of m6a gene and OA. (b) DCA.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/aefd014ceef1335dfed66a86.png"},{"id":29399185,"identity":"bec11231-082b-4a78-ad92-cbcba9e702f0","added_by":"auto","created_at":"2022-11-22 16:42:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":70459,"visible":true,"origin":"","legend":"\u003cp\u003e(a) m6a typing of OA disease samples. (b) PCA.\u003c/p\u003e\n\u003cp\u003eNote: The horizontal and vertical coordinates represent PC1 and PC2, respectively. Groups A and B are shown in blue and red, respectively.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/46109dbed6e6e122bbd70708.png"},{"id":29398416,"identity":"50484b30-e117-4716-b6b3-000f3cccd528","added_by":"auto","created_at":"2022-11-22 16:26:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":119740,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot of m6a cluster gene difference.\u003c/p\u003e\n\u003cp\u003eNote: The horizontal and vertical coordinates represent the m6a-related genes and gene expression, respectively. Groups A and B are shown in blue and red, respectively. The * symbol indicates a difference between these two groups (*, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/0ba2b1bda32b55f1c8f9e84f.png"},{"id":29397179,"identity":"44ff4f6c-e9b6-460b-8423-4e7c2b0e9e60","added_by":"auto","created_at":"2022-11-22 16:18:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":320840,"visible":true,"origin":"","legend":"\u003cp\u003eBox diagram of immune cell difference in m6a cluster.\u003c/p\u003e\n\u003cp\u003eNote: The horizontal and vertical coordinates represent different immune cells and immune cell expression, respectively. Groups A and B are shown in blue and red, respectively. The * symbol indicates a difference between these two groups (*, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/cd00eb30364b466fefc200f2.png"},{"id":29397187,"identity":"d07a28d6-6fc2-4fa6-a149-e2fc8a864fed","added_by":"auto","created_at":"2022-11-22 16:18:52","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":535313,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of the correlation between m6a-related genes and immune cells.\u003c/p\u003e\n\u003cp\u003eNote: The horizontal and vertical coordinates represent m6a-related genes and immune cells, respectively. Positive and negative correlations are shown in red and blue, respectively. The color degree represents the degree of positive and negative correlations.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/dc732261f1899f7d9c972d8b.png"},{"id":29399431,"identity":"4805cffc-9abe-470a-b609-d9e418a1e0da","added_by":"auto","created_at":"2022-11-22 16:50:52","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":286208,"visible":true,"origin":"","legend":"\u003cp\u003e(a-c) Box diagram of the correlation between high and low expression differences of m6a genes and immune cells.\u003c/p\u003e\n\u003cp\u003eNote: Figures from left to right are 6a, 6b, and 6c. The horizontal and vertical coordinates represent different immune cells and immune cell expression, respectively. The high and low expression groups are shown in blue and red, respectively. The * symbol indicates a difference between these two groups (*, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/e4ecad50fa99c265cfc4d9b1.png"},{"id":29397186,"identity":"b1b0fb78-73b5-40c6-984d-486cc2b5113f","added_by":"auto","created_at":"2022-11-22 16:18:52","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":341675,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph of enriched terms across input gene lists, colored by p-values.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/7cf19210c47c6ddf862f7d75.png"},{"id":29397181,"identity":"c070d598-3a17-4459-9465-d220c9e76197","added_by":"auto","created_at":"2022-11-22 16:18:52","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":356322,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of enriched terms.\u003c/p\u003e\n\u003cp\u003eNote: The nodes represent functions or pathways, and the lines represent their correlation. More lines indicate that more genes are enriched in this function or pathway.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/a741522f38ff282598f0b6b1.png"},{"id":34374311,"identity":"8bc0ab83-fa16-4785-97f8-b295402b0c67","added_by":"auto","created_at":"2023-03-16 18:59:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3192004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2187669/v1/c006d682-dfdc-493e-a4e8-c402ea4278fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the relationship between cartilage-associated m6a gene and osteoarthritis development based on bioinformatics and machine learning","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eOsteoarthritis (OA) is a common chronic joint disease, presenting with redness, swelling, heat, pain, dysfunction, and joint deformity. OA, the leading cause of disability in the elderly, affects 30.8\u0026nbsp;million adults in the United States and 300\u0026nbsp;million individuals worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The main pathological manifestation of OA is inflammation, and inflammatory cells damage the cartilage, leading to severe articular cartilage degeneration, which induces a foreign body reaction in the joint and aggravates the clinical symptoms of OA \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The inflammatory response caused by immune cells plays a dominant role in disease development and progression of OA.\u003c/p\u003e\u003cp\u003eRecently, ribonucleic acid (RNA) modifications have rapidly become one of the most widely researched topics in epitranscriptomics. Its emergence as new epitranscriptomic modifications has enriched the regulatory mechanisms of gene expression and provided new insights and strategies to explore the underlying pathogenesis of diseases \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The most prevalent RNA modification in coding and non-coding RNAs is n6-methyladenosine (m6a), the methylation of the sixth position of adenine bases in RNA molecules. Moreover, m6a is also an important post-transcriptional regulator \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The regulatory proteins involved in m6a modification can be divided into three categories: “writer,” “reader,” and “eraser.” Of these, the “writer” is a methyltransferase complex containing multiple subunits that cotranscriptionally target m6a to specific sites of installed mRNAs, including METTL3/14/16, RBM15/15B, ZC3H3, VIRMA, CBLL1, WTAP, and KIAA1429. “Writers” can be removed by “erasers,” RNA demethylases, that remove methyl groups in m6a, including fat mass and obesity-associated protein (FTO) and ALKB homolog 5 (ALKBH5). “Readers” are RNA-binding proteins that regulate downstream biological functions by recognizing target transcripts and preferentially recognizing and binding to modified sites, including YTHDF1/2/3, YTHDC1/2IGF2BP1/2/3, and HNRNPA2B1 \u003csup\u003e8, 9\u003c/sup\u003e. Studies showed that m6a is involved in various biological processes, such as human development, metabolism, immune regulation, sex determination, circadian rhythm, and cardiovascular system homeostasis, through gene expression regulation \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, evidence \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e showed that m6a is involved in the pathogenesis of immune-related diseases by regulating innate and adaptive immune cells through multiple mechanisms, but relevant studies investigating osteoarthritic diseases from the perspective of m6a are limited.\u003c/p\u003e\u003cp\u003eThis study used a bioinformatic approach to search public databases for m6a-related gene expression in osteoarthritic cartilage. The random forest tree method was used to screen the osteoarthritic cartilage's m6a disease signature gene. In addition, the correlation between the m6a disease signature gene and OA occurrence and its correlation with immune cells were analyzed to investigate the possible contribution of m6a-related genes to OA occurrence by affecting immune cells.\u003c/p\u003e"},{"header":"MATERIALS AND METHODs","content":"\u003cp\u003e\u003cb\u003eData source\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGSE98918 \u003csup\u003e11\u003c/sup\u003e and GSE117999 \u003csup\u003e12\u003c/sup\u003e gene expression profile microarrays were downloaded from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GSE117999 and GSE98918 data contained the meniscal tissues from 12 patients with OA and 12 without OA that were obtained by arthroscopic partial meniscectomy by Prof. Rai MF. Both GSE117999 and GSE98918 datasets are based on the GPL20844 platform.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData processing and m6a differential gene screening\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data from GSE98918 and GSE117999 were collated using Perl scripts, and they were processed and merged by the SVA package of the R software (version 4.1.2) to obtain the intersecting genes. Subsequently, the limma package was used to routinely correct the combined batch data. After outputting the corrected results, the data were processed through the list of m6a-related genes to extract m6a-related gene expression. Moreover, the differences in m6a gene expression between the control and experimental groups were analyzed to extract the differential gene expression and construct box line plots and heat maps of gene differences.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analysis between m6a genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data were processed using the limma, ggplot2, ggpubr, and ggExtra package in the R software (version 4.1.2) for the differential expression of the m6a gene. After reading the list of m6a-related genes, the control group was removed for correlation analysis, and the filter criteria for correlation coefficient (corFilter = 0.3) and correlation test p-value (pvalueFilter = 0.01) were set.\u003c/p\u003e\u003cp\u003e\u003cb\u003em6a disease signature gene model screening and signature gene screening\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe random forest tree and machine learning methods were used to screen for disease signature genes, and the more appropriate screening method was selected by comparing the residuals of the two methods. The R software (version 4.1.2) was used to analyze the differential expression of the m6a gene, construct random forest tree and machine learning models and predict their results, plot the inverse cumulative distribution of residuals, and detect the analysis with the receiver operating characteristic (ROC) curve. After comparison, random forest trees were selected to analyze the differential gene expressions. The randomForest package was used to filter the points with the lowest cross-validation error and obtain the number of points in the tree to obtain the disease signature genes and their expressions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of a predictive model of m6a disease signature genes and incidence\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe important gene expression data from the random forest tree were processed using the rms and rmda package in R software (version 4.1.2) to obtain the gene list. YTHDF1, METTL3, CBLL1, YTHDC2, FMR1, and YTHDC1 genes were used to construct the column/line graphs, with the Y- and X-axes representing the gene type and gene expression, respectively. The disease incidence was set as 0.1, 0.3, 0.7, and 0.99 to obtain the final column/line graphs, and the model’s accuracy was evaluated by plotting the decision curve analysis (DCA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferences in m6a typing and m6a-related genotyping with corresponding immune cell differences\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe m6a differential gene expression data were processed using ConsensusClusterPlus in R software (version 4.1.2) by deleting the control group samples and only retaining the experimental group samples. Data were subjected to cluster analysis, the typing results were output, and PCA analysis was performed on the typing results to determine their accuracy. Subsequently, the m6a gene expression differences between typing groups and immune cell differences, the correlation between m6a gene and immune cells, the correlation between individual gene expression corresponding to immune cells, and plotted box line and heat maps were analyzed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic difference analysis and Metascape functional analysis after m6a typing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe limma package in R software (version 4.1.2) was used to collate and analyze the two data, the corrected cartilage tissue gene expression and m6a typing results, set the filter criteria (logFC = 1; p = 0.05 after correction), and obtain the sample intersection of the two data. Difference analysis was performed by comparing groups according to m6a typing results, and the difference results were filtered to obtain genes with significant differences. The Metascape website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/gp/index.html\u003c/span\u003e\u003cspan address=\"https://metascape.org/gp/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to conduct enrichment analysis of significant differential genes to obtain network models of the function of differential genes and associated pathways.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eDifferences in m6a-related genes in osteoarthritic cartilage\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe obtained 25 m6a-related genes after data processing. The “writers” were METTL3, METTL14, METTL16, WTAP, ZC3H13, RBM15, and RBM15B. The “readers” were CBLL1, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, RBMX, and IGF2BP1. Moreover, the “erasers” were ALKBH5 and FTO. The gene expressions of METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, FMR1, and IGF2BP1 were significantly different between the normal and control groups. CBLL1, YTHDC1, and IGF2BP1 were highly expressed in the disease group, and the remaining m6a-related genes were lowly expressed (Fig.\u0026nbsp;1 and Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation between m6a genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed the correlation of m6a-related genes in the control group, and a significant correlation was found between “reader” CBLL1, “writer” METTL3, and “eraser” FTO. As CBLL1 and METTL3 expression increased, the FTO also increased (Fig.\u0026nbsp;3 a and b).\u003c/p\u003e\u003cp\u003e\u003cb\u003em6a disease signature gene model screening and signature gene screening\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe random forest tree and machine learning methods were used to further screen the disease signature genes from the 25 m6a-related genes of OA cartilage. A comparison of the accuracy of the two methods showed residual values between 0.4 and 0.6 for the random forest tree method and \u0026gt; 0.9 for the machine learning method. The ROC curves for the AUC values of the random forest tree and machine learning methods were 0.997 and 0.938, respectively (Fig.\u0026nbsp;4 a and b). The random forest tree method obtained six genes with importance values of \u0026gt; 2, namely METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, and FMR1, with YTHDF1 having the highest importance value (Fig.\u0026nbsp;5 a and b).\u003c/p\u003e\u003cp\u003e\u003cb\u003em6a disease signature gene and incidence prediction model with DCA\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe constructed a column/line plot with YTHDF1, METTL3, CBLL1, YTHDC2, FMR1, and YTHDC1 genes to identify if the disease incidence was higher when the total score was between 200 and 230 (Fig.\u0026nbsp;6a). Figure\u0026nbsp;6b shows that the model’s accuracy is high after plotting DCA.\u003c/p\u003e\u003cp\u003e\u003cb\u003em6a typing and typing differences\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe samples were typed into groups 2–13 based on the m6a gene expression in the control group, and the accuracy of the PCA of the grouped samples was used to divide them into groups A and B (Fig.\u0026nbsp;7 a and b). Figure\u0026nbsp;7b shows the PCA plot, and the two typed samples did not intersect. The m6a gene expression between groups A and B yielded significant differences in METTL3, YTHDC2, and FMR1, with group B showing high expression of METTL3, CBLL1, YTHDC2, and FMR1 (Fig.\u0026nbsp;8). Between the two groups of immune cells, a significant difference was found in activated CD4T cells, activated dendritic cells, immature dendritic cells, NK cells, neutrophils, and plasma cells, with only the neutrophils showing low expression in group B (Fig.\u0026nbsp;9). The correlation between genes and immune cells can be seen in the heat map in Fig.\u0026nbsp;10, where METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, and FMR1 are positively correlated with most immune cells. A comparative analysis of the expression of METTL3, YTHDC2, and FMR1 in different immune cells showed significant differences between high and low expressions of METTL3 and YTHDC2. The high and low expressions of METTL3 were significantly different in immune cells, such as activated B cells, activated dendritic cells, NK cells, eosinophils, monocytes, neutrophils, and plasma cells. YTHDC2 was significantly different on multiple immune cells, such as plasma, activated dendritic, regulatory T, and activated CD4T cells, whereas FMR1 was only associated with activated CD4T cells (Fig.\u0026nbsp;11 a–c).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic difference and Metascape analyses after m6a typing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe differential analysis of osteoarthritic chondrocytes based on m6a sample typing obtained 207 significant differential genes. Metascape analysis showed that differential gene functions are associated with cell development, differentiation, morphological changes, chemotaxis, and inflammatory response, mainly involving the FRA signaling pathway (Fig.\u0026nbsp;12 and Fig.\u0026nbsp;13).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOA is a total joint disease characterized by cartilage damage, subchondral bone changes, bone redundancy formation, and ligament and meniscal changes, with cartilage damage being the most prominent pathological change \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The mechanisms of OA-induced cartilage damage are complex, and the leading causes of cartilage damage may be genetic, epigenetic, environmental factors, immune cell responses, and inflammation-related cytokines \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMetascape functional pathway analysis showed that OA is mainly associated with functions that regulate cell development, differentiation, morphological changes, chemotaxis, and inflammatory responses, mainly involving the FRA pathway. FRA is a related antigen of the FOS protein family \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, mainly Fra1 (Fosl1) and Fra2 (Fosl2). Present studies on Fra \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e have focused on alveolar macrophages, and Fra1 can regulate the expression of LPS-stimulated inflammatory factors, such as interleukin (IL)-10 and IL-1β, during lung injury. In contrast, a recent study \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e found that Fra1 is also present in macrophages in OA, effectively inhibiting Arg1 expression and tilting macrophage function toward a pro-inflammatory phenotype. When Fra1 expression is reduced, symptoms of arthritis are also reduced. The inflammatory response mainly involves the functional role of immune cells. The synovial tissue in the early stages of OA contains the same immune cells as in rheumatoid arthritis, such as macrophages, T cells, and NK cells \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Various inflammatory cytokines have been detected in the synovial fluid of patients with OA, which is considered one of the causes of cartilage damage and repair inhibition \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Using single-cell sequencing of osteoarthritic chondrocytes, another study \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e found that OA cartilage contains an inflammatory amplification population dominated by IL-1 and tumor necrosis factor (TNF) receptors I and II, which may be due to the infiltration by perichondrial immune cells. Several other studies found that immune cells can inhibit the collagen and proteoglycan synthesis in chondrocytes by releasing pro-inflammatory factors, such as TNF-α, IL-1β, IL-6, IL-8, and promote the production of proteases, such as matrix metalloproteinases and aggregases, leading to cartilage destruction \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The FRA pathway is associated with the inflammatory response to developing OA.\u003c/p\u003e\u003cp\u003eMany connections are found between m6a-related genes and immune cells, such as dendritic cells, T cells, B cells, and macrophages. First, METTL3 is the catalytic subunit of the “writer” complex, essential for dendritic cell maturation and functional activation. METTL3 in dendritic cells promotes m6a formation in the signaling molecule transcript. Subsequently, the modified m6a transcript is recognized by the “reader” YTHDF1, which increases the transcription of its downstream information, thus, promoting dendritic cell activation and subsequent T cell responses \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Second, macrophages perform various functions, including removing damaged and dead cells and debris, presenting antigens to cells, and producing cytokines and other regulatory factors to modulate the immune response. Macrophages can be polarized into M1 and M2 phenotypes. The M1 macrophages produce interferon gamma (IFN-γ) to mediate pro-inflammatory activity, whereas the M2 macrophages produce the cytokine IL-4 to mediate anti-inflammatory activity \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. METTL3 protein levels are specifically upregulated in mouse macrophages after M1 polarization, which may be related to the direct methylation of METTL3 encoding signal transducer and activator of transcription 1 (STAT1), stimulating STAT1 protein levels to drive M1 macrophage polarization for inflammatory responses \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Another study \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e showed that FTO induced the downregulation of STAT1 gene expression in M1-polarized macrophages by silencing, which might be related to the FTO knockdown of phosphorylation of related genes. In this study, the analysis of the m6a gene correlation revealed a positive correlation between METTL3 and FTO expression in OA, which may be related to the results of this experimental study. B cells are the primary cells mediating humoral immunity. They rely on B-cell receptors to recognize specific antigens and differentiate into plasma cells, which produce and secrete specific antibodies that bind to target antigens \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Another study showed \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e that m6a modification and its regulatory factors may be involved in early B-cell development and proliferation. METTL14 deletion significantly reduced m6a levels in mRNA, leading to the deletion of genes required for B cell development, ultimately inhibiting B-cell proliferation and transition from large to small pre-B cells. YTHDF2 mediated transcriptional repression, suggesting that the m6a-related gene expression in osteoarthritic cartilage is associated with immune cells, which can act directly or release pro-inflammatory factors to cause damage to chondrocytes.\u003c/p\u003e\u003cp\u003eThe results showed that 25 m6a-related genes could be extracted from cartilage tissues. METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, FMR1, and IGF2BP1 gene expressions were significantly different between the normal and disease groups. The screening of m6a disease characteristic genes finally obtained METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, and FMR1. The samples were divided into groups A and B based on the m6a gene expression in the control group. The significant differences in immune cells between the two groups were mainly observed in activated CD4T cells, activated dendritic cells, immature dendritic cells, NK cells, neutrophils, and plasma cells. To further understand this relationship, the immune cell differences expressed by high and low expressions of METTL3, YTHDC2, and FMR1 were significantly different in the expression of immune cells, such as activated B cells, activated dendritic cells, NK cells, eosinophils, monocytes, neutrophils, and plasma cells, whereas METTL3 was significantly different in the expression of immune cells, such as activated B cells, activated dendritic cells, NK cells, eosinophils, monocytes, neutrophils, and plasma cells. YTHDC2 showed significant differences in the expression of several immune cells, including plasma, activated dendritic, regulatory T, and activated CD4T cells, whereas FMR1 was only associated with activated CD4T cells, suggesting that METTL3 and YTHDC2 in osteoarthritic chondrocytes are most deeply associated with immune cells. Treatment of ATDC5 cells with IL1-β increased the abundance of METTL3 mRNA and the proportion of m6a-methylated mRNA in total mRNA. Although interference with METTL3 decreased the proportion of IL1-β-induced apoptosis, it inhibited the IL1-β-induced increase in inflammatory cytokine levels and NFkB signaling pathway in chondrocytes. The inhibition of METTL3 expression reduced the expression of matrix metalloproteinase 13 and collagen (Coll)X and increased the expression of Aggrecan and CollII to ameliorate extracellular matrix destruction \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. A study \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e showed that METTL3 could also promote the cleavage of pri-miR-126-5p and the formation of mature miR-126-5p by binding to DGCR8, and miR-126-5p can promote the IL-1β induction to degenerate chondrocytes. Another study also demonstrated that METTL3-mediated m6a modification of ATG7 could regulate the autophagic GATA4 signaling pathway to promote chondrocyte senescence and OA progression \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Although research on the mechanism of action of YTHDC2 in OA is limited, studies on the effect of m6a-related genes on immune cells in lung cancer showed that most of the YTH family members, especially YTHDC2, are significantly associated with infiltration of CD4 + T cells, CD8 + T cells, macrophages, and neutrophils. Furthermore, YTHDC2 is a promising biomarker and potential therapeutic target for lung cancer detection \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Based on our study results, YTHDC2 may affect cartilage tissues through lymphocyte stimulation-related pro-inflammatory factors.\u003c/p\u003e\u003cp\u003eIn conclusion, the abnormal expression of m6a-related genes in cartilage is an important factor that may cause cartilage damage and OA. The m6a-related genes mainly affect immune cells and release related pro-inflammatory factors that damage cartilage cells, which provides a new idea to investigate the pathogenesis of OA from the perspective of m6a.\u003c/p\u003e"},{"header":"LIMITATIONS","content":"\u003cp\u003eThis study has some limitations. On the one hand, GSE117999 and GSE98918 contain high-throughput data for RA in GEO database. Beside, we did not search other databases for high-throughput data on gout because most of the current databases, such as TCGA and Oncomine, mainly provide microarray data for cancer-related research. Therefore, we will perform additional high-throughput sequencing of RA in the future. On the other hand, results are only partially verified in the query literature and the rest of them still lack experimental verification, thus further molecular biology experiments will be conducted in the follow-up work to verify gene functions at the cell or sample level.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to the terms of the BioMed Central Copyright and License Agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA \u0026nbsp;AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis data comes from geo public database(https://www.ncbi.nlm.nih.gov/geo)，the data presented in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST DISCLOSURE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Science Fund subsidized project, Grant/Award Number: 81960803, Guangxi University of Chinese Medicine first-class discipline construction open subject, Grant/Award Number: 2019XK026, Construction Project of Clinical Key Specialties (Trauma Surgery) of Guangxi Zhuang Autonomous Region, Key Disciplines of Health Care in Guangxi Zhuang Autonomous Region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShilei Song and Yueping Chen designed the study. Shilei Song analyzed the data.Feng Chen prepared the manuscript. All authors were responsible for critical revisions, and all authors read and approved the final version of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the technical assistance of Technology and funding in the Ruikang Hospital Affiliated to Guangxi University of Traditional Chinese Medicine and Guangxi University of l Chinese Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShlei Song \u0026nbsp;https://orcid.org/0000-0002-3771-335X\u003c/p\u003e\n\u003cp\u003eYueping Chen https://orcid.org/0000-0003-3860-1568\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCisternas MG, Murphy L, Sacks JJ, Solomon DH, Pasta DJ, Helmick CG. Alternative methods for defining osteoarthritis and the impact on estimating prevalence in a US population‐based survey. 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Aging (Albany NY). 2021,13(21):24205\u0026ndash;18. doi:10.18632/aging.203674.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"osteoarthritis, cartilage tissue, m6a-related gene, random forest tree, disease signature gene, immune cells, bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-2187669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2187669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003eThis study aimed to analyze the expression of n6-methyladenosine (m6a)-related genes in osteoarthritis (OA), the relationship between m6a signature genes and clinical morbidity, and the correlation between m6a gene immune cells by using bioinformatics and random forest tree methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eOA-related microarrays were obtained from the GEO database. The m6a-related genes were extracted, and their differential gene expression was analyzed using R software. Appropriate gene screening methods were selected to obtain m6a disease signature genes; m6a clinical prediction models were established; decision curve analysis (DCA) was applied to verify the model’s accuracy. Typing was performed according to m6a expression, and genetic differences between typing and differences in immune infiltration were analyzed. The correlation between the differential genes and immune cells was also analyzed. Finally, the m6a differential genes were analyzed using Metascape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eRandom forest tree screening was used to obtain the following m6a disease signature genes for cartilage in OA: METTL3, CBLL1, YTHDC1, YTHDC2, YTHDF1, and FMR1. A strong correlation was found between the expression of disease-characterizing genes and clinical disease incidence, which was higher when the total score was between 200 and 230. Based on the m6a gene expression in cartilage, the samples were divided into groups A and B, and METTL3, FMR1, and YTHDC2 had significant genetic differences in the two groups. Among the immune cells, activated CD4T, activated dendritic, natural killer T, and plasma cells were significantly different in the two groups. A significant correlation was found between the high expression of immune cells and the three m6a genes in group B. Metascape functional pathway analysis revealed that OA is mainly related to cell development, differentiation, morphological changes, chemotaxis, and inflammatory response, mainly involving the FRA pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe expression of m6a disease-characterizing genes is significantly correlated with the clinical incidence of OA, and the abnormal expression of m6a-related genes in OA cartilage is an important factor that may cause cartilage damage mainly by affecting immune cells, thus releasing relevant pro-inflammatory factors causing damage to chondrocytes.\u003c/p\u003e","manuscriptTitle":"Exploring the relationship between cartilage-associated m6a gene and osteoarthritis development based on bioinformatics and machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-22 16:18:47","doi":"10.21203/rs.3.rs-2187669/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75d0132a-cfbd-4d76-bd57-a3da509d0eb1","owner":[],"postedDate":"November 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-16T18:59:16+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-22 16:18:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2187669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2187669","identity":"rs-2187669","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}