Integrative analysis of ferroptosis-related genes and immune infiltration in peri-implantitis

Integrative analysis of ferroptosis-related genes and immune infiltration in peri-implantitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative analysis of ferroptosis-related genes and immune infiltration in peri-implantitis Zhongchao Wang, Guangping Wang, Jinghan Wang, Liang Shi, Weiwei Xiao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3782755/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Peri-implantitis (PI） is a common complication of dental implants that can result in implant failure. The study aims to identify genes related to PI and Ferroptosis, analyze relevant molecules in related pathways, and perform immune infiltration analysis, with the goal of investigating the classification of PI and creating a diagnostic model using its common genes. Methods The study first identified Ferroptosis-related genes (FRGs) by downloading datasets GSE33774 and GSE57631 and intersecting them with the FRGs set. Enrichment analysis, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), was conducted to compare enrichment pathways in the Control team and the PI team. Weighted Gene Co-Expression Network Analysis (WGCNA) was then used to search for genes and build logistic regression models, Support Vector Machine (SVM) models, and Least Absolute Shrinkage and Selection Operator (LASSO) regression models. A diagnostic model of PI composed of three genes HLF, PLIN2, and MAP1LC3A was obtained. The study utilized periodontal samples from patients with normal periodontal health and those with peri-implantitis to identify significant differences in the gene MAP1LC3A between the normal team and the PI team, further verifying this clinical correlation of FRHGs. Results The calibration curves of the diagnostic model showed close results, indicating better and more accurate performance. Immune cells exhibited a strong negative connection with one of the FRHGs——MAP1LC3A in both highly and low-risk categories of PI. Additionally, the FRHGs PLIN2 and HLF had strong positive and negative correlations with immune cells called monocytes, respectively. Two categories of PI, Cluster1 and Cluster2, were determined by the transcript levels of FRHGs in PI samples from the GEO Combined Datasets. Conclusion This study identified FRGs linked to PI and established a diagnostic model (HLF, PLIN2, MAP1LC3A). The accurate model revealed immunological characteristics of PI through immune infiltration analysis. Transcription levels of FRHGs classified PI samples into Cluster1 and Cluster2, enabling personalized treatment plans. Significant transcription difference of MAP1LC3A gene between normal and PI groups suggests its potential as a diagnostic biomarker. Integrating immune infiltration analysis offers new insights for PI research, enhancing understanding of its molecular mechanisms and targeted interventions. GEO database ferroptosis immune infiltration peri implantitis 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 Figure 14 Figure 15 Full Text Additional Declarations Tables 1 to 4 are available in the Supplementary Files section. Supplementary Files Table14.pdf TableS1FRGs.csv TableS2mRNATF.csv TableS3mRNAmiRNA.csv TableS4mRNARBP.csv TableS5mRNADrug.csv 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-3782755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265735299,"identity":"1da0253f-8b93-4a06-ae36-dd4d93e3b329","order_by":0,"name":"Zhongchao Wang","email":"","orcid":"","institution":"School of Stomatology of Southwest Medical University: Hospital of Stomatology of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhongchao","middleName":"","lastName":"Wang","suffix":""},{"id":265735300,"identity":"9ca73a66-feef-4da9-a238-d6d32b3ffeae","order_by":1,"name":"Guangping Wang","email":"","orcid":"","institution":"School of Stomatology of Southwest Medical University: Hospital of Stomatology of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangping","middleName":"","lastName":"Wang","suffix":""},{"id":265735301,"identity":"a67d3c9a-8813-47e1-b2ee-1c048ff9004c","order_by":2,"name":"Jinghan Wang","email":"","orcid":"","institution":"School of Stomatology of Southwest Medical University: Hospital of Stomatology of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinghan","middleName":"","lastName":"Wang","suffix":""},{"id":265735302,"identity":"9c56406b-b9b3-438d-83c7-c0b13b3a5fad","order_by":3,"name":"Liang Shi","email":"","orcid":"","institution":"School of Stomatology of Southwest Medical University: Hospital of Stomatology of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Shi","suffix":""},{"id":265735303,"identity":"1d9f0387-6227-494a-a8ff-fc42d0ad9bd8","order_by":4,"name":"Weiwei Xiao","email":"","orcid":"","institution":"School of Stomatology of Southwest Medical University: Hospital of Stomatology of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Xiao","suffix":""},{"id":265735304,"identity":"69c3a4d4-c165-4926-8b4b-80a7dddd8215","order_by":5,"name":"Yuxi Ma","email":"","orcid":"","institution":"School of Stomatology of Southwest Medical University: Hospital of Stomatology of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxi","middleName":"","lastName":"Ma","suffix":""},{"id":265735305,"identity":"9eb80f0e-ff38-4692-975a-54d5697725de","order_by":6,"name":"Mingxia Li","email":"","orcid":"","institution":"School of Stomatology of Southwest Medical University: Hospital of Stomatology of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingxia","middleName":"","lastName":"Li","suffix":""},{"id":265735306,"identity":"81efd79b-cd90-4ac1-bb61-7a1f9250bcb7","order_by":7,"name":"Liyuan Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBAC9nYG9g8fKqA8HmK08BxmYGOccYZULcycbSRpYeYxe8w4ry5x7YwExgdv2xjkzYnQYm5cuO1w4rYbCcyGc9sYDHc2ENBiz8xjID1z24FcoBY2ad42hgSDA4RtMZDmnVMH0sL+m1gtZtK8DcxgW5iJ1MJWbDjj2OH6bWceNkvOOSdhuIGgFvbmjQ8+1NQZmx1PPvjhTZmNPEFbGBg4DKAMxgYgIUFQPRCwPyBG1SgYBaNgFIxkAABHsj1/7E0wEgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2193-1392","institution":"School of Stomatology of Southwest Medical University: Hospital of Stomatology of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Liyuan","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2023-12-20 16:19:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3782755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3782755/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49383784,"identity":"bcd789ee-bc06-45d1-9f90-2e3a316dca33","added_by":"auto","created_at":"2024-01-09 19:44:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":409404,"visible":true,"origin":"","legend":"\u003cp\u003eFlow for the Comprehensive Analysis of FRDEGs\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/eb05dbe0cde05533e953e706.png"},{"id":49385996,"identity":"95351d47-9dc6-4a52-8171-9dd4c195ad66","added_by":"auto","created_at":"2024-01-09 19:52:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":375519,"visible":true,"origin":"","legend":"\u003cp\u003eBatch Effects Removal of GSE33774 and GSE57631\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eA-B. Box plot shows difference in Combined Datasets before batch effects removal （AA）and after batch effects removal removal（BB）C D. gene expression PCA before batch effects are eliminated eliminated（CC）and after batch effects removalremoval（DD）\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/0c82427a4c82901097402955.png"},{"id":49383793,"identity":"42990acc-e505-4759-8f96-8a0ccad14de3","added_by":"auto","created_at":"2024-01-09 19:44:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":557565,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs Analysis for Combined Datasets\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA. Volcano plot of the distributions of all samples in Combined Datasets B. Venn diagram intersects DEGs and\u003c/p\u003e\n\u003cp\u003eFRGs in Combined Datasets C. Heatmap displays expression patterns of FRDEGs in Combined Datasets D. Chromosome mapping of FRDEGs\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/689d2d72c882b0c85a6b9af4.png"},{"id":49383791,"identity":"a188876c-8f94-41a4-abe0-43d05ec165a6","added_by":"auto","created_at":"2024-01-09 19:44:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":321133,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;GO and KEGG Enrichment Analysis for FRDEGs\u003c/p\u003e\n\u003cp\u003eA.\u003c/p\u003e\n\u003cp\u003eThe GO and KEGG enrichment analysis of FRDEGs FRDEGs： BPBP， CCCC， MF and Pathway B E. Network diagram of enrichment analysis results of GO and KEGG of FRDEGs FRDEGs：BPBP（BB），CCCC（CC），MFMF（DD）and KEGGKEGG（EE）. The object is denoted by the orange node, the molecule is shown by the green node, and the relationship between the molecule and the item is illustrated by the line.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/3c25aaaee1ed93e9bcf0461a.png"},{"id":49383785,"identity":"fef57dd9-af4c-42cd-8fc3-d4dbe705e014","added_by":"auto","created_at":"2024-01-09 19:44:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":209153,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;GSEA for Combined Datasets\u003c/p\u003e\n\u003cp\u003eA.\u003c/p\u003e\n\u003cp\u003eGSEA enrichment results of 4 Biological function of Combined Datasets ; B E.GSEA shows PI have significant effect on Photodynamic Therapy induced Nfkb Survival Signaling Signaling（BB），Il8 CXCR2 Pathway Pathway（CC），Cytokines and Inflammatory Response Response（DD）and Il1 and Megakaryocytes in Obesity Obesity（EE）\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/6d0ae7ea28d4ff05b55f0c3e.png"},{"id":49386775,"identity":"78f0e7e7-1a98-4773-85c7-e7a4219016a6","added_by":"auto","created_at":"2024-01-09 20:00:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":225688,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA for Combined Datasets\u003c/p\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003cp\u003eB. Box plot (A) and heatmap (B) show the GSVA result between both group s . Blue was Control group and pink was PI group* p \u0026lt; 0.05, ** p \u0026lt; 0.01, p \u0026lt; 0.001. (|logFC| \u0026gt; 0.65, adj .p \u0026lt;\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/21e1f025af0c13eedbaa40be.png"},{"id":49383800,"identity":"b9abc300-c2b4-4202-b40f-af47f2412123","added_by":"auto","created_at":"2024-01-09 19:44:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":564578,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA for Combined Datasets\u003c/p\u003e\n\u003cp\u003eA. The best threshold of WGCNA is shown by scale free topology model . On the left, the picture shows the best\u003c/p\u003e\n\u003cp\u003esoft threshold The connectedness of several soft threshold conditions is depicted in the right picture. B. The result of module aggregation of genes of f 25% of variance genes. C. The upper part is cluster tree map, the lower part is gene module. D. Correlation analysis result of variance of the first 25% of gene clustering modules and PI group and Control group. E I. Venn diagrams show the gene between 32 FRDEGs and MEmagenta MEmagenta（EE），MEgreenMEgreen（FF），MEblackMEblack（GG），MEbrownMEbrown（HH）and MEyellow MEyellow（II）\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/604050420be519620a2e0347.png"},{"id":49383798,"identity":"d145a3f5-2691-41f1-9146-9a08f1af0e2c","added_by":"auto","created_at":"2024-01-09 19:44:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":171220,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Model of A\u003c/p\u003e\n\u003cp\u003eA. The forest plot of\u003c/p\u003e\n\u003cp\u003ePI diagnostic model includes 18 key genes of logistic regression model. B C. SVM algorithm finds the gene number with the lowest error rate rate（BB）and the highest accuracy accuracy（CC）. D E. Diagnostic model plot plot（DD）and variable trajectory plot （EE）of LASSO regression model\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/a73b11390b971dbf44f53893.png"},{"id":49383804,"identity":"76bd13b0-0763-438f-8251-8c8c22e1c69b","added_by":"auto","created_at":"2024-01-09 19:44:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":168726,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic and Validation Analysis of PI\u003c/p\u003e\n\u003cp\u003eA.\u003c/p\u003e\n\u003cp\u003eNomogram in the Combined Datasets of FRHG s in PI diagnostic models. B C. The PI diagnostic model is \u0026nbsp;based on the Calibration Curve diagram of the FRHG s in Combined Datasets Datasets（BB） and DAC (C). D. ROC curve of the LASSO Risk Score in the Combined Datasets.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/7e80f3b56474a50cf19baf1a.png"},{"id":49386003,"identity":"5f3effc4-db2f-4a2d-b509-dffb60e1e97b","added_by":"auto","created_at":"2024-01-09 19:52:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":336207,"visible":true,"origin":"","legend":"\u003cp\u003eRegulatory Network of Hub Genes\u003c/p\u003e\n\u003cp\u003eA. The mRNA\u003c/p\u003e\n\u003cp\u003eTF Regulatory Network of FRHGs. B. The mRNA miRNA Regulatory Network of FRHGs. C. The mRNA RBP Regulatory Network of FRHG s. D. The mRNA Drug Regulatory Network of FRHGs. mRNA are in Orange, TF in green, miRNA in Pink, RBP in blue, and Drug in purple\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/e1042565e9bc35d94d68bbee.png"},{"id":49383795,"identity":"c188204c-c532-4862-98ba-9dfed6b140f4","added_by":"auto","created_at":"2024-01-09 19:44:35","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":152524,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation and Expression Difference Analysis for Hub Genes\u003c/p\u003e\n\u003cp\u003eA. Box\u003c/p\u003e\n\u003cp\u003eplot shows FRHGs in Combined datasets. B. Grouping comparisons of FRHGs in data set GSE106090 box plot. C. FRHGs in integrated GEO Datasets. D. A correlation graph of FRHGs in dataset GSE106090. Blue is Control group, red is PI group. ** p \u0026lt;0.01, highly statistically significant; *** p \u0026lt;0.001, highly statistically \u0026nbsp;significant. The correlation coefficient is positive in orange and negative in green, and the connecting chord represents the correlation between genes. The wider the band, the darker the color, the greater the absolute value of the correlation coefficient.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/3dfc36700c4b0b28d3ba5713.png"},{"id":49385997,"identity":"7ca2a93b-7a89-4c71-bd93-71104600328a","added_by":"auto","created_at":"2024-01-09 19:52:35","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":382610,"visible":true,"origin":"","legend":"\u003cp\u003eRisk Group Immune Infiltration Analysis by ssGSEA Algorithm\u003c/p\u003e\n\u003cp\u003eA.\u003c/p\u003e\n\u003cp\u003eGrouping of immune cells in a subclass of PI disease is compared with a box plot. B C. The outcomes of the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ecorrelation analysis of the abundance of immune cell infiltration in the\u003c/p\u003e\n\u003cp\u003ePI\u003c/p\u003e\n\u003cp\u003ePI High Risk (B) and Low Risk (C) High Risk (B) and Low Risk (C) groups are shown. Dgroups are shown. D--E. Correlation dot plots of abundance of immune cell infiltration with ferroptosisE. Correlation dot plots of abundance of immune cell infiltration with ferroptosis-- associated hub genes in all the High Risk (D) and Low Risk (E) groups for associated hub genes in all the High Risk (D) and Low Risk (E) groups for PIPI. ssGSEA. ssGSEA； High Risk in pink and High Risk in pink and Low Risk in blue. * Low Risk in blue. * pp \u0026lt; 0.05. Orange is positive and green is negative.\u0026lt; 0.05. Orange is positive and green is negative.\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/9507f2986f3172428f7c1fe0.png"},{"id":49386776,"identity":"2929da3b-d664-43c6-b180-b68e81c7399d","added_by":"auto","created_at":"2024-01-09 20:00:35","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":347463,"visible":true,"origin":"","legend":"\u003cp\u003eCombined Datasets Immune Infiltration Analysis by CIBERSORT Algorithm\u003c/p\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003cp\u003eB. Bar Charts Charts（AA）and Box plotplot（BB）of immunocyte in Combined Datasets. C. Correlation landscape of Abundance of immune cell infiltration in Combined Datasets. D. Dot plots of the abundance of immune cell infiltration shows FRHGs in Combined Datasets. Blue is the Control group and pink is the PI group. Negative correlation in green and positive correlation in orange 。（|r value|= 0.3 0.3～0.5 weak association, 0.5 0.5～0.8 medium level association association）\u003c/p\u003e","description":"","filename":"Fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/0c1de9222eb82defdd4d311b.png"},{"id":49387082,"identity":"94743d50-e8bd-4d54-b5fc-64efb538c5f1","added_by":"auto","created_at":"2024-01-09 20:08:35","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":76271,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus Clustering Analysis for Hub Genes\u003c/p\u003e\n\u003cp\u003eA. Consensus cluster plot of PI samples. B\u003c/p\u003e\n\u003cp\u003eC. Concordance cumulative distribution function (CDF ) plots (B) and Delta plots (C) for concordance cluster analysis. D. 3D PCA of two subtypes of PI. E. Correlation Heatmap of \u0026nbsp;FRHG s in the subtypes of PI . CDF, Empirical Cumulative Distribution function; Cluster1 is pink and C luster 2 is blue.\u003c/p\u003e","description":"","filename":"Fig14.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/046d835bde246f334bc1596c.png"},{"id":49386005,"identity":"000cd927-881f-4c0b-bee7-ad87223c62ee","added_by":"auto","created_at":"2024-01-09 19:52:35","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":102825,"visible":true,"origin":"","legend":"\u003cp\u003efluorescence quantitative PCR of gene MAP1LC3A\u003c/p\u003e","description":"","filename":"Fig15.png","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/d298a4257c3bcecc386da43a.png"},{"id":50960968,"identity":"ab4ed288-4fd7-4c3b-b604-c85dcf9360cf","added_by":"auto","created_at":"2024-02-11 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20:00:35","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":320,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5mRNADrug.csv","url":"https://assets-eu.researchsquare.com/files/rs-3782755/v1/5669b185525f58f38754f758.csv"}],"financialInterests":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e","formattedTitle":"Integrative analysis of ferroptosis-related genes and immune infiltration in peri-implantitis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"GEO database, ferroptosis, immune infiltration, peri implantitis","lastPublishedDoi":"10.21203/rs.3.rs-3782755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3782755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003ePeri-implantitis (PI） is a common complication of dental implants that can result in implant failure. The study aims to identify genes related to PI and Ferroptosis, analyze relevant molecules in related pathways, and perform immune infiltration analysis, with the goal of investigating the classification of PI and creating a diagnostic model using its common genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u0026nbsp;\u003c/strong\u003eThe study first identified Ferroptosis-related genes (FRGs) by downloading datasets GSE33774 and GSE57631 and intersecting them with the FRGs set. Enrichment analysis, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), was conducted to compare enrichment pathways in the Control team and the PI team. Weighted Gene Co-Expression Network Analysis (WGCNA) was then used to search for genes and build logistic regression models, Support Vector Machine (SVM) models, and Least Absolute Shrinkage and Selection Operator (LASSO) regression models. A diagnostic model of PI composed of three genes HLF, PLIN2, and MAP1LC3A was obtained. The study utilized periodontal samples from patients with normal periodontal health and those with peri-implantitis to identify significant differences in the gene MAP1LC3A between the normal team and the PI team, further verifying this clinical correlation of FRHGs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe calibration curves of the diagnostic model showed close results, indicating better and more accurate performance. Immune cells exhibited a strong negative connection with one of the FRHGs——MAP1LC3A in both highly and low-risk categories of PI. Additionally, the FRHGs PLIN2 and HLF had strong positive and negative correlations with immune cells called monocytes, respectively. Two categories of PI, Cluster1 and Cluster2, were determined by the transcript levels of FRHGs in PI samples from the GEO Combined Datasets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThis study identified FRGs linked to PI and established a diagnostic model (HLF, PLIN2, MAP1LC3A). The accurate model revealed immunological characteristics of PI through immune infiltration analysis. Transcription levels of FRHGs classified PI samples into Cluster1 and Cluster2, enabling personalized treatment plans. Significant transcription difference of MAP1LC3A gene between normal and PI groups suggests its potential as a diagnostic biomarker. Integrating immune infiltration analysis offers new insights for PI research, enhancing understanding of its molecular mechanisms and targeted interventions.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Integrative analysis of ferroptosis-related genes and immune infiltration in peri-implantitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-09 19:44:30","doi":"10.21203/rs.3.rs-3782755/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":"99462e84-935b-4f5e-92a4-281aeb01cbb1","owner":[],"postedDate":"January 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-11T03:54:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-09 19:44:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3782755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3782755","identity":"rs-3782755","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}