Immunogenomic-based analysis of hierarchical clustering of diffuse large cell lymphoma

Immunogenomic-based analysis of hierarchical clustering of diffuse large cell lymphoma | 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 Immunogenomic-based analysis of hierarchical clustering of diffuse large cell lymphoma Longhao Wang, Wei Yuan, Lifeng Li, Zhibo Shen, Qishun Geng, Yuanyuan Zheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1530280/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 Diffuse Large B cell lymphoma (DLBCL) is one of the most usual type of adult lymphoma with heterogeneousness in histological morphology, prognosis and clinical indications. Prior to this, several studies were carried out to determine the DLBCL subtype based on the analysis of the genome profile.However, classification based on assessment of genes related to the immune system has limited clinical significance for DLBCL. We systematically explored the DLBCL gene expression dataset and provided publicly available clinical information on patients with GEO and TCGA. In this research, 928 DLBCL samples from the Cancer Genome Atlas (TCGA) were applied, we calculated 29 immune-related genomes' enrichment levels in each sample and stratified them into high immunity that was based on ssGSEA score (Immunity_H, n=323,68.7%), moderate (Immunity_M, n= 135, 28.7%) and low (Immunity_L, n= 12,2.6%). The ESTIMATE algorithm was used to calculate matrix score (range: -1,800.51,901.99), immunity score (range: -1,476.28,780.33), estimated score (range: -2,618.28,098.14), and tumor purity (range:0.216 0.976), All of them were significantly correlated with immune subtypes (Kruskal Wallis test, P < 0.001). At the same time, the correlation of related genes was analyzed by immunohistochemistry staining. In addition, DLBCL cells were cultured in transfected and vitro with siRNA to verify correlation analysis and gene expression. Finally, human peripheral blood lymphocytes were incubated with DLBCL cells, and stained. Flow cytometry was applied to analyze genes' influence on immune function.By analysis, immune checkpoint and HLA gene expression levels were higher in the Immunity_H group (Kruskal Wallis test, P < 0.05). The levels of Tfhs (follicular helper T cells), Monocytes, CD8+ T cells, M1 Macrophages, M2 Macrophages, CD4+ memory activated T cells, were the most excellent in Immunity_H, and total survival rate was higher in the Immunity_L. The GO term discovered in Immunity_H is connected with immunity. Through analysis, IRF4 (MUM1) was identified by us as immunotherapeutic target and a potential prognostic marker for DLBCL, which was made sure by using molecular biology experimentations. To conclude, immunosignature made a connection between DLBCL subtypes play a position in DLBCL prognostic stratification. Immunocharacteristics-related DLBCL subtypes' construction predicts expected patient results and supplies conceivable immunotherapy candida. genomic profiling immune subtypes IRF4 prognosis DLBCL Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Tables Table 1 and 2 are available in the Supplemental Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.pdf Table2.pdf 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-1530280","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":96762688,"identity":"71c0e312-c055-4e78-8515-ed32c626654e","order_by":0,"name":"Longhao Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Longhao","middleName":"","lastName":"Wang","suffix":""},{"id":96762689,"identity":"44eb8eae-0cbb-4c62-a604-21b7b9843232","order_by":1,"name":"Wei Yuan","email":"","orcid":"","institution":"People’Hospital of Zhengzhou","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yuan","suffix":""},{"id":96762690,"identity":"a9575140-e41a-4c6a-bc49-036fab91be91","order_by":2,"name":"Lifeng Li","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lifeng","middleName":"","lastName":"Li","suffix":""},{"id":96762691,"identity":"8e4b9a15-6325-470d-afba-5ea30e3b3a16","order_by":3,"name":"Zhibo Shen","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhibo","middleName":"","lastName":"Shen","suffix":""},{"id":96762692,"identity":"a6655408-0d60-4baa-b237-dd15741f63fb","order_by":4,"name":"Qishun Geng","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qishun","middleName":"","lastName":"Geng","suffix":""},{"id":96762693,"identity":"9900159d-2931-47fc-88a6-3b9a670fa88a","order_by":5,"name":"Yuanyuan Zheng","email":"","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Zheng","suffix":""},{"id":96762694,"identity":"4edffab8-a574-45e2-a67b-68aa120e45ba","order_by":6,"name":"Jie Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBAC9mYGBmYQg5+ZsfHBBwMbO4JaGGFaJNubmw1nFKQlE9bSANVicOZ4mzDPh0MgAQJa2pkPfy6osbFnuJHYxmxjcICZgf3w0Q34HcaWYDzjWBoz44zEtsc5Bnf4GHjS0m7g18JjkMzDdpiNWSKx3TjH4BkzgwSPGQEt/B8O8/w7zMMmkdgmbWFwmLGBkBbBZh7GZt62wxI8PAfbpBmI0SLNzGbMzNuXZiDB3ths2GOQlsxGyC98/Icff+b5ZmNvf5j94YMff2zs+NkPH8OrBROwkaZ8FIyCUTAKRgE2AADxDkT70+i2AQAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2022-04-06 15:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1530280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1530280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20189034,"identity":"c70c2ff9-7e09-4538-a0de-1bcf158492c3","added_by":"auto","created_at":"2022-04-11 13:11:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228773,"visible":true,"origin":"","legend":"\u003cp\u003eThe follow diagram of this study\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1/8f829e2fa33c118a20f0aad8.jpg"},{"id":20189036,"identity":"fc806860-24c2-4a02-b323-a5f01e0e81c3","added_by":"auto","created_at":"2022-04-11 13:12:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":580799,"visible":true,"origin":"","legend":"\u003cp\u003eA: Based on unsupervised cluster analysis of genomic ssGSEA score, 928 DLBCL samples were divided into three groups: Immunity_H (n = 636), Immunity_M (n = 322) and Immunity_L (n = 71).B: Heat map of Immunity_H, Immunity_M and Immunity_L subtypes according to 29 immune cell types.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1/6dbc452d998eb1c9221253d6.jpg"},{"id":20187951,"identity":"908635c0-6af6-4443-a709-8cd47cbd7f7a","added_by":"auto","created_at":"2022-04-11 13:06:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":308228,"visible":true,"origin":"","legend":"\u003cp\u003eA: Analysis of differences in tumor purity between three immune subtypes. Tumor purity was importantly more down in Immunity_H group and importantly more excellent in Immunity_L (P \u0026lt;0.001, Kruskal-Wallis test). B: Survival analysis of three immune subgroups. The survival curves of Immunity_L, Immunity_M and Immunity_H subgroups were significantly different (P = 4.396E-08). It also proved that immune grouping had a good predictive effect on the survival of diffuse LARGE B-cell lymphoma. Patients in Immunity_H had the best prognosis, patients in Immunity_L had got the poorest prognosis, and the Immunity_M was between them. C-H: The expression of PD-1, PD-L1, CD3D, HIF1A, IRF4 and other genes was meaningfully correlated with the immune subgroup.The expressions of PD-1, PD-L1, CD3D, HIF1A, IRF4 and other genes were meaningfully dissimilar between Immunity_L and Immunity_H (ANOVA text, P \u0026lt;0.001).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1/077a1420599dfcfe6c15257f.jpg"},{"id":20187952,"identity":"1abc3afa-38a0-40a8-ac22-5324724f89a7","added_by":"auto","created_at":"2022-04-11 13:06:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":505788,"visible":true,"origin":"","legend":"\u003cp\u003eA : Immune subsets were significantly associated with HLA family genes. among 24 HLA-related genes, only five genes, HLA-G, HLA-DRB6, HLA-DPB2, HLA-DOB, and HLA-B, were not significant in the distribution of immune subsets. The remaining HLA family members were statistically distributed in the immune subgroups (p\u0026lt;0.06). B: Immune subtypes were significantly associated with immune cell infiltration.Monocytes, M1 Macrophages, M2 Macrophages, CD8+ T cells, CD4+ memory activated T cells and the follicular helper T cells were substantially high up in the Immunity_H group than in the Immunity_M groups and Immunity_L. The results of B cells naive, B cells memory, Plasma cells and CD4+ naive T cells in Immunity_L were considerably more excellent than those in Immunity_M and Immunity_H. C-D: GO and KEGG analysis Differential gene enrichment analysis of Immunity_H and Immunity_L groups.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1/abc62b3466403ce6d245563b.jpg"},{"id":20187954,"identity":"1867d706-7c96-404e-ae80-3089aba72eef","added_by":"auto","created_at":"2022-04-11 13:06:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":399565,"visible":true,"origin":"","legend":"\u003cp\u003eA: IHC was used to detect the expression of IRF4 and PD-L1 in DLBCL. Two cases were stained with IRF4 and PD-L1 immunohistochemistry.Examine the section under a microscope. B: The images described are representative of 30 cases of DLBCL. Correlation between IRF4 IHC score and IRF4 IHC score in 30 DLBCL patients,calculated by Spearman's rank correlation methods, in 30 DLBCL cases. C: We transfected siControl and siIRF4 into DS cell line by transient transfection method. Proteins were collected and lysed, and the displayed proteins were analyzed by western blotting. D: The expression of PD-L1 was detected by real-time fluorescence quantitative PCR.The error bar represents three separate experiments.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1/69c41a7ac515a48bf1e4dd50.jpg"},{"id":20187955,"identity":"2642ae65-dd82-485b-99f1-4a891edc4561","added_by":"auto","created_at":"2022-04-11 13:06:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":349289,"visible":true,"origin":"","legend":"\u003cp\u003eA-B: PD-L1's flow cytometry analysis in siIRF4 DS cells in relation to restriction with or without IFN-γ therapy. C: Flow cytometry was used to analyze GranzyB+ CD8+ T cell or IFN-γ+ CD8+ T cell frequencies. D: and CD4+T cell frequency or Treg(FOXP3) in the PBMC.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1/eeb64fa25230bfcaa37ed049.jpg"},{"id":20189126,"identity":"62071524-ac33-4d5b-ac60-5f262e7bf721","added_by":"auto","created_at":"2022-04-11 13:12:12","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1038503,"visible":true,"origin":"","legend":"","description":"","filename":"MOLECULARANDCELLULARBIOCHEMISTRY1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1_covered.pdf"},{"id":20187962,"identity":"6b04aeca-b5f5-4d91-8f1e-afc8989ff279","added_by":"auto","created_at":"2022-04-11 13:07:05","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":782393,"visible":true,"origin":"","legend":"","description":"","filename":"MOLECULARANDCELLULARBIOCHEMISTRY1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1_covered.pdf"},{"id":20189035,"identity":"1cea05ef-6ca0-4991-854e-9922230a34af","added_by":"auto","created_at":"2022-04-11 13:12:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53698,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1/e1ac08dddf7f4ee103bc0517.pdf"},{"id":20187958,"identity":"0dd873be-ef4a-4b2b-b18b-951b8005852d","added_by":"auto","created_at":"2022-04-11 13:06:59","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18782,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1530280/v1/863d631350129a88eab04893.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immunogenomic-based analysis of hierarchical clustering of diffuse large cell lymphoma","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1530280/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplemental Files section.\u003c/p\u003e\n"}],"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":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":"genomic profiling, immune subtypes, IRF4, prognosis, DLBCL","lastPublishedDoi":"10.21203/rs.3.rs-1530280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1530280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Diffuse Large B cell lymphoma (DLBCL) is one of the most usual type of adult lymphoma with heterogeneousness in histological morphology, prognosis and clinical indications. Prior to this, several studies were carried out to determine the DLBCL subtype based on the analysis of the genome profile.However, classification based on assessment of genes related to the immune system has limited clinical significance for DLBCL. We systematically explored the DLBCL gene expression dataset and provided publicly available clinical information on patients with GEO and TCGA. In this research, 928 DLBCL samples from the Cancer Genome Atlas (TCGA) were applied, we calculated 29 immune-related genomes' enrichment levels in each sample and stratified them into high immunity that was based on ssGSEA score (Immunity_H, n=323,68.7%), moderate (Immunity_M, n= 135, 28.7%) and low (Immunity_L, n= 12,2.6%). The ESTIMATE algorithm was used to calculate matrix score (range: -1,800.51,901.99), immunity score (range: -1,476.28,780.33), estimated score (range: -2,618.28,098.14), and tumor purity (range:0.216 0.976), All of them were significantly correlated with immune subtypes (Kruskal Wallis test, P \u003c 0.001). At the same time, the correlation of related genes was analyzed by immunohistochemistry staining. In addition, DLBCL cells were cultured in transfected and vitro with siRNA to verify correlation analysis and gene expression. Finally, human peripheral blood lymphocytes were incubated with DLBCL cells, and stained. Flow cytometry was applied to analyze genes' influence on immune function.By analysis, immune checkpoint and HLA gene expression levels were higher in the Immunity_H group (Kruskal Wallis test, P \u003c 0.05). The levels of Tfhs (follicular helper T cells), Monocytes, CD8+ T cells, M1 Macrophages, M2 Macrophages, CD4+ memory activated T cells, were the most excellent in Immunity_H, and total survival rate was higher in the Immunity_L. The GO term discovered in Immunity_H is connected with immunity. Through analysis, IRF4 (MUM1) was identified by us as immunotherapeutic target and a potential prognostic marker for DLBCL, which was made sure by using molecular biology experimentations. To conclude, immunosignature made a connection between DLBCL subtypes play a position in DLBCL prognostic stratification. Immunocharacteristics-related DLBCL subtypes' construction predicts expected patient results and supplies conceivable immunotherapy candida.","manuscriptTitle":"Immunogenomic-based analysis of hierarchical clustering of diffuse large cell lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-11 13:06:56","doi":"10.21203/rs.3.rs-1530280/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":"23c49255-83ee-4242-8135-f7554647436e","owner":[],"postedDate":"April 11th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-04-11T13:06:58+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-11 13:06:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1530280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1530280","identity":"rs-1530280","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}