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Immune microenvironment-related gene mapping predicts immunochemotherapy response and prognosis in diffuse large B-cell lymphoma

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Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin’s lymphoma (NHL). The R-CHOP immunochemotherapy regimen is the first-line treatment option for DLBCL patients and has greatly improved the prognosis of DLBCL, making it a curable disease. However, drug resistance or relapse is the main challenge for current DLBCL treatment. Studies have shown that the tumor microenvironment plays an important role in the onset, development, and responsiveness to drugs in DLBCL. Here, we used the CIBERSORT algorithm to resolve the composition of the immune microenvironment of 471 DLBCL patients from the GEO database. We found that activated memory CD4+ T cells and γδ T cells were significantly associated with immunochemotherapy response. Weighted gene co-expression networks (WGCNA) were constructed using differentially expressed genes from immunochemotherapy responders and non-responders. The module most associated with these two types of T cells was defined as hub module. Enrichment analysis of the hub module showed that baseline immune status was significantly stronger in responders than in non-responders. A protein–protein interaction (PPI) network was constructed for hub module to identify hub genes. After survival analysis, five prognosis-related genes (CD3G, CD3D, GNB4, FCHO2, GPR183) were identified and all these genes were significantly negatively associated with PD1. Using our own patient cohort, we validated the efficacy of CD3G and CD3D in predicting immunochemotherapy response. Our study showed that CD3G, CD3D, GNB4, FCHO2, and GPR183 are involved in the regulation of the immune microenvironment of DLBCL. They can be used as biomarkers for predicting immunochemotherapy response and potential therapeutic targets in DLBCL.

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Data availability

Publicly available datasets were analyzed in this study. This data can be found here: Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). The database URLs used in the article include CIBERSORTx (https://cibersortx.stanford.edu/index.php) and STRING (https://string-db.org/).

References

  1. Alizadeh A, Eisen M, Davis R, Ma C, Lossos I, Rosenwald A, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403(6769):503–11. https://doi.org/10.1038/35000501.

    Article  CAS  PubMed  Google Scholar 

  2. Rosenwald A, Wright G, Chan W, Connors J, Campo E, Fisher R, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med. 2002;346(25):1937–47. https://doi.org/10.1056/NEJMoa012914.

    Article  PubMed  Google Scholar 

  3. Hans CP. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood. 2004;103(1):275–82. https://doi.org/10.1182/blood-2003-05-1545.

    Article  CAS  PubMed  Google Scholar 

  4. Friedberg J. Relapsed/refractory diffuse large B-cell lymphoma. Hematol Am Soc Hematol Educ Progr. 2011;2011:498–505. https://doi.org/10.1182/asheducation-2011.1.498.

    Article  Google Scholar 

  5. Crump M, Neelapu SS, Farooq U, Van Den Neste E, Kuruvilla J, Westin J, et al. Outcomes in refractory diffuse large B-cell lymphoma: results from the international SCHOLAR-1 study. Blood. 2017;130(16):1800–8. https://doi.org/10.1182/blood-2017-03-769620.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sehn LH, Gascoyne RD. Diffuse large B-cell lymphoma: optimizing outcome in the context of clinical and biologic heterogeneity. Blood. 2015;125(1):22–32. https://doi.org/10.1182/blood-2014-05-577189.

    Article  CAS  PubMed  Google Scholar 

  7. Jiang S, Qin Y, Jiang H, Liu B, Shi J, Meng F, et al. Molecular profiling of Chinese R-CHOP treated DLBCL patients: identifying a high-risk subgroup. Int J Cancer. 2020. https://doi.org/10.1002/ijc.33049.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Solimando AG, Annese T, Tamma R, Ingravallo G, Maiorano E, Vacca A, et al. New insights into diffuse large B-cell lymphoma pathobiology. Cancers. 2020. https://doi.org/10.3390/cancers12071869.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Shain KH, Dalton WS, Tao J. The tumor microenvironment shapes hallmarks of mature B-cell malignancies. Oncogene. 2015;34(36):4673–82. https://doi.org/10.1038/onc.2014.403.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Leivonen SK, Pollari M, Bruck O, Pellinen T, Autio M, Karjalainen-Lindsberg ML, et al. T-cell inflamed tumor microenvironment predicts favorable prognosis in primary testicular lymphoma. Haematologica. 2019;104(2):338–46. https://doi.org/10.3324/haematol.2018.200105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Opinto G, Vegliante MC, Negri A, Skrypets T, Loseto G, Pileri SA, et al. The tumor microenvironment of DLBCL in the computational era. Front Oncol. 2020;10:351. https://doi.org/10.3389/fonc.2020.00351.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Coutinho R, Clear AJ, Mazzola E, Owen A, Greaves P, Wilson A, et al. Revisiting the immune microenvironment of diffuse large B-cell lymphoma using a tissue microarray and immunohistochemistry: robust semi-automated analysis reveals CD3 and FoxP3 as potential predictors of response to R-CHOP. Haematologica. 2015;100(3):363–9. https://doi.org/10.3324/haematol.2014.110189.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7. https://doi.org/10.1038/nmeth.3337.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559. https://doi.org/10.1186/1471-2105-9-559.

    Article  CAS  Google Scholar 

  15. Visco C, Li Y, Xu-Monette ZY, Miranda RN, Green TM, Li Y, et al. Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma: a report from the International DLBCL Rituximab-CHOP Consortium Program Study. Leukemia. 2012;26(9):2103–13. https://doi.org/10.1038/leu.2012.83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Xu-Monette ZY, Wu L, Visco C, Tai YC, Tzankov A, Liu WM, et al. Mutational profile and prognostic significance of TP53 in diffuse large B-cell lymphoma patients treated with R-CHOP: report from an International DLBCL Rituximab-CHOP Consortium Program Study. Blood. 2012;120(19):3986–96. https://doi.org/10.1182/blood-2012-05-433334.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cheson BD. The International Harmonization Project for response criteria in lymphoma clinical trials. Hematol Oncol Clin N Am. 2007;21(5):841–54. https://doi.org/10.1016/j.hoc.2007.06.011.

    Article  Google Scholar 

  18. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. https://doi.org/10.1093/nar/gkv007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gene Ontology C. The Gene Ontology (GO) project in 2006. Nucleic Acids Res. 2006;34(Database issue):D322–6. https://doi.org/10.1093/nar/gkj021.

    Article  CAS  Google Scholar 

  20. Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47(D1):D590–5. https://doi.org/10.1093/nar/gky962.

    Article  CAS  PubMed  Google Scholar 

  21. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. https://doi.org/10.1089/omi.2011.0118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362–8. https://doi.org/10.1093/nar/gkw937.

    Article  CAS  PubMed  Google Scholar 

  23. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. https://doi.org/10.1101/gr.1239303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cheson BD, Fisher RI, Barrington SF, Cavalli F, Schwartz LH, Zucca E, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol. 2014;32(27):3059–68. https://doi.org/10.1200/JCO.2013.54.8800.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Scott DW, Gascoyne RD. The tumour microenvironment in B cell lymphomas. Nat Rev Cancer. 2014;14(8):517–34. https://doi.org/10.1038/nrc3774.

    Article  CAS  PubMed  Google Scholar 

  26. Ansell S, Stenson M, Habermann T, Jelinek D, Witzig T. Cd4+ T-cell immune response to large B-cell non-Hodgkin’s lymphoma predicts patient outcome. J Clin Oncol. 2001;19(3):720–6. https://doi.org/10.1200/jco.2001.19.3.720.

    Article  CAS  PubMed  Google Scholar 

  27. Keane C, Gill D, Vari F, Cross D, Griffiths L, Gandhi M. CD4(+) tumor infiltrating lymphocytes are prognostic and independent of R-IPI in patients with DLBCL receiving R-CHOP chemo-immunotherapy. Am J Hematol. 2013;88(4):273–6. https://doi.org/10.1002/ajh.23398.

    Article  CAS  PubMed  Google Scholar 

  28. Xu Y, Kroft S, McKenna R, Aquino D. Prognostic significance of tumour-infiltrating T lymphocytes and T-cell subsets in de novo diffuse large B-cell lymphoma: a multiparameter flow cytometry study. Br J Haematol. 2001;112(4):945–9. https://doi.org/10.1046/j.1365-2141.2001.02649.x.

    Article  CAS  PubMed  Google Scholar 

  29. Zitvogel L, Apetoh L, Ghiringhelli F, Kroemer G. Immunological aspects of cancer chemotherapy. Nat Rev Immunol. 2008;8(1):59–73. https://doi.org/10.1038/nri2216.

    Article  CAS  PubMed  Google Scholar 

  30. Emens L. Chemoimmunotherapy. Cancer J. 2010;16(4):295–303. https://doi.org/10.1097/PPO.0b013e3181eb5066.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhao Y, Niu C, Cui J. Gamma-delta (gammadelta) T cells: friend or foe in cancer development? J Transl Med. 2018;16(1):3. https://doi.org/10.1186/s12967-017-1378-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wu D, Wu P, Qiu F, Wei Q, Huang J. Human γδT-cell subsets and their involvement in tumor immunity. Cell Mol Immunol. 2016;14(3):245–53. https://doi.org/10.1038/cmi.2016.55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Silva-Santos B, Mensurado S, Coffelt SB. γδ T cells: pleiotropic immune effectors with therapeutic potential in cancer. Nat Rev Cancer. 2019;19(7):392–404. https://doi.org/10.1038/s41568-019-0153-5.

    Article  CAS  PubMed  Google Scholar 

  34. Reboursiere E, Gac A-C, Garnier A, Salaun V, Reman O, Pham A-D, et al. Increased frequencies of circulating and tumor-resident Vδ1+ T cells in patients with diffuse large B-cell lymphoma. Leuk Lymphoma. 2017;59(1):187–95. https://doi.org/10.1080/10428194.2017.1321751.

    Article  CAS  PubMed  Google Scholar 

  35. Tokuyama H, Hagi T, Mattarollo SR, Morley J, Wang Q, So HF, et al. V gamma 9 V delta 2 T cell cytotoxicity against tumor cells is enhanced by monoclonal antibody drugs–rituximab and trastuzumab. Int J Cancer. 2008;122(11):2526–34. https://doi.org/10.1002/ijc.23365.

    Article  CAS  PubMed  Google Scholar 

  36. Mattarollo SR, Kenna T, Nieda M, Nicol AJ. Chemotherapy and zoledronate sensitize solid tumour cells to Vgamma9Vdelta2 T cell cytotoxicity. Cancer Immunol Immunother. 2007;56(8):1285–97. https://doi.org/10.1007/s00262-007-0279-2.

    Article  CAS  PubMed  Google Scholar 

  37. Rimsza LM. Loss of MHC class II gene and protein expression in diffuse large B-cell lymphoma is related to decreased tumor immunosurveillance and poor patient survival regardless of other prognostic factors: a follow-up study from the Leukemia and Lymphoma Molecular Profiling Project. Blood. 2004;103(11):4251–8. https://doi.org/10.1182/blood-2003-07-2365.

    Article  CAS  PubMed  Google Scholar 

  38. Challa-Malladi M, Lieu YK, Califano O, Holmes AB, Bhagat G, Murty VV, et al. Combined genetic inactivation of beta2-microglobulin and CD58 reveals frequent escape from immune recognition in diffuse large B cell lymphoma. Cancer Cell. 2011;20(6):728–40. https://doi.org/10.1016/j.ccr.2011.11.006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wilkinson ST, Vanpatten KA, Fernandez DR, Brunhoeber P, Garsha KE, Glinsmann-Gibson BJ, et al. Partial plasma cell differentiation as a mechanism of lost major histocompatibility complex class II expression in diffuse large B-cell lymphoma. Blood. 2012;119(6):1459–67. https://doi.org/10.1182/blood-2011-07-363820.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Morandi F, Yazdanifar M, Cocco C, Bertaina A, Airoldi I. Engineering the bridge between innate and adaptive immunity for cancer immunotherapy: focus on gammadelta T and NK cells. Cells. 2020. https://doi.org/10.3390/cells9081757.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Linderoth J, Eden P, Ehinger M, Valcich J, Jerkeman M, Bendahl PO, et al. Genes associated with the tumour microenvironment are differentially expressed in cured versus primary chemotherapy-refractory diffuse large B-cell lymphoma. Br J Haematol. 2008;141(4):423–32. https://doi.org/10.1111/j.1365-2141.2008.07037.x.

    Article  CAS  PubMed  Google Scholar 

  42. Oldham WM, Hamm HE. Heterotrimeric G protein activation by G-protein-coupled receptors. Nat Rev Mol Cell Biol. 2008;9(1):60–71. https://doi.org/10.1038/nrm2299.

    Article  CAS  PubMed  Google Scholar 

  43. Umar A, Kang H, Timmermans AM, Look MP, Meijer-van Gelder ME, den Bakker MA, et al. Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer. Mol Cell Proteomics. 2009;8(6):1278–94. https://doi.org/10.1074/mcp.M800493-MCP200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wang B, Li D, Rodriguez-Juarez R, Farfus A, Storozynsky Q, Malach M, et al. A suppressive role of guanine nucleotide-binding protein subunit beta-4 inhibited by DNA methylation in the growth of anti-estrogen resistant breast cancer cells. BMC Cancer. 2018;18(1):817. https://doi.org/10.1186/s12885-018-4711-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Block H, Stadtmann A, Riad D, Rossaint J, Sohlbach C, Germena G, et al. Gnb isoforms control a signaling pathway comprising Rac1, Plcβ2, and Plcβ3 leading to LFA-1 activation and neutrophil arrest in vivo. Blood. 2016;127(3):314–24. https://doi.org/10.1182/blood-2015-06-651034.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Smith A, Stanley P, Jones K, Svensson L, McDowall A, Hogg N. The role of the integrin LFA-1 in T-lymphocyte migration. Immunol Rev. 2007;218:135–46. https://doi.org/10.1111/j.1600-065X.2007.00537.x.

    Article  CAS  PubMed  Google Scholar 

  47. Liu Y, Gu J, Yang L, Tsai P, Guo Y, Xue K, et al. The adhesion molecule ICAM-1 in diffuse large B-cell lymphoma post-rituximab era: relationship with prognostic importance and rituximab resistance. Aging. 2020;13(1):181–93. https://doi.org/10.18632/aging.202180.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Birkenbach M, Josefsen K, Yalamanchili R, Lenoir G, Kieff E. Epstein-Barr virus-induced genes: first lymphocyte-specific G protein-coupled peptide receptors. J Virol. 1993;67(4):2209–20. https://doi.org/10.1128/jvi.67.4.2209-2220.1993.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Niss Arfelt K, Barington L, Benned-Jensen T, Kubale V, Kovalchuk AL, Daugvilaite V, et al. EBI2 overexpression in mice leads to B1 B-cell expansion and chronic lymphocytic leukemia-like B-cell malignancies. Blood. 2017;129(7):866–78. https://doi.org/10.1182/blood-2016-02-697185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hannedouche S, Zhang J, Yi T, Shen W, Nguyen D, Pereira JP, et al. Oxysterols direct immune cell migration via EBI2. Nature. 2011;475(7357):524–7. https://doi.org/10.1038/nature10280.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Liu C, Yang XV, Wu J, Kuei C, Mani NS, Zhang L, et al. Oxysterols direct B-cell migration through EBI2. Nature. 2011;475(7357):519–23. https://doi.org/10.1038/nature10226.

    Article  CAS  PubMed  Google Scholar 

  52. Yi T, Wang X, Kelly LM, An J, Xu Y, Sailer AW, et al. Oxysterol gradient generation by lymphoid stromal cells guides activated B cell movement during humoral responses. Immunity. 2012;37(3):535–48. https://doi.org/10.1016/j.immuni.2012.06.015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Pereira JP, Kelly LM, Xu Y, Cyster JG. EBI2 mediates B cell segregation between the outer and centre follicle. Nature. 2009;460(7259):1122–6. https://doi.org/10.1038/nature08226.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gatto D, Paus D, Basten A, Mackay CR, Brink R. Guidance of B cells by the orphan G protein-coupled receptor EBI2 shapes humoral immune responses. Immunity. 2009;31(2):259–69. https://doi.org/10.1016/j.immuni.2009.06.016.

    Article  CAS  PubMed  Google Scholar 

  55. Cattoretti G, Mandelbaum J, Lee N, Chaves AH, Mahler AM, Chadburn A, et al. Targeted disruption of the S1P2 sphingosine 1-phosphate receptor gene leads to diffuse large B-cell lymphoma formation. Cancer Res. 2009;69(22):8686–92. https://doi.org/10.1158/0008-5472.CAN-09-1110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Baptista AP, Gola A, Huang Y, Milanez-Almeida P, Torabi-Parizi P, Urban JF, et al. The chemoattractant receptor Ebi2 drives intranodal naive CD4+ T cell peripheralization to promote effective adaptive immunity. Immunity. 2019;50(5):1188–201. https://doi.org/10.1016/j.immuni.2019.04.001.

    Article  CAS  PubMed  Google Scholar 

  57. Clottu AS, Mathias A, Sailer AW, Schluep M, Seebach JD, Du Pasquier R, et al. EBI2 expression and function: robust in memory lymphocytes and increased by natalizumab in multiple sclerosis. Cell Rep. 2017;18(1):213–24. https://doi.org/10.1016/j.celrep.2016.12.006.

    Article  CAS  PubMed  Google Scholar 

  58. Li J, Lu E, Yi T, Cyster JG. EBI2 augments Tfh cell fate by promoting interaction with IL-2-quenching dendritic cells. Nature. 2016;533(7601):110–4. https://doi.org/10.1038/nature17947.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Willinger T. Metabolic control of innate lymphoid cell migration. Front Immunol. 2019. https://doi.org/10.3389/fimmu.2019.02010.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gatto D, Wood K, Caminschi I, Murphy-Durland D, Schofield P, Christ D, et al. The chemotactic receptor EBI2 regulates the homeostasis, localization and immunological function of splenic dendritic cells. Nat Immunol. 2013;14(5):446–53. https://doi.org/10.1038/ni.2555.

    Article  CAS  PubMed  Google Scholar 

  61. Conner S, Schmid S. Regulated portals of entry into the cell. Nature. 2003;422(6927):37–44. https://doi.org/10.1038/nature01451.

    Article  CAS  PubMed  Google Scholar 

  62. McMahon HT, Boucrot E. Molecular mechanism and physiological functions of clathrin-mediated endocytosis. Nat Rev Mol Cell Biol. 2011;12(8):517–33. https://doi.org/10.1038/nrm3151.

    Article  CAS  PubMed  Google Scholar 

  63. Mulkearns EE, Cooper JA. FCH domain only-2 organizes clathrin-coated structures and interacts with Disabled-2 for low-density lipoprotein receptor endocytosis. Mol Biol Cell. 2012;23(7):1330–42. https://doi.org/10.1091/mbc.E11-09-0812.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Henne WM, Boucrot E, Meinecke M, Evergren E, Vallis Y, Mittal R, et al. FCHo proteins are nucleators of clathrin-mediated endocytosis. Science. 2010;328(5983):1281–4. https://doi.org/10.1126/science.1188462.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Ned R, Swat W, Andrews N. Transferrin receptor 1 is differentially required in lymphocyte development. Blood. 2003;102(10):3711–8. https://doi.org/10.1182/blood-2003-04-1086.

    Article  CAS  PubMed  Google Scholar 

  66. Uezu A, Umeda K, Tsujita K, Suetsugu S, Takenawa T, Nakanishi H. Characterization of the EFC/F-BAR domain protein, FCHO2. Genes Cells. 2011;16(8):868–78. https://doi.org/10.1111/j.1365-2443.2011.01536.x.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Gene Expression Omnibus for providing detailed transcriptomic data and clinical information.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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  1. Wanjun Chen and Weijie Liang contributed equally to this work.

Authors and Affiliations

  1. Department of Blood Transfusion, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China

    Wanjun Chen, Chixiang Liu, Hongtian Chen, Piao Lv, Yuan Yao & Huayou Zhou

  2. Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China

    Weijie Liang

  3. Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China

    Yongjian He

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  1. Wanjun Chen
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Wanjun Chen and Weijie Liang. The first draft of the manuscript was written by Wanjun Chen, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Ethical approval for this study was obtained from Ethics Boards of Nanfang Hospital, Southern Medical University.

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12032_2021_1642_MOESM1_ESM.tif

Supplementary file1 (TIF 11255 kb) Supplementary Figure Progression-free survival curves of CD3G, CD3D, GNB4, FCHO2, and GPR183.

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Chen, W., Liang, W., He, Y. et al. Immune microenvironment-related gene mapping predicts immunochemotherapy response and prognosis in diffuse large B-cell lymphoma. Med Oncol 39, 44 (2022). https://doi.org/10.1007/s12032-021-01642-3

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