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2000
Volume 26, Issue 16
  • ISSN: 1389-2010
  • E-ISSN: 1873-4316

Abstract

Background

T-cell exhaustion (TEX) is one reason for immunotherapy resistance among cancers, but the specific mechanism and influencing factors of TEX in diffuse large B-cell lymphoma (DLBCL) are not fully understood. This study aimed to establish a TEX signature for predicting the prognosis of DLBCL and investigate the immune characteristics related to the TEX signature.

Methods

The gene expression data of DLBCL were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Prognostic TEX related genes were selected by Cox regression analysis for prognostic signature (TEX score) construction. The correlation of risk grouping with immune cell infiltration was analyzed by CIBERSORT and ssGSEA. Molecular mechanisms between high and low TEX score groups were explored by gene set enrichment analysis (GSEA).

Results

A total of 115 differentially expressed TEX-related genes were selected, and 12 were prognosis-related after Cox regression. Following Ninesignature genes, including TRIM6, BIRC3, CTSC, GBP3, IRF3, TRIM22, IFI30, TRIM25 and BAG4 were identified to construct a TEX score. The receiver operator characteristic curve curves suggested that the model presented high predictive precision. A nomogram was established, which also had good prediction performance in survival prognosis. The composition of immune cells in the two risk groups was significantly different. GSEA identified 33 hallmarks between two risk groups, which were associated with immune cells infiltration and inflammation.

Conclusion

The TEX score has prognosis-predicting value for DLBCL and might be a valuable biomarker to guide clinical decision‐making for patients with DLBCL.

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References

  1. ZhangL. ChenZ. ZuoW. ZhuX. LiY. LiuX. WeiX. Omacetaxine mepesuccinate induces apoptosis and cell cycle arrest, promotes cell differentiation, and reduces telomerase activity in diffuse large B-cell lymphoma cells.Mol. Med. Rep.20161343092310010.3892/mmr.2016.4899 26935769
    [Google Scholar]
  2. LiS. YoungK.H. MedeirosL.J. Diffuse large B-cell lymphoma.Pathology2018501748710.1016/j.pathol.2017.09.006 29167021
    [Google Scholar]
  3. ShaoY. LvX. YingS. GuoQ. Artificial intelligence-driven precision medicine: multi-omics and spatial multi-omics approaches in diffuse large B-cell lymphoma (DLBCL).Front. Biosci. (Landmark Ed.)2024291240410.31083/j.fbl2912404 39735973
    [Google Scholar]
  4. CarpioC. BouabdallahR. YsebaertL. SanchoJ.M. SallesG. CordobaR. PintoA. GhariboM. RascoD. PanizoC. Lopez-MartinJ.A. SantoroA. SalarA. DamianS. MartinA. VerhoefG. Van den NesteE. WangM. CoutoS. CarrancioS. WengA. WangX. SchmitzF. WeiX. HegeK. TrotterM.W.B. RisueñoA. BuchholzT.J. HagnerP.R. GandhiA.K. PourdehnadM. RibragV. Avadomide monotherapy in relapsed/refractory DLBCL: safety, efficacy, and a predictive gene classifier.Blood202013513996100710.1182/blood.2019002395 31977002
    [Google Scholar]
  5. ZhouY. LiG. Kaempferol protects cell damage in in vitro ischemia reperfusion model in rat neuronal PC12 cells.BioMed Res. Int.202020201246107910.1155/2020/2461079 32382538
    [Google Scholar]
  6. Susanibar-AdaniyaS. BartaS.K. 2021 Update on Diffuse large B cell lymphoma: A review of current data and potential applications on risk stratification and management.Am. J. Hematol.202196561762910.1002/ajh.26151 33661537
    [Google Scholar]
  7. HöpkenU.E. RehmA. Targeting the tumor microenvironment of leukemia and lymphoma.Trends Cancer20195635136410.1016/j.trecan.2019.05.001 31208697
    [Google Scholar]
  8. PasqualucciL. The genetic basis of diffuse large B-cell lymphoma.Curr. Opin. Hematol.201320433634410.1097/MOH.0b013e3283623d7f 23673341
    [Google Scholar]
  9. JenniferK.L. GrzegorzS.N. Diffuse large B-Cell lymphoma: from novel molecular classifications to tailored targeted therapies.J. Canc. Metast. Treat.2022811
    [Google Scholar]
  10. WallinJ.J. BendellJ.C. FunkeR. SznolM. KorskiK. JonesS. HernandezG. MierJ. HeX. HodiF.S. DenkerM. LevequeV. CañameroM. BabitskiG. KoeppenH. ZiaiJ. SharmaN. GaireF. ChenD.S. WaterkampD. HegdeP.S. McDermottD.F. Atezolizumab in combination with bevacizumab enhances antigen-specific T-cell migration in metastatic renal cell carcinoma.Nat. Commun.2016711262410.1038/ncomms12624 27571927
    [Google Scholar]
  11. LinZ. ChenX. LiZ. LuoY. FangZ. XuB. HanM. PD-1 antibody monotherapy for malignant melanoma: a systematic review and meta-analysis.PLoS One2016118e016048510.1371/journal.pone.0160485 27483468
    [Google Scholar]
  12. HuX. ChenZ. WangZ. XiaoQ. Cancer evolution: Special focus on the immune aspect of cancer.Semin. Cancer Biol.202286Pt 242043510.1016/j.semcancer.2022.05.006 35589072
    [Google Scholar]
  13. MenaresE. Gálvez-CancinoF. Cáceres-MorgadoP. GhoraniE. LópezE. DíazX. Saavedra-AlmarzaJ. FigueroaD.A. RoaE. QuezadaS.A. LladserA. Tissue-resident memory CD8+ T cells amplify anti-tumor immunity by triggering antigen spreading through dendritic cells.Nat. Commun.2019101440110.1038/s41467‑019‑12319‑x 31562311
    [Google Scholar]
  14. HanahanD. CoussensL.M. Accessories to the crime: functions of cells recruited to the tumor microenvironment.Cancer Cell201221330932210.1016/j.ccr.2012.02.022 22439926
    [Google Scholar]
  15. SarnaikA.A. HwuP. MuléJ.J. Pilon-ThomasS. Tumor-infiltrating lymphocytes: A new hope.Cancer Cell20244281315131810.1016/j.ccell.2024.06.015 39029463
    [Google Scholar]
  16. ThommenD.S. SchumacherT.N. T cell dysfunction in cancer.Cancer Cell201833454756210.1016/j.ccell.2018.03.012 29634943
    [Google Scholar]
  17. JiangY. LiY. ZhuB. T-cell exhaustion in the tumor microenvironment. Cell Dea.Dis.201566e179210.1038/cddis.2015.162 26086965
    [Google Scholar]
  18. KumarS. SinghS.K. RanaB. RanaA. Tumor-infiltrating CD8+ T cell antitumor efficacy and exhaustion: molecular insights.Drug Discov. Today202126495196710.1016/j.drudis.2021.01.002 33450394
    [Google Scholar]
  19. FrancoF. JaccardA. RomeroP. YuY.R. HoP.C. Metabolic and epigenetic regulation of T-cell exhaustion.Nat. Metab.20202101001101210.1038/s42255‑020‑00280‑9 32958939
    [Google Scholar]
  20. McLaneL.M. Abdel-HakeemM.S. WherryE.J. CD8 T cell exhaustion during chronic viral infection and cancer.Annu. Rev. Immunol.201937145749510.1146/annurev‑immunol‑041015‑055318 30676822
    [Google Scholar]
  21. BoyeroL. Sánchez-GastaldoA. AlonsoM. Noguera-UclésJ.F. Molina-PineloS. Bernabé-CaroR. Primary and acquired resistance to immunotherapy in lung cancer: unveiling the mechanisms underlying of immune checkpoint blockade therapy.Cancers (Basel)20201212372910.3390/cancers12123729 33322522
    [Google Scholar]
  22. GaoZ. FengY. XuJ. LiangJ. T-cell exhaustion in immune-mediated inflammatory diseases: New implications for immunotherapy.Front. Immunol.20221397739410.3389/fimmu.2022.977394 36211414
    [Google Scholar]
  23. ZhengS. MaJ. LiJ. PangX. MaM. MaZ. CuiW. Lower PTEN may be associated with CD8+ T cell exhaustion in diffuse large B-cell lymphoma.Hum. Immunol.2023841055156010.1016/j.humimm.2023.07.007 37481380
    [Google Scholar]
  24. DuboisS. ViaillyP.J. BohersE. BertrandP. RuminyP. MarchandV. MaingonnatC. MareschalS. PicquenotJ.M. PentherD. JaisJ.P. TessonB. PeyrouzeP. FigeacM. DesmotsF. FestT. HaiounC. LamyT. Copie-BergmanC. FabianiB. DelarueR. PeyradeF. AndréM. KettererN. LeroyK. SallesG. MolinaT.J. TillyH. JardinF. Biological and clinical relevance of associated genomic alterations in MYD88 L265P and non-L265P–mutated diffuse large B-cell lymphoma: analysis of 361 cases.Clin. Cancer Res.20172392232224410.1158/1078‑0432.CCR‑16‑1922 27923841
    [Google Scholar]
  25. DuboisS. TessonB. MareschalS. ViaillyP.J. BohersE. RuminyP. EtancelinP. PeyrouzeP. Copie-BergmanC. FabianiB. PetrellaT. JaisJ.P. HaiounC. SallesG. MolinaT.J. LeroyK. TillyH. JardinF. Refining diffuse large B-cell lymphoma subgroups using integrated analysis of molecular profiles.EBioMedicine201948586910.1016/j.ebiom.2019.09.034 31648986
    [Google Scholar]
  26. NørgaardC.H. JakobsenL.H. GentlesA.J. DybkærK. El-GalalyT.C. BødkerJ.S. SchmitzA. JohansenP. HeroldT. SpiekermannK. BrownJ.R. KlitgaardJ.L. JohnsenH.E. BøgstedM. Subtype assignment of CLL based on B-cell subset associated gene signatures from normal bone marrow – A proof of concept study.PLoS One2018133e019324910.1371/journal.pone.0193249 29513759
    [Google Scholar]
  27. DybkærK. BøgstedM. FalgreenS. BødkerJ.S. KjeldsenM.K. SchmitzA. BilgrauA.E. Xu-MonetteZ.Y. LiL. BergkvistK.S. LaursenM.B. Rodrigo-DomingoM. MarquesS.C. RasmussenS.B. NyegaardM. GaihedeM. MøllerM.B. SamworthR.J. ShahR.D. JohansenP. El-GalalyT.C. YoungK.H. JohnsenH.E. Diffuse large B-cell lymphoma classification system that associates normal B-cell subset phenotypes with prognosis.J. Clin. Oncol.201533121379138810.1200/JCO.2014.57.7080 25800755
    [Google Scholar]
  28. ShaC. BarransS. CareM.A. CunninghamD. ToozeR.M. JackA. WestheadD.R. Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas.Genome Med.2015716410.1186/s13073‑015‑0187‑6 26207141
    [Google Scholar]
  29. ZhangZ. ChenL. ChenH. ZhaoJ. LiK. SunJ. ZhouM. Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy.EBioMedicine20228310420710.1016/j.ebiom.2022.104207 35961204
    [Google Scholar]
  30. LiberzonA. BirgerC. ThorvaldsdóttirH. GhandiM. MesirovJ.P. TamayoP. The molecular signatures database (MSigDB) hallmark gene set collection.Cell Syst.20151641742510.1016/j.cels.2015.12.004 26771021
    [Google Scholar]
  31. RitchieM.E. PhipsonB. WuD. HuY. LawC.W. ShiW. SmythG.K. Limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucl. Aci. Res.2015437e4710.1093/nar/gkv007
    [Google Scholar]
  32. HaynesW. Benjamini–Hochberg Method.Encyclopedia of Systems Biology. DubitzkyW. WolkenhauerO. ChoK-H. YokotaH. New York, NYSpringer New York20137810.1007/978‑1‑4419‑9863‑7_1215
    [Google Scholar]
  33. MayakondaA. LinD.C. AssenovY. PlassC. KoefflerH.P. Maftools: efficient and comprehensive analysis of somatic variants in cancer.Genome Res.201828111747175610.1101/gr.239244.118 30341162
    [Google Scholar]
  34. WangP. WangY. HangB. ZouX. MaoJ.H. A novel gene expression-based prognostic scoring system to predict survival in gastric cancer.Oncotarget2016734553435535110.18632/oncotarget.10533 27419373
    [Google Scholar]
  35. FriedmanJ. HastieT. TibshiraniR. NarasimhanB. TayK. SimonN. QianJ. glmnet: Lasso and elastic-net regularized generalized linear models.CRAN R Reposit.202139559510.18637/jss.v039.i05
    [Google Scholar]
  36. HarrellF.E.Jr HarrellM.F.E.Jr HmiscD. Package ‘rms’. Vanderbilt University.CRAN R Reposit.20178229
    [Google Scholar]
  37. HuD. ZhouM. ZhuX. Deciphering immune-associated genes to predict survival in clear cell renal cell cancer.BioMed Res. Int.20192019250684310.1155/2019/2506843 31886185
    [Google Scholar]
  38. ChenB. KhodadoustM.S. LiuC.L. NewmanA.M. AlizadehA.A. Profiling tumor infiltrating immune cells with cibersort.Methods Mol. Biol.2018171124325910.1007/978‑1‑4939‑7493‑1_12 29344893
    [Google Scholar]
  39. YiM. NissleyD.V. McCormickF. StephensR.M. ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications.Sci. Rep.20201011025810.1038/s41598‑020‑66986‑8 32581224
    [Google Scholar]
  40. ZhaoC. ZhangZ. JingT. A novel signature of combing cuproptosis- with ferroptosis-related genes for prediction of prognosis, immunologic therapy responses and drug sensitivity in hepatocellular carcinoma.Front. Oncol.202212100099310.3389/fonc.2022.1000993 36249031
    [Google Scholar]
  41. ZhangX. ZhangS. YanX. ShanY. LiuL. ZhouJ. m6A regulator-mediated RNA methylation modification patterns are involved in immune microenvironment regulation of periodontitis.J. Cell. Mol. Med.20212573634364510.1111/jcmm.16469 33724691
    [Google Scholar]
  42. GeeleherP. CoxN. HuangR.S. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels.PLoS One201499e10746810.1371/journal.pone.0107468 25229481
    [Google Scholar]
  43. YuG. WangL.G. HanY. HeQ.Y. clusterProfiler: an R package for comparing biological themes among gene clusters.OMICS201216528428710.1089/omi.2011.0118 22455463
    [Google Scholar]
  44. AizazM. KhanA.S. KhanM. MusazadeE. YangG. Advancements in tumor-infiltrating lymphocytes: Historical insights, contemporary milestones, and future directions in oncology therapy.Crit. Rev. Oncol. Hematol.202420210447110.1016/j.critrevonc.2024.104471 39117163
    [Google Scholar]
  45. WangZ. WuX. Study and analysis of antitumor resistance mechanism of PD1/PD‐L1 immune checkpoint blocker.Cancer Med.20209218086812110.1002/cam4.3410 32875727
    [Google Scholar]
  46. ChowA. PericaK. KlebanoffC.A. WolchokJ.D. Clinical implications of T cell exhaustion for cancer immunotherapy.Nat. Rev. Clin. Oncol.2022191277579010.1038/s41571‑022‑00689‑z 36216928
    [Google Scholar]
  47. NgK.T.P. LoC.M. GuoD.Y. QiX. LiC.X. GengW. LiuX.B. LingC.C. MaY.Y. YeungW.H. ShaoY. PoonR.T.P. FanS.T. ManK. Identification of transmembrane protein 98 as a novel chemoresistance-conferring gene in hepatocellular carcinoma.Mol. Cancer Ther.20141351285129710.1158/1535‑7163.MCT‑13‑0806 24608572
    [Google Scholar]
  48. TanZ. SongL. WuW. ZhouY. ZhuJ. WuG. CaoL. SongJ. LiJ. ZhangW. TRIM14 promotes chemoresistance in gliomas by activating Wnt/β-catenin signaling via stabilizing Dvl2.Oncogene201837405403541510.1038/s41388‑018‑0344‑7 29867201
    [Google Scholar]
  49. MandellM.A. SahaB. ThompsonT.A. The tripartite nexus: autophagy, cancer, and tripartite motif-containing protein family members.Front. Pharmacol.20201130810.3389/fphar.2020.00308 32226386
    [Google Scholar]
  50. WengC. JinR. JinX. YangZ. HeC. ZhangQ. XuJ. LvB. Exploring the mechanisms, biomarkers, and therapeutic targets of TRIM family in gastrointestinal cancer.Drug Des. Devel. Ther.2024185615563910.2147/DDDT.S482340 39654601
    [Google Scholar]
  51. WangJ. YeJ. LiuR. ChenC. WangW. TRIM47 drives gastric cancer cell proliferation and invasion by regulating CYLD protein stability.Biol. Direct202419110610.1186/s13062‑024‑00555‑1 39516831
    [Google Scholar]
  52. ChiangD.C. YapB.K. TRIM25, TRIM28 and TRIM59 and their protein partners in cancer signaling crosstalk: Potential novel therapeutic targets for cancer.Curr. Issues Mol. Biol.20244610107451076110.3390/cimb46100638 39451518
    [Google Scholar]
  53. YaoY. ZhouS. YanY. FuK. XiaoS. The tripartite motif-containing 24 is a multifunctional player in human cancer.Cell Biosci.202414110310.1186/s13578‑024‑01289‑3 39160596
    [Google Scholar]
  54. Côrte-RealJ.V. BaldaufH.M. AbrantesJ. EstevesP.J. Evolution of the guanylate binding protein (GBP) genes: Emergence of GBP7 genes in primates and further acquisition of a unique GBP3 gene in simians.Mol. Immunol.2021132798110.1016/j.molimm.2021.01.025 33550067
    [Google Scholar]
  55. PraefckeG.J.K. Regulation of innate immune functions by guanylate-binding proteins.Int. J. Med. Microbiol.2018308123724510.1016/j.ijmm.2017.10.013 29174633
    [Google Scholar]
  56. JiangT. JinP. HuangG. LiS.C. The function of guanylate binding protein 3 (GBP3) in human cancers by pan-cancer bioinformatics.Math. Biosci. Eng.20232059511952910.3934/mbe.2023418 37161254
    [Google Scholar]
  57. JefferiesC.A. Regulating IRFs in IFN driven disease.Front. Immunol.20191032510.3389/fimmu.2019.00325 30984161
    [Google Scholar]
  58. TamuraT. YanaiH. SavitskyD. TaniguchiT. The IRF family transcription factors in immunity and oncogenesis.Annu. Rev. Immunol.200826153558410.1146/annurev.immunol.26.021607.090400 18303999
    [Google Scholar]
  59. XuH.G. ChenC. ChenL.Y. PanS. Pan‐cancer analysis identifies the IRF family as a biomarker for survival prognosis and immunotherapy.J. Cell. Mol. Med.2024283e1808410.1111/jcmm.18084 38130025
    [Google Scholar]
  60. WangW. ChapmanN.M. ZhangB. LiM. FanM. LaribeeR.N. ZaidiM.R. PfefferL.M. ChiH. WuZ.H. Upregulation of PD-L1 via HMGB1-activated IRF3 and NF-κB contributes to UV radiation-induced immune suppression.Cancer Res.201979112909292210.1158/0008‑5472.CAN‑18‑3134 30737234
    [Google Scholar]
  61. WangL. ZhuY. ZhangN. XianY. TangY. YeJ. RezaF. HeG. WenX. JiangX. The multiple roles of interferon regulatory factor family in health and disease.Signal Transduct. Target. Ther.20249128210.1038/s41392‑024‑01980‑4 39384770
    [Google Scholar]
  62. KuangT. ZhangL. ChaiD. ChenC. WangW. Construction of a T-cell exhaustion-related gene signature for predicting prognosis and immune response in hepatocellular carcinoma.Aging (Albany NY)202315125751577410.18632/aging.204830 37354485
    [Google Scholar]
  63. WuY. DuB. LinM. JiX. LvC. LaiJ. The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma.Sci. Rep.20231311341510.1038/s41598‑023‑40662‑z 37592010
    [Google Scholar]
  64. BejaranoL. JordāoM.J.C. JoyceJ.A. Therapeutic targeting of the tumor microenvironment.Cancer Discov.202111493395910.1158/2159‑8290.CD‑20‑1808 33811125
    [Google Scholar]
  65. DesaiS.A. PatelV.P. BhosleK.P. NagareS.D. ThombareK.C. The tumor microenvironment: Shaping cancer progression and treatment response.J. Chemother.2024371154410.1080/1120009X.2023.2300224 38179655
    [Google Scholar]
  66. ZhouJ. DingT. PanW. ZhuL. LiL. ZhengL. Increased intratumoral regulatory T cells are related to intratumoral macrophages and poor prognosis in hepatocellular carcinoma patients.Int. J. Cancer200912571640164810.1002/ijc.24556 19569243
    [Google Scholar]
  67. BingleL. BrownN.J. LewisC.E. The role of tumour‐associated macrophages in tumour progression: implications for new anticancer therapies.J. Pathol.2002196325426510.1002/path.1027 11857487
    [Google Scholar]
  68. LadányiA. KissJ. MohosA. SomlaiB. LiszkayG. GildeK. FejősZ. GaudiI. DobosJ. TímárJ. Prognostic impact of B-cell density in cutaneous melanoma.Cancer Immunol. Immunother.201160121729173810.1007/s00262‑011‑1071‑x 21779876
    [Google Scholar]
  69. SchalperK.A. BrownJ. Carvajal-HausdorfD. McLaughlinJ. VelchetiV. SyrigosK.N. HerbstR.S. RimmD.L. Objective measurement and clinical significance of TILs in non-small cell lung cancer.J. Natl. Cancer Inst.20151073dju43510.1093/jnci/dju435 25650315
    [Google Scholar]
  70. JuX. ZhangH. ZhouZ. ChenM. WangQ. Tumor-associated macrophages induce PD-L1 expression in gastric cancer cells through IL-6 and TNF-ɑ signaling.Exp. Cell Res.2020396211231510.1016/j.yexcr.2020.112315 33031808
    [Google Scholar]
  71. MaQ. HaoS. HongW. TergaonkarV. SethiG. TianY. DuanC. Versatile function of NF-ĸB in inflammation and cancer.Exp. Hematol. Oncol.20241316810.1186/s40164‑024‑00529‑z 39014491
    [Google Scholar]
  72. KhanA. ZhangY. MaN. ShiJ. HouY. NF-κB role on tumor proliferation, migration, invasion and immune escape.Cancer Gene Ther.202431111599161010.1038/s41417‑024‑00811‑6 39033218
    [Google Scholar]
  73. QianJ. WangC. WangB. YangJ. WangY. LuoF. XuJ. ZhaoC. LiuR. ChuY. The IFN-γ/PD-L1 axis between T cells and tumor microenvironment: Hints for glioma anti-PD-1/PD-L1 therapy.J. Neuroinflammat.201815129010.1186/s12974‑018‑1330‑2 30333036
    [Google Scholar]
  74. XiangZ. ZhouZ. SongS. LiJ. JiJ. YanR. WangJ. CaiW. HuW. ZangL. ZhuZ. ZhangZ. LiM. YuY. Dexamethasone suppresses immune evasion by inducing GR/STAT3 mediated downregulation of PD-L1 and IDO1 pathways.Oncogene202140315002501210.1038/s41388‑021‑01897‑0 34175886
    [Google Scholar]
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