Skip to content
2000
image of Mediating Effects of Plasma Metabolites in Inflammatory Protein- Lymphoma Causality: A Mendelian Randomization Study

Abstract

Introduction

Diffuse large B-cell lymphoma (DLBCL) pathogenesis is poorly understood, with limited causal evidence linking circulating inflammatory proteins (CIPs) and metabolites to disease risk. Observational studies face challenges from confounding and reverse causation, while existing Mendelian Randomization (MR) analyses lack bidirectional designs and multi-omics integration.

Methods

A bidirectional two-sample MR design was applied using inverse-variance weighting (IVW). Genetic instruments for 91 CIPs derived from Olink proteomic data (14,824 participants). DLBCL genetic associations (1,050 cases; 314,193 controls) were obtained from FinnGen (R10 release). Data for 1,091 blood metabolites and 309 metabolite ratios were sourced from the GWAS Catalog.

Results

Ten CIPs exhibited causal effects on DLBCL. Risk-increasing proteins included: IL-10 (OR=1.46, 95%CI=1.05-2.03), TSLP (1.37,1.01-1.84), IL-17C (1.34,1.05-1.72), NRTN (1.30,1.02-1.66), OPG (1.29,1.01-1.66), and MCP1 (1.26,1.04-1.52). Protective proteins included: CD40 (0.82,0.67-1.00), CXCL9 (0.78,0.61-0.98), CD5 (0.77,0.61-0.97), and MCP3 (0.76,0.58-0.99). Reverse causation was absent for 7 proteins. Mediation analysis revealed 17.2% (=0.048) of CD5’s protective effect was mediated by 1-methylhistidine.

Discussion

These findings establish CIPs as causal factors in DLBCL pathogenesis and identify metabolite-mediated pathways as novel mechanistic links. The bidirectional design and multi-omics integration overcome key limitations of prior research, though statistical power for some mediation tests was limited by metabolite GWAS sample sizes.

Conclusion

Plasma inflammatory proteins causally influence DLBCL risk, partially mediated by metabolites. This underscores metabolite pathways as potential targets for therapeutic intervention.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673382735250904071151
2025-10-02
2025-11-05

  • From This Site

    /content/journals/cmc/10.2174/0109298673382735250904071151
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Li S. Young K.H. Medeiros L.J. Diffuse large B-cell lymphoma. Pathology 2018 50 1 74 87 10.1016/j.pathol.2017.09.006 29167021
    [Google Scholar]
  2. Reddy A. Zhang J. Davis N.S. Moffitt A.B. Love C.L. Waldrop A. Leppa S. Pasanen A. Meriranta L. Karjalainen-Lindsberg M.L. Nørgaard P. Pedersen M. Gang A.O. Høgdall E. Heavican T.B. Lone W. Iqbal J. Qin Q. Li G. Kim S.Y. Healy J. Richards K.L. Fedoriw Y. Bernal-Mizrachi L. Koff J.L. Staton A.D. Flowers C.R. Paltiel O. Goldschmidt N. Calaminici M. Clear A. Gribben J. Nguyen E. Czader M.B. Ondrejka S.L. Collie A. Hsi E.D. Tse E. Au-Yeung R.K.H. Kwong Y.L. Srivastava G. Choi W.W.L. Evens A.M. Pilichowska M. Sengar M. Reddy N. Li S. Chadburn A. Gordon L.I. Jaffe E.S. Levy S. Rempel R. Tzeng T. Happ L.E. Dave T. Rajagopalan D. Datta J. Dunson D.B. Dave S.S. Genetic and functional drivers of diffuse large b cell lymphoma. Cell 2017 171 2 481 494.e15 10.1016/j.cell.2017.09.027 28985567
    [Google Scholar]
  3. Bakhshi T.J. Georgel P.T. Genetic and epigenetic determinants of diffuse large B-cell lymphoma. Blood Cancer J. 2020 10 12 123 10.1038/s41408‑020‑00389‑w 33277464
    [Google Scholar]
  4. Sehn L.H. Gascoyne R.D. Diffuse large B-cell lymphoma: Optimizing outcome in the context of clinical and biologic heterogeneity. Blood 2015 125 1 22 32 10.1182/blood‑2014‑05‑577189 25499448
    [Google Scholar]
  5. Siegel R.L. Giaquinto A.N. Jemal A. Cancer statistics, 2024. CA Cancer J. Clin. 2024 74 1 12 49 10.3322/caac.21820 38230766
    [Google Scholar]
  6. Autio M. Leivonen S.-k Brück O. Mustjoki S. Karjalainen-Lindsberg M.-L. Beiske K. Holte H. Pellinen T. Leppä S. Leppä S. Immune cell constitution in the tumor microenvironment predicts the outcome in diffuse large B-cell lymphoma. Haematologica 2020 106 3 718 729 10.3324/haematol.2019.243626 32079690
    [Google Scholar]
  7. Azzaoui I. Uhel F. Rossille D. Pangault C. Dulong J. Le Priol J. Lamy T. Houot R. Le Gouill S. Cartron G. Godmer P. Bouabdallah K. Milpied N. Damaj G. Tarte K. Fest T. Roussel M. T-cell defect in diffuse large B-cell lymphomas involves expansion of myeloid-derived suppressor cells. Blood 2016 128 8 1081 1092 10.1182/blood‑2015‑08‑662783 27338100
    [Google Scholar]
  8. Chen W. Liang W. He Y. Liu C. Chen H. Lv P. Yao Y. Zhou H. Immune microenvironment-related gene mapping predicts immunochemotherapy response and prognosis in diffuse large B-cell lymphoma. Med. Oncol. 2022 39 4 44 10.1007/s12032‑021‑01642‑3 35092504
    [Google Scholar]
  9. Pedersen L.M. Jürgensen G.W. Johnsen H.E. Serum levels of inflammatory cytokines at diagnosis correlate to the bcl-6 and CD10 defined germinal centre (GC) phenotype and bcl-2 expression in patients with diffuse large B-cell lymphoma. Br. J. Haematol. 2005 128 6 813 819 10.1111/j.1365‑2141.2005.05393.x 15755285
    [Google Scholar]
  10. Zhao H. Wu L. Yan G. Chen Y. Zhou M. Wu Y. Li Y. Inflammation and tumor progression: Signaling pathways and targeted intervention. Signal Transduct. Target. Ther. 2021 6 1 263 10.1038/s41392‑021‑00658‑5 34248142
    [Google Scholar]
  11. Arffman M. Meriranta L. Autio M. Holte H. Jørgensen J. Brown P. Jyrkkiö S. Jerkeman M. Drott K. Fluge Ø. Björkholm M. Karjalainen-Lindsberg M.L. Beiske K. Pedersen M.Ø. Leivonen S.K. Leppä S. Inflammatory and subtype-dependent serum protein signatures predict survival beyond the ctDNA in aggressive B cell lymphomas. Med (N. Y.) 2024 5 6 583 602.e5 10.1016/j.medj.2024.03.007 38579729
    [Google Scholar]
  12. Wright G.W. Huang D.W. Phelan J.D. Coulibaly Z.A. Roulland S. Young R.M. Wang J.Q. Schmitz R. Morin R.D. Tang J. Jiang A. Bagaev A. Plotnikova O. Kotlov N. Johnson C.A. Wilson W.H. Scott D.W. Staudt L.M. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell 2020 37 4 551 568.e14 10.1016/j.ccell.2020.03.015 32289277
    [Google Scholar]
  13. Chapuy B. Stewart C. Dunford A.J. Kim J. Kamburov A. Redd R.A. Lawrence M.S. Roemer M.G.M. Li A.J. Ziepert M. Staiger A.M. Wala J.A. Ducar M.D. Leshchiner I. Rheinbay E. Taylor-Weiner A. Coughlin C.A. Hess J.M. Pedamallu C.S. Livitz D. Rosebrock D. Rosenberg M. Tracy A.A. Horn H. van Hummelen P. Feldman A.L. Link B.K. Novak A.J. Cerhan J.R. Habermann T.M. Siebert R. Rosenwald A. Thorner A.R. Meyerson M.L. Golub T.R. Beroukhim R. Wulf G.G. Ott G. Rodig S.J. Monti S. Neuberg D.S. Loeffler M. Pfreundschuh M. Trümper L. Getz G. Shipp M.A. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat. Med. 2018 24 5 679 690 10.1038/s41591‑018‑0016‑8 29713087
    [Google Scholar]
  14. McHugh J. B cell-derived IL-6 promotes disease. Nat. Rev. Rheumatol. 2017 13 11 633 10.1038/nrrheum.2017.163 28959044
    [Google Scholar]
  15. Rabinovich E. Pradhan K. Sica R.A. Bachier-Rodriguez L. Mantzaris I. Kornblum N. Shastri A. Gritsman K. Goldfinger M. Verma A. Braunschweig I. Elevated LDH greater than 400 U/L portends poorer overall survival in diffuse large B-cell lymphoma patients treated with CD19 CAR-T cell therapy in a real world multi-ethnic cohort. Exp. Hematol. Oncol. 2021 10 1 55 10.1186/s40164‑021‑00248‑9 34886908
    [Google Scholar]
  16. Peixi W. Huifang S. Chunyu L. Association of creatinine level with neurodegenerative disorders: A prospective cohort study and Mendelian randomization analysis. Neurol Sci. 2025 46 5123 5132 10.1007/s10072‑025‑08361‑x
    [Google Scholar]
  17. Bowden J. Holmes M.V. Meta-analysis and Mendelian Randomization: A review. Res. Synth. Methods 2019 10 4 486 496 10.1002/jrsm.1346 30861319
    [Google Scholar]
  18. Slob E.A.W. Burgess S. A comparison of robust Mendelian Randomization methods using summary data. Genet. Epidemiol. 2020 44 4 313 329 10.1002/gepi.22295 32249995
    [Google Scholar]
  19. Morrison J. Knoblauch N. Marcus J.H. Stephens M. He X. Mendelian Randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat. Genet. 2020 52 7 740 747 10.1038/s41588‑020‑0631‑4 32451458
    [Google Scholar]
  20. Wang M.T.M. Bolland M.J. Grey A. Reporting of limitations of observational research. JAMA Intern. Med. 2015 175 9 1571 1572 10.1001/jamainternmed.2015.2147 26053740
    [Google Scholar]
  21. Richmond R.C. Smith G.D. Mendelian Randomization: Concepts and Scope. Cold Spring Harb. Perspect. Med. 2022 12 1 a040501 10.1101/cshperspect.a040501 34426474
    [Google Scholar]
  22. Qiu S. Cai Y. Yao H. Lin C. Xie Y. Tang S. Zhang A. Small molecule metabolites: Discovery of biomarkers and therapeutic targets. Signal Transduct. Target. Ther. 2023 8 1 132 10.1038/s41392‑023‑01399‑3 36941259
    [Google Scholar]
  23. Smelik M. Zhao Y. Li X. Loscalzo J. Sysoev O. Mahmud F. Aly D.M. Benson M. An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases. Sci. Rep. 2024 14 1 12710 10.1038/s41598‑024‑63399‑9 38830935
    [Google Scholar]
  24. Kleinstern G. Camp N.J. Berndt S.I. Birmann B.M. Nieters A. Bracci P.M. McKay J.D. Ghesquières H. Lan Q. Hjalgrim H. Benavente Y. Monnereau A. Wang S.S. Zhang Y. Purdue M.P. Zeleniuch-Jacquotte A. Giles G.G. Vermeulen R. Cocco P. Albanes D. Teras L.R. Brooks-Wilson A.R. Vajdic C.M. Kane E. Caporaso N.E. Smedby K.E. Salles G. Vijai J. Chanock S.J. Skibola C.F. Rothman N. Slager S.L. Cerhan J.R. Lipid trait variants and the risk of non-hodgkin lymphoma subtypes: A mendelian randomization study. Cancer Epidemiol. Biomarkers Prev. 2020 29 5 1074 1078 10.1158/1055‑9965.EPI‑19‑0803 32108027
    [Google Scholar]
  25. Barberini L. Noto A. Fattuoni C. Satta G. Zucca M. Cabras M.G. Mura E. Cocco P. The metabolomic profile of lymphoma subtypes: A pilot study. Molecules 2019 24 13 2367 10.3390/molecules24132367 31248049
    [Google Scholar]
  26. Zhao J.H. Stacey D. Eriksson N. Macdonald-Dunlop E. Hedman Å.K. Kalnapenkis A. Enroth S. Cozzetto D. Digby-Bell J. Marten J. Folkersen L. Herder C. Jonsson L. Bergen S.E. Gieger C. Needham E.J. Surendran P. Metspalu A. Milani L. Mägi R. Nelis M. Hudjašov G. Paul D.S. Polasek O. Thorand B. Grallert H. Roden M. Võsa U. Esko T. Hayward C. Johansson Å. Gyllensten U. Powell N. Hansson O. Mattsson-Carlgren N. Joshi P.K. Danesh J. Padyukov L. Klareskog L. Landén M. Wilson J.F. Siegbahn A. Wallentin L. Mälarstig A. Butterworth A.S. Peters J.E. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat. Immunol. 2023 24 9 1540 1551 10.1038/s41590‑023‑01588‑w 37563310
    [Google Scholar]
  27. Chen Y. Lu T. Pettersson-Kymmer U. Stewart I.D. Butler-Laporte G. Nakanishi T. Cerani A. Liang K.Y.H. Yoshiji S. Willett J.D.S. Su C.Y. Raina P. Greenwood C.M.T. Farjoun Y. Forgetta V. Langenberg C. Zhou S. Ohlsson C. Richards J.B. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat. Genet. 2023 55 1 44 53 10.1038/s41588‑022‑01270‑1 36635386
    [Google Scholar]
  28. Lu C. Chen Q. Tao H. Xu L. Li J. Wang C. Yu L. The causal effect of inflammatory bowel disease on diffuse large B-cell lymphoma: Two-sample Mendelian Randomization study. Front. Immunol. 2023 14 1171446 10.3389/fimmu.2023.1171446 37593734
    [Google Scholar]
  29. Qiu R. Su Y. Pan L. Fan K. Sun Z. Liang Y. Lin X. Zhang Y. Causal relationship between immune mediators and Parkinson’s disease: A Mendelian randomization analysis. Sci. Rep. 2025 15 1 24884 10.1038/s41598‑025‑11198‑1 40640367
    [Google Scholar]
  30. Jiang P. Yu F. Zhou X. Shi H. He Q. Song X. Dissecting causal links between gut microbiota, inflammatory cytokines, and DLBCL: A Mendelian Randomization study. Blood Adv. 2024 8 9 2268 2278 10.1182/bloodadvances.2023012246 38507680
    [Google Scholar]
  31. Palmer T.M. Lawlor D.A. Harbord R.M. Sheehan N.A. Tobias J.H. Timpson N.J. Smith G.D. Sterne J.A.C. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat. Methods Med. Res. 2012 21 3 223 242 10.1177/0962280210394459 21216802
    [Google Scholar]
  32. Skrivankova V.W. Richmond R.C. Woolf B.A.R. Yarmolinsky J. Davies N.M. Swanson S.A. VanderWeele T.J. Higgins J.P.T. Timpson N.J. Dimou N. Langenberg C. Golub R.M. Loder E.W. Gallo V. Tybjaerg-Hansen A. Smith G.D. Egger M. Richards J.B. Strengthening the reporting of observational studies in epidemiology using Mendelian Randomization. JAMA 2021 326 16 1614 1621 10.1001/jama.2021.18236 34698778
    [Google Scholar]
  33. Carter A.R. Sanderson E. Hammerton G. Richmond R.C. Smith G.D. Heron J. Taylor A.E. Davies N.M. Howe L.D. Mendelian Randomisation for mediation analysis: Current methods and challenges for implementation. Eur. J. Epidemiol. 2021 36 5 465 478 10.1007/s10654‑021‑00757‑1 33961203
    [Google Scholar]
  34. Sekula P. Del Greco M F. Pattaro C. Köttgen A. Mendelian Randomization as an approach to assess causality using observational data. J. Am. Soc. Nephrol. 2016 27 11 3253 3265 10.1681/ASN.2016010098 27486138
    [Google Scholar]
  35. Zheng J. Baird D. Borges M.C. Bowden J. Hemani G. Haycock P. Evans D.M. Smith G.D. Recent developments in Mendelian Randomization studies. Curr. Epidemiol. Rep. 2017 4 4 330 345 10.1007/s40471‑017‑0128‑6 29226067
    [Google Scholar]
  36. Burgess S. Davies N.M. Thompson S.G. Bias due to participant overlap in two-sample Mendelian Randomization. Genet. Epidemiol. 2016 40 7 597 608 10.1002/gepi.21998 27625185
    [Google Scholar]
  37. Burgess S. Thompson S.G. Interpreting findings from Mendelian Randomization using the MR-Egger method. Eur. J. Epidemiol. 2017 32 5 377 389 10.1007/s10654‑017‑0255‑x 28527048
    [Google Scholar]
  38. Wang Z. Song B. Wang J. Wang X. Tang B. Alzheimer’s disease and its family history reduce the risk of non-Hodgkin’s lymphoma: A Mendelian Randomization study. Alzheimers Dement. 2024 20 10 7423 7425 10.1002/alz.14205 39192692
    [Google Scholar]
  39. Lyu C. Wang Y. Xu R. Mendelian Randomization analysis reveals causal effects of inflammatory bowel disease and autoimmune hyperthyroidism on diffuse large B-cell lymphoma risk. Sci. Rep. 2024 14 1 29163 10.1038/s41598‑024‑79791‑4 39587169
    [Google Scholar]
  40. Ochoa L.B. Rijnhart J.J.M. Penninx B.W. Wardenaar K.J. Twisk J.W.R. Heymans M.W. Performance of methods to conduct mediation analysis with time-to-event outcomes. Stat. Neerl. 2020 74 1 72 91 10.1111/stan.12191
    [Google Scholar]
  41. Zheng X. Yin J. Zhao L. Qian Y. Xu J. Mediation analysis of gut microbiota and plasma metabolites in asthma pathogenesis using Mendelian Randomization. J. Asthma 2025 62 8 1 13 10.1080/02770903.2025.2478504 40071715
    [Google Scholar]
  42. Nizzoli G. Larghi P. Paroni M. Crosti M.C. Moro M. Neddermann P. Caprioli F. Pagani M. De Francesco R. Abrignani S. Geginat J. IL-10 promotes homeostatic proliferation of human CD8+ memory T cells and, when produced by CD1c+ DCs, shapes naive CD8+ T-cell priming. Eur. J. Immunol. 2016 46 7 1622 1632 10.1002/eji.201546136 27129615
    [Google Scholar]
  43. Mittal S.K. Roche P.A. Suppression of antigen presentation by IL-10. Curr. Opin. Immunol. 2015 34 22 27 10.1016/j.coi.2014.12.009 25597442
    [Google Scholar]
  44. Taylor J.G. Gribben J.G. Microenvironment abnormalities and lymphomagenesis: Immunological aspects. Semin. Cancer Biol. 2015 34 36 45 10.1016/j.semcancer.2015.07.004 26232774
    [Google Scholar]
  45. Garaud S. Taher T.E. Debant M. Burgos M. Melayah S. Berthou C. Parikh K. Pers J.O. Luque-Paz D. Chiocchia G. Peppelenbosch M. Isenberg D.A. Youinou P. Mignen O. Renaudineau Y. Mageed R.A. CD5 expression promotes IL-10 production through activation of the MAPK/Erk pathway and upregulation of TRPC1 channels in B lymphocytes. Cell. Mol. Immunol. 2018 15 2 158 170 10.1038/cmi.2016.42 27499044
    [Google Scholar]
  46. Conroy S.M. Maskarinec G. Morimoto Y. Franke A.A. Cooney R.V. Wilkens L.R. Goodman M.T. Hernadez B.Y. Le Marchand L. Henderson B.E. Kolonel L.N. Non-hodgkin lymphoma and circulating markers of inflammation and adiposity in a nested case-control study: The multiethnic cohort. Cancer Epidemiol. Biomarkers Prev. 2013 22 3 337 347 10.1158/1055‑9965.EPI‑12‑0947 23300021
    [Google Scholar]
  47. Fei F. Zheng M. Xu Z. Sun R. Chen X. Cao B. Li J. Plasma metabolites forecast occurrence and prognosis for patients with diffuse large B-cell lymphoma. Front. Oncol. 2022 12 894891 10.3389/fonc.2022.894891 35734601
    [Google Scholar]
  48. Yu J. Fu L. Zhang Z. Ding L. Hong L. Gao F. Jin J. Feng W. Fu J. Hong P. Xu C. Causal relationships between circulating inflammatory cytokines and diffuse large B cell lymphoma: A bidirectional Mendelian Randomization study. Clin. Exp. Med. 2023 23 8 4585 4595 10.1007/s10238‑023‑01221‑y 37910257
    [Google Scholar]
  49. Loong F. Chan A.C.L. Ho B.C.S. Chau Y.P. Lee H.Y. Cheuk W. Yuen W.K. Ng W.S. Cheung H.L. Chan J.K.C. Diffuse large B-cell lymphoma associated with chronic inflammation as an incidental finding and new clinical scenarios. Mod. Pathol. 2010 23 4 493 501 10.1038/modpathol.2009.168 20062008
    [Google Scholar]
  50. Fahrmann J.F. Saini N.Y. Chia-Chi C. Irajizad E. Strati P. Nair R. Fayad L.E. Ahmed S. Lee H.J. Iyer S. Steiner R. Vykoukal J. Wu R. Dennison J.B. Nastoupil L. Jain P. Wang M. Green M. Westin J. Blumenberg V. Davila M. Champlin R. Shpall E.J. Kebriaei P. Flowers C.R. Jain M. Jenq R. Stein-Thoeringer C.K. Subklewe M. Neelapu S.S. Hanash S. A polyamine-centric, blood-based metabolite panel predictive of poor response to CAR-T cell therapy in large B cell lymphoma. Cell Rep. Med. 2022 3 11 100720 10.1016/j.xcrm.2022.100720 36384092
    [Google Scholar]
  51. Hartert K.T. Wenzl K. Krull J.E. Manske M. Sarangi V. Asmann Y. Larson M.C. Maurer M.J. Slager S. Macon W.R. King R.L. Feldman A.L. Gandhi A.K. Link B.K. Habermann T.M. Yang Z.Z. Ansell S.M. Cerhan J.R. Witzig T.E. Nowakowski G.S. Novak A.J. Targeting of inflammatory pathways with R2CHOP in high-risk DLBCL. Leukemia 2021 35 2 522 533 10.1038/s41375‑020‑0766‑4 32139889
    [Google Scholar]
  52. Bouras E. Karhunen V. Gill D. Huang J. Haycock P.C. Gunter M.J. Johansson M. Brennan P. Key T. Lewis S.J. Martin R.M. Murphy N. Platz E.A. Travis R. Yarmolinsky J. Zuber V. Martin P. Katsoulis M. Freisling H. Nøst T.H. Schulze M.B. Dossus L. Hung R.J. Amos C.I. Ahola-Olli A. Palaniswamy S. Männikkö M. Auvinen J. Herzig K.H. Keinänen-Kiukaanniemi S. Lehtimäki T. Salomaa V. Raitakari O. Salmi M. Jalkanen S. Cruk Caps Pegasus Jarvelin M-R. Dehghan A. Tsilidis K.K. Circulating inflammatory cytokines and risk of five cancers: A Mendelian Randomization analysis. BMC Med. 2022 20 1 3 10.1186/s12916‑021‑02193‑0 35012533
    [Google Scholar]
  53. Sanderson E. Glymour M.M. Holmes M.V. Kang H. Morrison J. Munafò M.R. Palmer T. Schooling C.M. Wallace C. Zhao Q. Smith G.D. Mendelian Randomization. Nat. Rev. Methods Primers 2022 2 1 6 10.1038/s43586‑021‑00092‑5 37325194
    [Google Scholar]
  54. Calvo J. Places L. Espinosa G. Padilla O. Vilà J.M. Villamor N. Ingelmo M. Gallart T. Vives J. Font J. Lozano F. Identification of a natural soluble form of human CD5. Tissue Antigens 1999 54 2 128 137 10.1034/j.1399‑0039.1999.540203.x 10488739
    [Google Scholar]
  55. Felekkis K. Papaneophytou C. The circulating biomarkers league: Combining mirnas with cell-free DNAs and proteins. Int. J. Mol. Sci. 2024 25 6 3403 10.3390/ijms25063403 38542382
    [Google Scholar]
  56. Su Y. Peng Z. Wang Y. Yang S. Xu X. Liu W. Bao Q. Jiang C. Qian K. Fan X. Metabolites in serum small extracellular vesicles instead of small extracellular vesicles-depleted serum have better diagnostic value for cancers at early stage. Small 2025 21 7 2411871 10.1002/smll.202411871 39757515
    [Google Scholar]
  57. Pomyen Y. Budhu A. Chaisaingmongkol J. Forgues M. Dang H. Ruchirawat M. Mahidol C. Wang X.W. Pupacdi B. Rabibhadana S. Phonphutkul K. Lertprasertsuke N. Chotirosniramit A. Auewarakul C.U. Ungtrakul T. Budhisawasdi V. Pairojkul C. Sangrajang S. Harris C.C. Loffredo C.A. Wiltrout R. Tumor metabolism and associated serum metabolites define prognostic subtypes of Asian hepatocellular carcinoma. Sci. Rep. 2021 11 1 12097 10.1038/s41598‑021‑91560‑1 34103600
    [Google Scholar]
  58. Peron G. Lin D. Editorial: Serum metabolites in diagnostics and therapeutics. Front Mol. Biosci. 2024 11
    [Google Scholar]
  59. Wai K.C. Gwan C.H. Lishan S.A. Christine H. Jessica Q. Bun C.Y. One-carbon metabolism, insulin resistance, and fecundability in a Singapore prospective preconception cohort study. Am. J. Clin. Nutr. 2025 122 1 335 343 10.1016/j.ajcnut.2025.04.035
    [Google Scholar]
  60. Zhang A. Sun H. Wang X. Serum metabolomics as a novel diagnostic approach for disease: A systematic review. Anal. Bioanal. Chem. 2012 404 4 1239 1245 10.1007/s00216‑012‑6117‑1 22648167
    [Google Scholar]
  61. Zhang C. Zhou S. Chang H. Zhuang F. Shi Y. Chang L. Ai W. Du J. Liu W. Liu H. Zhou X. Wang Z. Hong T. Metabolomic profiling identified serum metabolite biomarkers and related metabolic pathways of colorectal cancer. Dis. Markers 2021 2021 1 9 10.1155/2021/6858809 34917201
    [Google Scholar]
  62. Lu W. Jiang Z. Huang J. Bian J. Yu X. Preoperative serum metabolites and potential biomarkers for perioperative cognitive decline in elderly patients. Front. Psychiatry 2021 12 665097 10.3389/fpsyt.2021.665097 34093278
    [Google Scholar]
  63. Baker S.A. Rutter J. Metabolites as signalling molecules. Nat. Rev. Mol. Cell Biol. 2023 24 5 355 374 10.1038/s41580‑022‑00572‑w 36635456
    [Google Scholar]
  64. Wang Y. Zhou L. Wang N. Qiu B. Yao D. Yu J. He M. Li T. Xie Y. Yu X. Bi Z. Sun X. Ji X. Li Z. Mo D. Ge W. Comprehensive characterization of metabolic consumption and production by the human brain. Neuron 2025 113 11 1708 1722.e5 10.1016/j.neuron.2025.03.003 40147438
    [Google Scholar]
  65. Zhang J. Hao Z. Chen Z. Su X. Xu W. Jiang X. Nian X. Unveiling the atlas of associations between 1,400 plasma metabolites and 24 tumors: Mendelian Randomization analyses. Transl. Cancer Res. 2024 13 9 4938 4956 10.21037/tcr‑24‑359 39430859
    [Google Scholar]
  66. Lv M. Cao D. Zhang L. Hu C. Li S. Zhang P. Zhu L. Yi X. Li C. Yang A. Yang Z. Zhu Y. Zhang K. Pan W. METTL9 mediated N1-histidine methylation of zinc transporters is required for tumor growth. Protein Cell 2021 12 12 965 970 10.1007/s13238‑021‑00857‑4 34218407
    [Google Scholar]
  67. Davydova E. Shimazu T. Schuhmacher M.K. Jakobsson M.E. Willemen H.L.D.M. Liu T. Moen A. Ho A.Y.Y. Małecki J. Schroer L. Pinto R. Suzuki T. Grønsberg I.A. Sohtome Y. Akakabe M. Weirich S. Kikuchi M. Olsen J.V. Dohmae N. Umehara T. Sodeoka M. Siino V. McDonough M.A. Eijkelkamp N. Schofield C.J. Jeltsch A. Shinkai Y. Falnes P.Ø. The methyltransferase METTL9 mediates pervasive 1-methylhistidine modification in mammalian proteomes. Nat. Commun. 2021 12 1 891 10.1038/s41467‑020‑20670‑7 33563959
    [Google Scholar]
  68. Bi F. Qiu Y. Wu Z. Liu S. Zuo D. Huang Z. Li B. Yuan Y. Niu Y. Qiu J. METTL9-SLC7A11 axis promotes hepatocellular carcinoma progression through ferroptosis inhibition. Cell Death Discov. 2023 9 1 428 10.1038/s41420‑023‑01723‑4 38017014
    [Google Scholar]
  69. Durani U. Ansell S.M. CD5+ diffuse large B-cell lymphoma: A narrative review. Leuk. Lymphoma 2021 62 13 3078 3086 10.1080/10428194.2021.1953010 34284686
    [Google Scholar]
  70. Simões I.T. Aranda F. Carreras E. Andrés M.V. Casadó-Llombart S. Martinez V.G. Lozano F. Immunomodulatory effects of soluble CD5 on experimental tumor models. Oncotarget 2017 8 64 108156 108169 10.18632/oncotarget.22564 29296231
    [Google Scholar]
  71. Fenutría R. Martinez V.G. Simões I. Postigo J. Gil V. Martínez-Florensa M. Sintes J. Naves R. Cashman K.S. Alberola-Ila J. Ramos-Casals M. Soldevila G. Raman C. Merino J. Merino R. Engel P. Lozano F. Transgenic expression of soluble human CD5 enhances experimentally-induced autoimmune and anti-tumoral immune responses. PLoS One 2014 9 1 84895 10.1371/journal.pone.0084895 24454761
    [Google Scholar]
  72. Velasco-de Andrés M. Casadó-Llombart S. Català C. Leyton-Pereira A. Lozano F. Aranda F. Soluble CD5 and CD6: Lymphocytic Class I Scavenger Receptors as Immunotherapeutic Agents. Cells 2020 9 12 2589 10.3390/cells9122589 33287301
    [Google Scholar]
  73. Sun J.C. Lanier L.L. NK cell development, homeostasis and function: Parallels with CD8+ T cells. Nat. Rev. Immunol. 2011 11 10 645 657 10.1038/nri3044 21869816
    [Google Scholar]
  74. Schranner D. Kastenmüller G. Schönfelder M. Römisch-Margl W. Wackerhage H. Metabolite concentration changes in humans after a bout of exercise: A systematic review of exercise metabolomics studies. Sports Med. Open 2020 6 1 11 10.1186/s40798‑020‑0238‑4 32040782
    [Google Scholar]
  75. Dong Y. Lam S.M. Li Y. Li M.D. Shui G. The circadian clock at the intersection of metabolism and aging – emerging roles of metabolites. J. Genet. Genomics 2025 10.1016/j.jgg.2025.04.014
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673382735250904071151
Loading
Mediating Effects of Plasma Metabolites in Inflammatory Protein- Lymphoma Causality: A Mendelian Randomization Study
Bentham Science Publishers ; https://doi.org/10.2174/0109298673382735250904071151
/content/journals/cmc/10.2174/0109298673382735250904071151
/content/journals/cmc/10.2174/0109298673382735250904071151
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keywords: mendelian randomization ; immune microenvironment ; Diffuse large B-cell lymphoma ; circulating inflammatory proteins ; serum metabolites ; IVW method

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test