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The value of prognostic nutritional index in nasal-type, extranodal natural killer/T-cell lymphoma

Abstract

Extranodal natural killer/T-cell lymphoma (ENKTL) is an aggressive disorder with heterogeneous clinical characteristics and poor prognosis. The combined value of baseline serum albumin level and absolute peripheral lymphocyte count showed prognostic information in a variety of malignancies, but its evidence is limited in ENKTL. The purpose of this study is to evaluate the impact of prognostic nutritional index (PNI) in ENKTL, and to provide some nutritionally and immunologically relevant information for better risk stratification. We conducted a retrospective study in 533 patients newly diagnosed with ENKTL. The PNI was calculated as albumin (g/L) + 5 × lymphocyte count (109/L). The optimal cutoff values for serum albumin and lymphocyte count were 40.6 g/L and 1.18 × 109/L, respectively, and 47.3 for PNI. After a median follow-up of 70 months, the 5-year overall survival (OS) and progression-free survival (PFS) were 56.2% and 49.5%, respectively. Patients in low PNI group had more unfavorable clinical features, and tended to have worse 5-year OS and PFS compared with those in high PNI group. According PNI-associated prognostic score, patients were classified into different risk groups. Significant difference has been found in 5-year OS and PFS in different risk groups. When PNI and PNI-associated prognostic score were superimposed on the International Prognostic Index (IPI), prognostic index of natural killer lymphoma (PINK), or nomogram-revised risk index (NRI) categories, the PNI and PNI-associated prognostic score provided additional prognostic information. Therefore, PNI and PNI-associated prognostic score could be independent prognostic factors for ENKTL and may be useful for risk stratification and clinical decision-making.

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Data availability

The original data involved in this research will be made practicable by the authors on reasonable requirement, without excessive reservation.

Code availability

SPSS version 26.0 software (IBM SPSS) and R software (version 4.0.5).

References

  1. Lee J, Suh C, Park YH et al (2006) Extranodal natural killer T-cell lymphoma, nasal-type: a prognostic model from a retrospective multicenter study. J Clin Oncol 24:612–618

    Article  Google Scholar 

  2. Bhatkule MA, Dhawle MS, Kumbhakarna NR et al (2014) Nasal natural killer/T cell lymphoma. Indian J Hematol Blood Transfus 30:S292–S293

    Article  Google Scholar 

  3. Yamaguchi M, Suzuki R, Oguchi M et al (2017) Treatments and outcomes of patients with extranodal natural killer/T-cell lymphoma diagnosed between 2000 and 2013: a cooperative study in Japan. J Clin Oncol 35:32–39

    Article  Google Scholar 

  4. Yang Y, Zhu Y, Cao JZ et al (2015) Risk-adapted therapy for early-stage extranodal nasal-type NK/T-cell lymphoma: analysis from a multicenter study. Blood 126:1424–1432

    Article  CAS  Google Scholar 

  5. Kwong YL, Kim WS, Lim ST et al (2012) SMILE for natural killer/T-cell lymphoma: analysis of safety and efficacy from the Asia Lymphoma Study Group. Blood 120:2973–2980

    Article  CAS  Google Scholar 

  6. Chim CS, Ma SY, Au WY et al (2004) Primary nasal natural killer cell lymphoma: long-term treatment outcome and relationship with the International Prognostic Index. Blood 103:216–221

    Article  CAS  Google Scholar 

  7. You JY, Chi KH, Yang MH et al (2004) Radiation therapy versus chemotherapy as initial treatment for localized nasal natural killer (NK)/T-cell lymphoma: a single institute survey in Taiwan. Ann Oncol 15:618–625

    Article  Google Scholar 

  8. Kim SJ, Yoon DH, Jaccard A et al (2016) A prognostic index for natural killer cell lymphoma after non-anthracycline-based treatment: a multicenter, retrospective analysis. Lancet Oncol 17:389–400

    Article  CAS  Google Scholar 

  9. Chen SY, Yang Y, Qi SN et al (2021) Validation of nomogram-revised risk index and comparison with other models for extranodal nasal-type NK/T-cell lymphoma in the modern chemotherapy era: indication for prognostication and clinical decision-making. Leukemia 35(1):130–142

    Article  Google Scholar 

  10. Xiong J, Cui BW, Wang N et al (2020) Genomic and Transcriptomic Characterization of Natural Killer T Cell Lymphoma. Cancer Cell 37(3):403-419.e6

    Article  CAS  Google Scholar 

  11. Candido J, Hagemann T (2013) Cancer-related inflammation. J Clin Immunol 33(Suppl. 1):S79–S84

    Article  Google Scholar 

  12. Mayne ST, Playdon MC, Rock CL (2016) Diet, nutrition, and cancer: past, present and Future. Nat Rev Clin Onco 13(8):504–515

    Article  Google Scholar 

  13. Sun K, Chen S, Xu J et al (2014) The prognostic significance of the prognostic nutritional index in cancer: a systematic review and meta-analysis. J Cancer Res Clin Oncol 140(9):1537–1549

    Article  Google Scholar 

  14. Yang Y, Gao P, Chen X et al (2016) Prognostic significance of preoperative prognostic nutritional index in colorectal cancer: results from a retrospective cohort study and a meta-analysis. Oncotarget 7(36):58543–58552

    Article  Google Scholar 

  15. Yang Y, Gao P, Song Y et al (2016) The prognostic nutritional index is a predictive indicator of prognosis and postoperative complications in gastric cancer: a meta-analysis. Eur J Surg Oncol 42(8):1176–1182

    Article  CAS  Google Scholar 

  16. Zhao Y, Xu P, Kang H et al (2016) Prognostic nutritional index as a prognostic biomarker for survival in digestive system carcinomas. Oncotarget 7(52):86573–86583

    Article  Google Scholar 

  17. Chen P, Wang C, Cheng B et al (2017) Plasma fibrinogen and serum albumin levels (FA score) act as a promising prognostic indicator in non-small cell lung cancer. Oncotargets Ther 10:3107–3118

    Article  Google Scholar 

  18. Luan C, Wang F, Wei N et al (2020) Prognostic nutritional index and the prognosis of diffuse large b-cell lymphoma: a meta-analysis. Cancer Cell Int 20:455

    Article  CAS  Google Scholar 

  19. Yao NN, Hou Q, Zhang SP et al (2020) Prognostic Nutritional Index, Another Prognostic Factor for Extranodal Natural Killer/T Cell Lymphoma. Nasal Type Front Oncol 10:877

    Article  Google Scholar 

  20. Chen KL, Liu YH, Li WY et al (2015) The prognostic nutritional index predicts survival for patients with extranodal natural killer/T cell lymphoma, nasal type. Ann Hematol 94:1389–1400

    Article  CAS  Google Scholar 

  21. Onodera T, Goseki N, Kosaki G (1984) Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi 85:1001–1005

    CAS  PubMed  Google Scholar 

  22. Lenz G, Wright G, Dave SS et al (2008) Stromal gene signature in large-B-cell lymphomas. N Engl J Med 22:2313–2323

    Article  Google Scholar 

  23. Tran H, Nourse J, Hall S et al (2008) Immunodeficiency-associated lymphomas. Blood Rev 22:261–281

    Article  Google Scholar 

  24. Morton LM, Wang SS, Cozen W et al (2008) Etiologic heterogeneity among non-Hodgkin lymphoma subtypes. Blood 112:5150–5160

    Article  CAS  Google Scholar 

  25. Kim DH, Baek JH, Chae YS et al (2007) Absolute lymphocyte counts predicts response to chemotherapy and survival in diffuse large B-cell lymphoma. Leukemia 21:2227–2230

    Article  CAS  Google Scholar 

  26. von Meyenfeldt M (2005) Cancer-associated malnutrition: an introduction. Eur J Oncol Nurs 9(Suppl 2):S35–S38

    Article  Google Scholar 

  27. Don BR, Kaysen G (2004) Serum albumin: relationship to inflammation and nutrition. Semin Dial 17(6):432–427

    Article  Google Scholar 

  28. Gupta D, Lis CG (2010) Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J 9:69

    Article  Google Scholar 

  29. Ishizuka M, Nagata H, Takagi K et al (2007) Inflammation-based prognostic score is a novel predictor of postoperative outcome in patients with colorectal cancer. Ann Surg 246:1047–1051

    Article  Google Scholar 

  30. Ishizuka M, Nagata H, Takagi K et al (2009) Influence of inflammation-based prognostic score on mortality of patients undergoing chemotherapy for far advanced or recurrent unresectable colorectal cancer. Ann Surg 250:268–272

    Article  Google Scholar 

  31. McMillan DC, Crozier JE, Canna K et al (2007) Evaluation of an inflammation-based prognostic score (GPS) in patients undergoing resection for colon and rectal cancer. Int J Colorectal Dis 22:881–886

    Article  Google Scholar 

  32. Brenner DA, Buck M, Feitelberg SP et al (1990) Tumor necrosis factor-alpha inhibits albumin gene expression in a murine model of cachexia. J Clin Invest 85(1):248–255

    Article  CAS  Google Scholar 

  33. McMillan DC, Watson WS, O’Gorman P et al (2001) Albumin concentrations are primarily determined by the body cell mass and the systemic inflammatory response in cancer patients with weight loss. Nutr Cancer 39(2):210–213

    Article  CAS  Google Scholar 

  34. Perisa V, Zibar L, Knezovic A et al (2017) Prognostic nutritional index as a predictor of prognosis in patients with diffuse large B cell lymphoma. Wien Klin Wochenschr 129:411–419

    Article  CAS  Google Scholar 

  35. Lee SF, Ng TY, Wong FCS (2019) The value of prognostic nutritional index in follicular lymphoma. Am J Clin Oncol 42:202–207

    Article  Google Scholar 

  36. Shoji F, Morodomi Y, Akamine T et al (2016) Predictive impact for postoperative recurrence using the preoperative prognostic nutritional index in pathological stage I non-small cell lung cancer. Lung Cancer 98:15–21

    Article  Google Scholar 

  37. Kanda M, Mizuno A, Tanaka C et al (2016) Nutritional predictors for postoperative short-term and long-term outcomes of patients with gastric cancer. Medicine (Baltimore) 95:e3781

    Article  CAS  Google Scholar 

  38. Kim HS, Kim KH, Kim KH et al (2009) Whole blood Epstein-Barr virus DNA load as a diagnostic and prognostic surrogate: extranodal natural killer/T-cell lymphoma. Leuk Lymphoma 50:757–763

    Article  CAS  Google Scholar 

  39. Ito Y, Kimura H, Maeda Y et al (2012) Pretreatment EBV-DNA copy number is predictive of response and toxicities to SMILE chemotherapy for extranodal NK/T-cell lymphoma, nasal type. Clin Cancer Res 18:4183–4190

    Article  CAS  Google Scholar 

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Authors and Affiliations

Authors

Contributions

LQZ constructed this work and revised the manuscript. NL collected and analyzed data, performed research, and wrote the paper. MJ provided valuable recommendations. WCW participated in data analysis. All authors read and approved the final manuscript and the submitted version. All authors agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work.

Corresponding author

Correspondence to Li-qun Zou.

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Ethics approval

All data included in this study were approved by Ethics Committee on Biomedical Research, West China Hospital of Sichuan University, and in accordance with the declaration of Helsinki.

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Informed consent was exempted for this was a retrospective study.

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The authors declare no competing interests.

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Highlights

PNI and PNI-associated prognostic score predicted prognosis in ENKTL; when these factors were applied to IPI, PINK, and NRI models, better results were found.

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Li, N., Jiang, M., Wu, Wc. et al. The value of prognostic nutritional index in nasal-type, extranodal natural killer/T-cell lymphoma. Ann Hematol 101, 1545–1556 (2022). https://doi.org/10.1007/s00277-022-04849-0

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  • DOI: https://doi.org/10.1007/s00277-022-04849-0

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