Identification of IFN-γ/IL-4 ratio as a new predictor and the significance of its based immune related prognostic model in diffuse large B-cell lymphoma

Identification of IFN-γ/IL-4 ratio as a new predictor and the significance of its based immune related prognostic model in diffuse large B-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 Identification of IFN-γ/IL-4 ratio as a new predictor and the significance of its based immune related prognostic model in diffuse large B-cell lymphoma Pan Zhou, Yunmeng Zhou, Liu Yang, Chao Liu, Suqiong Zuo, Xiaohang Pei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6562919/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Aug, 2025 Read the published version in BMC Cancer → Version 1 posted 15 You are reading this latest preprint version Abstract Background Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignant tumor of B-cell origin that is predisposed by abnormal immune function and is extremely challenging. Lymphocyte subsets and circulating cytokines are easily accessible immune indicators, which may be useful in prognostication of newly diagnosed DLBCL patients, independently. However, the association between these two clinical laboratory findings and prognostication of newly diagnosed DLBCL patients has not been studied. Method Here, we retrospectively analyzed 94 newly diagnosed DLBCL patients initially treated in our institution between 2017 and 2022. The prognostic influence of lymphocyte subsets, cytokines levels and other factors, including age, tumor stage on progression-free survival (PFS) and overall survival (OS) were studied by Kaplan–Meier curves as well as univariate and multivariate Cox regression models. Based on the findings in our center, we constructed an immune-related prognostic model and further validated it in an independent cohort from another center. Results The results suggested that IFN-γ/IL-4 ratio and CD4 + T cell count were independent risk variables for both PFS and OS in DLBCL patients. Besides, multivariate analysis showed that age was associated with the worse OS whereas CD8 + T cell count was associated with the inferior PFS. Moreover, elevated pretreatment IFN-γ/IL-4 ratio was significantly correlated with poor clinical response efficacy. Compared to patients experienced with death, lower level of IFN-γ/IL-4 ratio was discovered in surviving patients during the subsequent treatment cycles. Additionally, a new immune-related prognostic score model (IRPS) was constructed based on age, CD4 + T cell count and IFN-γ/IL-4 ratio, where high-risk patients had worse overall survival than low-risk patients. Meanwhile, the IRS could refine the IPI score well and validation of the IRS model in another independent cohort confirmed its effectiveness. Conclusion IFN-γ/IL-4 ratio is a simple, accessible but useful prognostic factor in newly diagnosed DLBCL patients, and the IRS model could better suggest PFS and OS of DLBCL, allowing for better risk stratification. Diffuse large B-cell lymphoma IFN-γ/IL-4 ratio CD4 + T cell Immune related prognostic model International prognostic index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of aggressive lymphoma, which occupies approximately 30–40% of all adult non-Hodgkin's lymphoma (NHL). Patients often show a rapidly growing tumour mass in one or more lymph nodes or extra-nodal tissues(1, 2). With the use of rituximab combined immunochemotherapy, the prognosis of patients has been greatly improved. Nevertheless, DLBCL is highly heterogeneous both clinically and prognostically, with nearly 40% of patients becoming refractory or relapsing(3). Therefore, it becomes particularly important to assess the accurate prognostic risk of each newly diagnosed patient and design the appropriate initial treatment. Currently, the most commonly used clinical predictive model is the International Prognostic Index (IPI), which involves five parameters: age > 60, elevated serum lactate dehydrogenase (LDH), Eastern Cooperative Oncology Group (ECOG) performance status ≥ 2, Ann Arbor stage III or IV and number of involved extranodal sites ≥ 2. For patients with age < 60, the aa-IPI score was derived. With the advent of the rituximab era, the IPI score has diminished and R-IPI, NCCN-IPI were proposed, which shows superior prediction in outcome of DLBCL patients treated with standard immunochemotherapy(4–6). In spite of the fact that these scoring systems provided better prognostic guidance, novel biomarkers are still needed to better identify high-risk patients who could benefit from more aggressive therapeutic approaches. DLBCL is a result of malignant B-cell development, which potentially involves the systemic immunology and the tumor microenvironment, including abnormal immune cells constitution and the aberrant immune responses(7, 8). For examples, a large study has shown that the absolute blood peripheral CD4 + T cells is an strong independent poor predictor of survival in R-CHOP-treated patients with DLBCL(9). Low circulating CD4 + T-cell levels predict poorer PFS outcomes in DLBCL(10). A low ratio of circulating CD8 + T Lymphocytes to M- myeloid-derived suppressor cells (MDSCs) serves as a poor prognostic factor for both PFS and OS in treatment-naïve DLBCL patients(11). Lymphocyte to monocyte ratio (LMR) reflecting both the immune status in the peripheral blood as well as in the tumor micro-environment has been suggested as an effective prognostic factor for predicting clinical survival in DLBCL patients (12). In addition, cytokines may serve as indicators of tumor immune status and modulate the immune system during lymphoma progression. For examples, elevated pretreatment serum cytokines have been reported to associated with an increased likelihood of disease relapse and an inferior survival in patients with DLBCL (13). Serum levels of IL-6 and IL-10 positively correlated with high IPI score, bone marrow involvement, elevated LDH/β2-microglobulin (β2-MG), short PFS and OS in patients with DLBCL (14). Although numerous studies have demonstrated that several types of lymphocytes and cytokines could be used to predict prognosis of DLBCL, the clinical significance of the combination of these immunoregulation components remains indistinct. Therefore, in this study, we retrospectively analyzed the count of peripheral lymphocyte subtypes and cytokines levels in 94 newly diagnosed DLBCL patients and explored their impact on the prognosis, aiming to discover better predictors and build a new prognostic model correlated with DLBCL. 2. Materials and methods 2.1 Subjects This retrospective study collected data from newly diagnosed DLBCL patients at the Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, between January 2017 and December 2023. The histological classification of DLBCL was performed according to the guidelines by the World Health Organization. The inclusion criteria were as follows: (1) newly diagnosed DLBCL patients; (2) at least 4 cycles of chemotherapy were completed. The exclusion criteria were as follows: (1) patients with other tumors; (2) patients with special types of lymphoma (primary central nervous system lymphoma, primary mediastinal large B-cell lymphoma, and transformed DLBCL). 94 patients with DLBCL, including 49 (53.2%) females and 45 (47.8%) males, were enrolled. The median age was 63 years, and the follow-up date was up to July 2024. A validation cohort included 47 patients with newly diagnosed DLBCL from the First Affiliated Hospital of Zhengzhou University during the same period. This research was carried out with approval from the Ethics Committee of the Henan Provincial People’s Hospital (Zhengzhou University People’s Hospital Zhengzhou University People’s Hospital) and the First Affiliated Hospital of Zhengzhou University and was conducted in accordance with the Declaration of Helsinki. 2.2 Data collection The clinical baseline characteristics were collected from medical records, including age, gender, ECOG score, IPI score, B-symptoms (fever, night sweats, or weight loss), Ann Arbor stage, lactate dehydrogenase (LDH) level, extra-nodal involvement, IPI score, and treatment regimen. Likewise, laboratorial data were available from the hospital-based laboratory, such as serum β2-microglobulin (serum β2-MG), hemoglobin (HB), C-reactive protein (CRP), bone marrow involvement, lymphocyte subsets (CD 4 + T cell, CD8 + T cell, B cell, and NK-cell counts), serum cytokines (IL-4, IL-6, IL-10, IL-17, IL-12, IFN-γ) and other laboratory test results were analyzed, and CT, MRI, PET-CT and other imaging results were collected for evaluation of their efficacy. 2.3 Patients’ follow-up The follow-up data were obtained through electronic medical records and telephone interviews. Progression-free survival (PFS) was calculated from the date from diagnosis until disease progression, relapse, death, or the end of follow-up, while overall survival (OS) was defined as the interval between the date of diagnosis and the death of patient or the end of follow-up. 2.3 Statistical analyses Comparisons between groups were made using the Mann-Whitney U test, receiver operating characteristic (ROC) was used to define optimal cut-off values for lymphocyte subtypes (CD4 + T cell, CD8 + T cell, B cell, NK cell counts) and cytokines (IL-4, IL-6, IL-10, IL-17, IL-12, IFN-γ/IL-4, IL-12/IL-17). Univariate logistic regression and multivariate logistic regression were performed using Cox proportional risk regression models to assess prognostic variables for survival analysis. Kaplan–Meier survival curves and log-rank tests were used to estimate survival time. Variables with statistical significance in the multivariate analysis were selected to build a new predictive model. ROC curves were used to calculate the AUC values when comparing the performance of different prognostic scores. The new model was compared with the existing prognostic model by plotting the receiver operating characteristic curve (ROC) to evaluate its predictive value and applicability. SPSS statistical software package 20.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism version 8.0.1 (GraphPad Software, CA, USA) were used for statistical analyses. P < 0.05 was considered statistically significant. 3. Results 3.1 General clinical characteristics of the newly diagnosed DLBCL patients Overall, 45 (47.8%) male and 49 (53.2%) female patients diagnosed with DLBCL were included in this study and their characteristics are presented in Table 1. The median age at diagnosis was 63 (9–89) years and 51 (54.2%) were >60 years. 14 (14.8%) patients had ECOG PS score >2 and 39 (41.4%) had B symptoms. Regarding Ann Arbor stage, 26 (27.7%) patients had stage I or II, and 68 (72.3%) had stage III or IV. LDH level was elevated in 56 (58.9%) cases. A total of 24 (35.7%) cases had ≥2 extranodal lesions and bone marrow invasion was involved in 27 (28.7%) cases. The patients were stratified into 2 risk-predicting groups by IPI value: 44 (46.8%) cases in low to intermediate risk group (0–2) and 50 (53.2%) cases in intermediate to high risk group (3–5). A total of 78 (82.9%) patients were treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone) or R-CHOP-like immunochemotherapy and 26 (17.1%) cases were treated with R2 (rituximab, lenalidomide) regimen. 3.2 Prognostic factors for OS and PFS of the newly diagnosed DLBCL patients Clinical indicators with significant impact on OS and PFS in the univariable analyses were presented in Table 2. The univariate analysis revealed that a age >60 years, IPI score ≥3, CD4+ T ≤263.5/ul, CD8+ T≤245/ul , IFN-γ/IL-4 ratio≤2.66, IFN-γ≤9.36pg/ml, IL-12/IL-17 ratio≥0.72 were all high-risk indicators affecting patients’ OS and PFS. However, a β2-MG level >2.3 mg/L was identified as a high-risk factor adversely affecting patients' PFS, but demonstrated minimal impact on OS. The Kaplan-Meier analysis presented in Figures 1 and 2 revealed significant variables related to inferior OS or PFS. To further determine the independent prognostic values, subsequent multivariate survival analysis was performed. As shown in Table 3, age>60 (p=0.030), CD4+ T ≤263.5/ul (p=0.022), and IFN-γ/IL-4 ratio≤2.66 (p=0.001) were significant prognostic factors for worse OS. CD4+ T ≤263.5/ul (p=0.037), CD8+ T≤245/ul (p=0.019), and IFN-γ/IL-4 ratio≤2.66 (p=0.001) were identified as predictors for inferior PFS. 3.3 Relationship of IFN-γ/IL-4 to efficacy of treatment Given that the IFN-γ/IL-4 ratio emerged as the strongest prognostic factor in survival analysis, we further investigated its value in efficacy assessment. Using the cut-off value of 2.66 at diagnosis, patients were divided into high IFN-γ/IL-4 (63/94, 67.0%) and low IFN-γ/IL-4 (31/94, 32.9%) groups. Among the high IFN-γ/IL-4 group, 22/63 (34.9%) patients achieved complete response (CR), 18/63 (28.6%) patients achieved PR, 8/63 (12.7%) patients achieved SD, and 15/63 (23.8%) patients experienced progressive disease (PD). In contrast, in the low IFN-γ/IL-4 group, only (7/31, 22.6%) patients achieved CR, 5/31 (16.1%) patients achieved PR, 4/31 (12.9%) patients experienced SD, and 15/31 (48.4%) developed PD (Figure 3A). Therefore, these results showed that a significant higher overall response rate (ORR) was observed in the higher IFN-γ/IL-4 group at diagnosis compared to the lower IFN-γ/IL-4 group. To evaluate IFN-γ/IL-4 dynamic changes during treatment, we analyzed 61 patients with complete data, measuring peripheral plasma cytokines at initial diagnosis (n=61), after 2 cycles (n=61), 4 cycles (n=51), and 6 cycles (n=46) of chemotherapy. As shown in Figure 3B, patients who survived maintained significantly higher IFN-γ/IL-4 ratios than non-survivors throughout all chemotherapy cycles (p<0.05). 3.4 Construction of a new immune-related prognostic score and integration with the IPI score in newly diagnosed DLBCL patients Using statistically significant predictors of shorter OS identified in multivariate analyses, we constructed a novel immune-related prognostic score (IRPS) assigning 1 point each for age >60 years, CD4+ T cells ≤263.5/μL, and IFN-γ/IL-4 ratio ≤2.66 with score range from 0–3. The new prognostic model stratified patients into low-risk (0-1 scores) and high-risk (2-3 scores) groups, with the low-risk group demonstrating significantly superior OS (p<0.0001) and PFS (p<0.0001) compared to the high-risk group (Figure 4A-B). Next, we performed ROC analysis to compare the sensitivity and specificity of survival prediction between the newly established IRPS model and the IPI model. The results that the IRPS model showed superior discriminative ability for OS prediction compared to the IPI score, with AUC values of 0.7295 and 0.6095, respectively (Figure 4C). To further evaluate whether IRPS could refine IPI risk stratification, we reclassified patients across all IPI risk categories (low-risk: 0-1; intermediate-risk: 2-3; high-risk: 4-5) using the IRPS score. As shown in Figure 5, our novel model could effectively discriminate both OS and PFS across all IPI risk stratifications (low-risk group, p<0.01; intermediate-risk group, p<0.01; high-risk group, p<0.01). 3.5 Validation of the new immune-related prognostic model To confirm the predictive accuracy of this novel prognostic model patients with newly diagnosed DLBCL, we collected a validation cohort from the First Affiliated Hospital of Zhengzhou University during the same study period. A total of 47 patients with complete data meeting the inclusion criteria were enrolled, comprising 27 males (47.8%) and 20 females (53.2%) with a median age of 55 years and followed through July 2024. Survival probabilities were analyzed using Kaplan-Meier curves after stratifying patients into high- and low-risk groups based on IRPS scoring criteria. The results revealed statistically significant differences in both OS (Figure 6A; p<0.001) and PFS (Figure 6B; p<0.001) between high- and low-risk groups stratified by the IRPS model, confirming its robust prognostic validity. Notably, the IRPS model showed non-inferior prognostic performance relative to the IPI model, achieving a slightly higher AUC value (0.7941 vs 0.7818) in the validation cohort (Figure 6C). 4. Discussion Despite rapid therapeutic advances in DLBCL, patient outcomes remain highly heterogeneous, influenced by clinical factors, biologic and molecular subtypes, and other undiscovered factors (15). Dysfunctional anti-cancer immunity including the systemic immune response and tumor infiltrating immune response contributes to DLBCL pathogenesis and progression. Thereinto, low absolute lymphocyte counts, and aberrate peripheral lymphocyte subset distributions serve as indicators of systemic immune status and predict poor prognosis in DLBCL patients, while tumor infiltrating lymphocytes abnormity represents the local immune microenvironment (16, 17). Generally speaking, lymphocyte subsets are typically composed of CD4 + T cells, CD8 + T cells, B lymphocytes, and natural killer (NK) cells (18). CD4 + helper T cells promote the activation of cytotoxic CD8 + T cells and secrete a variety of cytokines that mediate anti-tumor immune responses, thereby playing a critical role in the anti-tumour immune response. CD8 + T cells are cytotoxic T cells that directly lyse tumor cells by releasing cytotoxins containing perforin and granzyme (19, 20). Several studies of DLBCL have found that CD4 + T cells are an independent prognostic factor and that higher circulating and local intratumoral CD4 + T cells are associated with improved clinical prognosis (7, 10, 21). Consistent with these studies, our study similarly demonstrated that CD4 + T cell counts in peripheral blood at initial diagnosis was positively correlated with both OS and PFS. It was reported that DLBCL with high density of CD8 + infiltrating T cells seemed to have improved outcome. For examples, Rajnai H et al. found that the number of tumour-infiltrating CD8 + T was an independent favorable prognostic marker for survival in primary diffuse large B-cell lymphoma of bone (22). Shi and colleagues identified that high CD8 + T-cell infiltration density in tumor tissues correlated with prolonged progression-free survival (PFS) in Chinese DLBCL patients, independent of rituximab treatment status (23). Gergely et al. proposed that patients with low pretreatment CD8 + T cell counts in B-cell non-Hodgkin's lymphoma had significantly lower overall survival rates (24). Nevertheless, the prognostic significance of peripheral blood CD8 + T cell counts remains unexplored in broader DLBCL populations. Surprisingly, in our study we further observed that elevated peripheral blood CD8 + T cell counts at initial diagnosis were significantly associated with improved PFS and OS. Given that peripheral blood T cells correlate positively with tumor-infiltrating T cells in DLBCL and considering CD8 + T cells serve as precursors for cytotoxic T lymphocytes (CTLs) (25), we hypothesize that circulating CD8 + T cell depletion may result in inadequate CTL-mediated tumor cytotoxicity, thereby adversely affecting DLBCL patient survival. NK cells, serving as surrogate markers of immune status, demonstrate significant associations with clinical outcomes in DLBCL patients during the rituximab era (26). However, our analysis revealed no statistically significant correlation between peripheral NK-cell counts and survival outcomes in DLBCL patients. Despite limited data on peripheral B cells in DLBCL, Rusak M et al. discovered that diagnostic B cell counts lacked clinical value, but post-R-CHOP B-cell expansion predicted inferior treatment outcomes (17). Although post-treatment B cell levels were not analyzed in our study, we similarly demonstrated that baseline B cell counts at initial diagnosis showed no prognostic significance in DLBCL patients. As we know, T lymphocytes function as primary effector cells in mediating cellular immunity, with cytokine secretion representing a fundamental mechanism of their biological activity and immune regulation. According to the cytokine expression profiles and immune regulatory functions, CD4 + T cells differentiate into specialized subsets, including Th1, Th2, Th17, and T follicular helper (Tfh) cells, with Th1 and Th2 representing two pivotal effector lineages (27). Th1 cells exert direct antitumor effects by secreting cytokines such as IFN-γ, IL-2, and TNF-α, which induce tumor cell apoptosis, senescence, and functional inactivation. In contrast, Th2 cells promote tumour growth and metastasis by activating STAT6 through IL-4 (28–30). Thus, Th1/Th2 imbalance is a common mechanism for immune escape and is closely related to cancer development and prognosis. Abnormalities in the CD4 + T cell compartment, including the Th1 and Th2 subsets and related cytokines, have been shown to affect the occurrence of non-Hodgkin lymphoma (7). However, whether Th1/Th2 imbalance at diagnosis impacts prognosis in newly diagnosed DLBCL patients remains largely undetermined. Given that Th1 response activation is primarily driven by IFN-γ while Th2 differentiation depends on IL-4, we tried to use the peripheral blood IFN-γ/IL-4 ratio as a surrogate for Th1/Th2 balance and investigated its prognostic role in DLBCL. Here, our study identified the IFN-γ/IL-4 ratio as an independent significant prognostic biomarker of both OS and PFS in DLBCL. Notably, we stratified patients into high- and low-ratio groups using an optimal cutoff value for survival analysis. The efficacy assessment revealed that patients with high IFN-γ/IL-4 ratios showed superior treatment responses, with significantly increased CR/PR rates compared to low-ratio counterparts. In addition, the longitudinal analysis of IFN-γ/IL-4 ratio dynamics during treatment revealed sustained elevation in patients with favorable prognostic outcomes. Mori T et al. employed flow cytometric analysis of intracellular IFN-γ/IL-4 to evaluate Th1/Th2 balance, demonstrating Th2 polarization in untreated DLBCL patients versus Th1 dominance in complete remission cases (31). This conclusion together with our findings demonstrate that high IFN-γ/IL-4 levels (reflecting Th1 polarization) correlate with enhanced treatment responses, reduced relapse rates, and superior clinical outcomes, suggesting their potential involvement in DLBCL pathogenesis and progression. However, Mehdi et al. demonstrated in their cohort study of 47 Hodgkin's lymphoma and 48 DLBCL patients that higher peripheral blood IFN-γ-/IL-4 + Th2 lymphocytes might be associated with a favorable prognosis like lower rate of relapse (32). Therefore, our future study might investigate the level of IFN-γ/IL-4 in patients with sustained remission versus relapse to further determine its prognostic significance. Due to the marked heterogeneity in DLBCL patient survival outcomes, comprehensive risk assessment prior to treatment initiation is essential for optimal therapeutic strategy selection. Currently, the IPI, R-IPI, and NCCN-IPI are the most commonly employed prognostic scoring systems in DLBCL. However, all 3 scoring systems failed to identify a patient subgroup with long-term survival clearly < 50% in the rituximab era (4, 33). Advances in molecular profiling have increasingly highlighted the critical roles of both systemic and tumor microenvironmental immunity in DLBCL pathogenesis, driving recent proposals to incorporate immune-related biomarkers into prognostic scoring systems (8, 34, 35). In this study, using multivariate regression analysis of clinical (age) and immunologic parameters (CD4 + T cell count, IFN-γ/IL-4 ratio), we established an immune-related prognostic score (IRPS) that effectively classifies DLBCL patients into low-risk group (0–1 scores) and high-risk group (2–3 scores). Encouragingly, validation at another independent center replicated the model’s predictive performance. What’s more surprisingly, ROC curve analysis demonstrated superior prognostic performance of our novel model compared to the IPI scoring system. Additionally, the IRPS model could further substratified each IPI risk category (low/intermediate/high) patients into distinct low- and high-risk subsets. Integrating the IRPS model with conventional IPI scoring improves risk stratification by detecting unfavorable-prognosis patients originally classified as IPI low-intermediate risk, thereby offering timely opportunities to apply appropriate therapeutic interventions to potentially enhancing survival and prognosis. Moreover, these clinical indicators are economical and readily available in clinical practice, making their integration into daily clinical practice feasible. Our study has several limitations. Firstly, the single-center design and relatively small sample size may limit generalizability, future multi-center validation with larger cohorts is needed to confirm these findings. Secondly, due to the limited sample size in the validation cohort, we were unable to demonstrate the prognostic superiority of the IRPS model over the IPI scoring system. Thirdly, as a retrospective analysis, our findings warrant prospective confirmation to assess multifactorial interactions more rigorously. Finally, the use of plasma IFN-γ/IL-4 levels to represent the Th1/Th2 ratio in our research may not be accurate enough, and more studies are needed to validate the relationship between IFN-γ/IL-4 and Th1/Th2. Conclusion In summary, our study demonstrates that the IFN-γ/IL-4 ratio serves as a significant independent predictor of clinical outcomes in newly diagnosed DLBCL patients. The Immune-Related Prognostic Score (IRPS) model, constructed from the IFN-γ/IL-4 ratio in conjunction with age and CD4 + T-cell counts, significantly improves IPI-based risk stratification and might lead to more accurate prognostic assessments and more optimized treatment strategies. Declarations Ethics approval and consent to participate The study was conducted in compliance with the Helsinki Declaration and was approved by the Medical Ethical Committees of both the Henan Provincial People’s Hospital (Zhengzhou University People’s Hospital Zhengzhou University People’s Hospital) and the First Affiliated Hospital of Zhengzhou University. Written informed consent to participate in the study was obtained from all the patients treated in the Henan Provincial People’s Hospital (Zhengzhou University People’s Hospital Zhengzhou University People’s Hospital) and the First Affiliated Hospital of Zhengzhou University. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial Not applicable. Funding: This work was funded by the Youth Program of the National Natural Science Foundation of China (No. 82100222), the Henan Province Medical Science and Technology Project Constructed by the Provincial and Ministerial Departments, PR China (No. SBGJ202303005), and the Henan Province Medical Science and Technology Tackling Program Joint Co-construction Project, PR China (No. LHGJ20230023). Author Contribution Pan Zhou and Yunmeng Zhou collected, assembled the data and wrote the manuscript. Liu Yang and Chao Liu participated in the work of follow-up. Suqiong Zuo and Xiaohang Pei participated in the literature review and statistical analysis. Rongjun Ma and Yuqing Chen supervised and reexamined the search process. Xiaoli Yuan and Zunmin Zhu conceived, designed the study and revised the manuscript. All authors wrote and approved of the article and are accountable for publication. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Q. Cui, W. Tan, B. Song, R. J. Peng, L. Wang, R. Dorajoo, K. P. Ng, G. W. Lin, W. Y. Au, R. H. S. Liang, C. C. Khor, Q. L. Zhang, J. N. Foo, S. P. Li, F. R. Zhang, X. J. Zhang, X. Q. Yu, Q. Lan, S. Chanock, W. H. Jia, S. T. Lim, W. Y. Li, N. Rothman, J. X. Bei, J. Liu, D. Lin and J. J. Liu: Genetic susceptibility of diffuse large B-cell lymphoma: a meta genome-wide association study in Asian population. Leukemia , 39(3), 694-702 (2025) doi:10.1038/s41375-024-02503-4 M. Martelli, A. J. Ferreri, C. Agostinelli, A. Di Rocco, M. Pfreundschuh and S. A. Pileri: Diffuse large B-cell lymphoma. Crit Rev Oncol Hematol , 87(2), 146-71 (2013) doi:10.1016/j.critrevonc.2012.12.009 R. Vaidya and T. E. Witzig: Prognostic factors for diffuse large B-cell lymphoma in the R(X)CHOP era. Ann Oncol , 25(11), 2124-2133 (2014) doi:10.1093/annonc/mdu109 A. S. Ruppert, J. G. Dixon, G. Salles, A. Wall, D. Cunningham, V. Poeschel, C. Haioun, H. Tilly, H. Ghesquieres, M. Ziepert, J. Flament, C. Flowers, Q. Shi and N. Schmitz: International prognostic indices in diffuse large B-cell lymphoma: a comparison of IPI, R-IPI, and NCCN-IPI. Blood , 135(23), 2041-2048 (2020) doi:10.1182/blood.2019002729 L. H. Sehn, B. Berry, M. Chhanabhai, C. Fitzgerald, K. Gill, P. Hoskins, R. Klasa, K. J. Savage, T. Shenkier, J. Sutherland, R. D. Gascoyne and J. M. Connors: The revised International Prognostic Index (R-IPI) is a better predictor of outcome than the standard IPI for patients with diffuse large B-cell lymphoma treated with R-CHOP. Blood , 109(5), 1857-61 (2007) doi:10.1182/blood-2006-08-038257 Z. Zhou, L. H. Sehn, A. W. Rademaker, L. I. Gordon, A. S. Lacasce, A. Crosby-Thompson, A. Vanderplas, A. D. Zelenetz, G. A. Abel, M. A. Rodriguez, A. Nademanee, M. S. Kaminski, M. S. Czuczman, M. Millenson, J. Niland, R. D. Gascoyne, J. M. Connors, J. W. Friedberg and J. N. Winter: An enhanced International Prognostic Index (NCCN-IPI) for patients with diffuse large B-cell lymphoma treated in the rituximab era. Blood , 123(6), 837-42 (2014) doi:10.1182/blood-2013-09-524108 Y. Kusano, M. Yokoyama, Y. Terui, N. Nishimura, Y. Mishima, K. Ueda, N. Tsuyama, H. Yamauchi, A. Takahashi, N. Inoue, K. Takeuchi and K. Hatake: Low absolute peripheral blood CD4+ T-cell count predicts poor prognosis in R-CHOP-treated patients with diffuse large B-cell lymphoma. Blood Cancer J , 7(5), e561 (2017) doi:10.1038/bcj.2017.43 M. Autio, S. K. Leivonen, O. Bruck, S. Mustjoki, J. Meszaros Jorgensen, M. L. Karjalainen-Lindsberg, K. Beiske, H. Holte, T. Pellinen and S. Leppa: Immune cell constitution in the tumor microenvironment predicts the outcome in diffuse large B-cell lymphoma. Haematologica , 106(3), 718-729 (2021) doi:10.3324/haematol.2019.243626 Y. Kusano, M. Yokoyama, Y. Terui, N. Nishimura, Y. Mishima, K. Ueda, N. Tsuyama, H. Yamauchi, A. Takahashi, N. Inoue, K. Takeuchi and K. Hatake: Low absolute peripheral blood CD4+ T-cell count predicts poor prognosis in R-CHOP-treated patients with diffuse large B-cell lymphoma. Blood Cancer J , 7(4), e558 (2017) doi:10.1038/bcj.2017.37 J. Judd, E. Dulaimi, T. Li, M. M. Millenson, H. Borghaei, M. R. Smith and T. Al-Saleem: Low Level of Blood CD4(+) T Cells Is an Independent Predictor of Inferior Progression-free Survival in Diffuse Large B-cell Lymphoma. Clin Lymphoma Myeloma Leuk , 17(2), 83-88 (2017) doi:10.1016/j.clml.2016.11.005 H.-Y. Wang, F.-C. Yang, C.-F. Yang, C.-K. Tsai, P.-S. Ko, Y.-C. Liu and N.-J. Chen: Ratio of Circulating CD8+ T Lymphocytes to M-MDSCs (CD8MMR): A Novel Prognostic Predictor for Treatment-Naïve DLBCL Patients. Blood , 142, 1763 (2023) F. Gao, J. Hu, J. Zhang and Y. Xu: Prognostic Value of Peripheral Blood Lymphocyte/monocyte Ratio in Lymphoma. J Cancer , 12(12), 3407-3417 (2021) doi:10.7150/jca.50552 S. M. Ansell, M. J. Maurer, S. C. Ziesmer, S. L. Slager, T. M. Habermann, B. Link, T. E. Witzig, J. Cerhan and A. J. Novak: Pretreatment serum cytokines predict early disease relapse and a poor prognosis in diffuse large B-cell lymphoma (DLBCL) patients. Blood , 116(21), 991 (2010) C. Bao, J. Gu, X. Huang, L. You, Z. Zhou and J. Jin: Cytokine profiles in patients with newly diagnosed diffuse large B-cell lymphoma: IL-6 and IL-10 levels are associated with adverse clinical features and poor outcomes. Cytokine , 169, 156289 (2023) doi:10.1016/j.cyto.2023.156289 J. L. Koff and C. R. Flowers: Prognostic modeling in diffuse large B-cell lymphoma in the era of immunochemotherapy: Where do we go from here? Cancer , 123(17), 3222-3225 (2017) doi:10.1002/cncr.30740 H. Hou, Y. Luo, G. Tang, B. Zhang, R. Ouyang, T. Wang, M. Huang, S. Wu, D. Li and F. Wang: Dynamic changes in peripheral blood lymphocyte subset counts and functions in patients with diffuse large B cell lymphoma during chemotherapy. Cancer Cell Int , 21(1), 282 (2021) doi:10.1186/s12935-021-01978-w M. Rusak, L. Bolkun, J. Chociej-Stypulkowska, J. Pawlus, J. Kloczko and M. Dabrowska: Flow-cytometry-based evaluation of peripheral blood lymphocytes in prognostication of newly diagnosed DLBCL patients. Blood Cells Mol Dis , 59, 92-6 (2016) doi:10.1016/j.bcmd.2016.04.004 D. F. LaRosa and J. S. Orange: 1. Lymphocytes. Journal of Allergy and Clinical Immunology , 121(2), S364-S369 (2008) J. Zhu and W. E. Paul: CD4 T cells: fates, functions, and faults. Blood , 112(5), 1557-69 (2008) doi:10.1182/blood-2008-05-078154 N. S. Nicholas, B. Apollonio and A. G. Ramsay: Tumor microenvironment (TME)-driven immune suppression in B cell malignancy. Biochim Biophys Acta , 1863(3), 471-482 (2016) doi:10.1016/j.bbamcr.2015.11.003 C. Keane, D. Gill, F. Vari, D. Cross, L. Griffiths and M. Gandhi: CD4(+) tumor infiltrating lymphocytes are prognostic and independent of R-IPI in patients with DLBCL receiving R-CHOP chemo-immunotherapy. Am J Hematol , 88(4), 273-6 (2013) doi:10.1002/ajh.23398 H. Rajnai, F. H. Heyning, L. Koens, A. Sebestyen, H. Andrikovics, P. C. Hogendoorn, A. Matolcsy and A. Szepesi: The density of CD8+ T-cell infiltration and expression of BCL2 predicts outcome of primary diffuse large B-cell lymphoma of bone. Virchows Arch , 464(2), 229-39 (2014) doi:10.1007/s00428-013-1519-9 Y. Shi, L. Deng, Y. Song, D. Lin, Y. Lai, L. Zhou, L. Yang and X. Li: CD3+/CD8+ T-cell density and tumoral PD-L1 predict survival irrespective of rituximab treatment in Chinese diffuse large B-cell lymphoma patients. Int J Hematol , 108(3), 254-266 (2018) doi:10.1007/s12185-018-2466-7 L. Gergely, A. Vancsa, Z. Miltenyi, Z. Simon, S. Barath and A. Illes: Pretreatment T lymphocyte numbers are contributing to the prognostic significance of absolute lymphocyte numbers in B-cell non-Hodgkins lymphomas. Pathol Oncol Res , 17(2), 249-55 (2011) doi:10.1007/s12253-010-9306-2 F. E. Laddaga, G. Ingravallo, A. Mestice, R. Tamma, T. Perrone, E. Maiorano, D. Ribatti, G. Specchia and F. Gaudio: Correlation between circulating blood and microenvironment T lymphocytes in diffuse large B-cell lymphomas. J Clin Pathol , 75(7), 493-497 (2022) doi:10.1136/jclinpath-2020-207048 J. K. Kim, J. S. Chung, H. J. Shin, M. K. Song, J. W. Yi, D. H. Shin, D. S. Lee and S. M. Baek: Influence of NK cell count on the survival of patients with diffuse large B-cell lymphoma treated with R-CHOP. Blood Res , 49(3), 162-9 (2014) doi:10.5045/br.2014.49.3.162 C. Dong: Cytokine Regulation and Function in T Cells. Annu Rev Immunol , 39, 51-76 (2021) doi:10.1146/annurev-immunol-061020-053702 Q. Shang, X. Yu, Q. Sun, H. Li, C. Sun and L. Liu: Polysaccharides regulate Th1/Th2 balance: A new strategy for tumor immunotherapy. Biomed Pharmacother , 170, 115976 (2024) doi:10.1016/j.biopha.2023.115976 O. Wurtz, M. Bajenoff and S. Guerder: IL-4-mediated inhibition of IFN-gamma production by CD4+ T cells proceeds by several developmentally regulated mechanisms. Int Immunol , 16(3), 501-8 (2004) doi:10.1093/intimm/dxh050 A. Basu, G. Ramamoorthi, G. Albert, C. Gallen, A. Beyer, C. Snyder, G. Koski, M. L. Disis, B. J. Czerniecki and K. Kodumudi: Differentiation and Regulation of T(H) Cells: A Balancing Act for Cancer Immunotherapy. Front Immunol , 12, 669474 (2021) doi:10.3389/fimmu.2021.669474 T. Mori, R. Takada, R. Watanabe, S. Okamoto and Y. Ikeda: T-helper (Th)1/Th2 imbalance in patients with previously untreated B-cell diffuse large cell lymphoma. Cancer Immunol Immunother , 50(10), 566-8 (2001) doi:10.1007/s00262-001-0232-8 M. Dehghani, M. Ramzi, M. Kalani, H. Golmoghaddam and N. Arandi: Higher Peripheral Blood IFN-gamma-/IL-4+ Th2 Lymphocytes Are Associated with Lower Rate of Relapse in Patients with Lymphoma. Immunol Invest , 51(2), 452-463 (2022) doi:10.1080/08820139.2020.1840583 J. C. Wight, G. Chong, A. P. Grigg and E. A. Hawkes: Prognostication of diffuse large B-cell lymphoma in the molecular era: moving beyond the IPI. Blood Rev , 32(5), 400-415 (2018) doi:10.1016/j.blre.2018.03.005 A. I. Cioroianu, P. I. Stinga, L. Sticlaru, M. D. Cioplea, L. Nichita, C. Popp and F. Staniceanu: Tumor Microenvironment in Diffuse Large B-Cell Lymphoma: Role and Prognosis. Anal Cell Pathol (Amst) , 2019, 8586354 (2019) doi:10.1155/2019/8586354 C. Jimenez-Cortegana, N. Palazon-Carrion, A. Martin Garcia-Sancho, E. Nogales-Fernandez, F. Carnicero-Gonzalez, E. Rios-Herranz, F. de la Cruz-Vicente, G. Rodriguez-Garcia, R. Fernandez-Alvarez, A. Rueda Dominguez, M. Casanova-Espinosa, N. Martinez-Banaclocha, J. Guma-Padro, J. Gomez-Codina, J. Labrador, A. Salar-Silvestre, D. Rodriguez-Abreu, L. Galvez-Carvajal, M. Provencio, M. Sanchez-Beato, M. Guirado-Risueno, P. Espejo-Garcia, M. Lejeune, T. Alvaro, V. Sanchez-Margalet, L. de la Cruz-Merino, G. Spanish Lymphoma Oncology, C. the Spanish Group for Immunobiotherapy of, G. Spanish Lymphoma Oncology, C. the Spanish Group for Immunobiotherapy of, G. Spanish Lymphoma Oncology and C. the Spanish Group for Immunobiotherapy of: Circulating myeloid-derived suppressor cells and regulatory T cells as immunological biomarkers in refractory/relapsed diffuse large B-cell lymphoma: translational results from the R2-GDP-GOTEL trial. J Immunother Cancer , 9(6) (2021) doi:10.1136/jitc-2020-002323 Tables Table1. The general clinical features Clinical Characteristics Total patients (94) Median age(range) 63 (9-89) >60 years 51 (54.2%) Male 45 (47.8%) ECOG>2 14 (14.8%) B symptoms 39 (41.4%) Ann Arbor stage 1-2 26 (27.7%) 3-4 68 (72.3%) LDH>250U/L 56 (58.9%) Extra nodal involvement>2 24 (35.7%) IPI score 0-2 44 (46.8%) 3-5 50 (53.2%) Bone marrow involvement 27 (28.7%) Treatment CHOP-like 78 (82.9%) R2 16 (17.1%) CD4+T cell(/ul)≤263.5 17 (17.8%) CD8+T cell(/ul)≤245 24 (25.2%) B cell(/ul)≤39.95 30 (31.5%) NK cell(/ul)≤10.7 2 (2.1%) IL-4(pg/ml)≤0.72 69 (73.4%) IL-6(pg/ml)≤10.31 43 (46.2%) IL-10(pg/ml)≤3.51 46 (48.9%) IL-17(pg/ml)≤6.33 51 (53.6%) IL-12(pg/ml)≤0.72 17 (17.8%) IFN-γ(pg/ml)≤9.36 25 (26.3%) IFN-γ/IL-4≤2.66 54 (56.3%) IL-12/IL-17≤0.72 26 (32.3%) Abbreviations: ECOG, Eastern Cooperative Oncology Group; LDH, lactate dehydrogenase; IPI, International Prognostic Index; β2-MG, β2-microglobulin; IL, interleukin. Table2. Univariate analysis of OS and PFS. 2.1 The general clinical characteristics Characteristic PFS OS p HR 95.0% CI p HR 95.0% CI Age>60 years .019 2.316 (1.151-4.659 .028 2.147 (1.087-4.241) Gender .592 1.195 (.623-2.290) .570 1.208 (.629-2.323) ECOG>2 .206 1.764 (.731-4.254) .224 1.727 (.716-4.164) IPI score≥3 .016 2.252 (1.165-4.356) .039 1.987 (1.036-3.810) Ann Arbor(12/34） .278 1.546 (.704-3.397) .309 1.504 (.685-3.301) Treatment 0.59 2.117 (.973-4.608) .064 2.052 (0.960-2.384) B symptoms .469 1.276 (.660-2.465) .669 1.154 (.598-2.228) Extra nodal involvement>2 .052 1.914 (.993-3.690) .100 1.721 (.901-3.289) Bone marrow Involvement .577 1.218 (.610-2.431) .651 1.173 (.588-2.340) Hb>100g/L .320 .670 (305-1.475) .518 .770 (.349-1.701) LDH>250U/L .446 1.310 (.654-2.621) .442 1.313 (.656-2.627) Alb>35g/L .068 .548 (.287-1.046) .100 .580 (.303-1.110) β2-MG>2.3mg/L .037 2 .469 (1.057-5.766) .077 2.141 (.920-4.978) CRP> mg/L .844 1.101 (.421-2.878) .930 1.044 (.401-2.720) Abbreviations: ECOG, Eastern Cooperative Oncology Group; IPI, International Prognostic Index; Hb, haemoglobin; LDH, lactate dehydrogenase; Alb, lbumin; β2-MG, β2-microglobulin; CRP, C-reactive protein. 2.1 The general clinical characteristics Characteristic PFS OS p HR 95.0% CI p HR 95.0% CI CD4+T cell≤263.5/ul .041 .466 (.224-968) .044 .473 (.228-.981) CD8+T cell≤245/ul .004 .363 (.184-.718) .025 .465 (.237-.909) B cell ≤39.95/ul .140 1.764 (.830-3.749) .158 1.721 (.810-3.656) NK cell ≤10.7/ul .553 20.813 (.001-471244.026) .475 .044 (.000-234.747) IL-6≤10.31pg/ml .084 1.833 (.923-3.639) .106 .571 (.290-1.126) IL-4≤0.72 pg/ml .127 2.095 (.810-5.422) .239 .609 (.266-1.392) IL-10≤3.51pg/ml .339 1.405 (.700-2.818) .080 .540 (.270-1.077) INF-γ≤ 9.36pg/ml .015 .428 (.217-.845) .013 .421 (.213-.832) IL-17≤ 6.33pg/ml .664 1.154 (.604-2.206) .589 .836 (.437-1.600) IL-12 ≤0.72pg/ml .129 2.519 (.765-8.291) .100 .369 (.112-1.212) IFN-γ/IL-4≤2.66 .001 .315 (.164-.606) .000 .304 (.158-.584) IL-12/IL-17 ≤0.72 .037 2.001 (1.044-3.835) .030 2.056 (1.074-3.937) Table3. multifactorial analysis of OS and PFS. Characteristic PFS OS p HR 95.0% CI p HR 95.0% CI Age>60 years 0.74 2.408 (.917-6.323) .030 2.918 (1.107-7.688) IPI score≥3 .283 1.717 (.640-4.601) .440 1.469 (.553-3.900) β2-MG>2.3mg/L .655 1.285 (.428-3.862) .912 1.065 (.351-3.228) CD4+T cell≤263.5/ul .037 .346 (.128-.936) .022 .312 (.116-.844) CD8+T cell≤245/ul .019 .305 (.113-.823) .074 .408 (.153-1.090) IFN-γ/IL-4≤2.66 .001 .222 (.090-.545) .001 .226 (.092-.555) IL-12/IL-17≤0.72 .882 1.071 (.436-2.63) .822 1.107 (.458-2.678) Abbreviations: IPI, International Prognostic Index; β2-MG, β2-microglobulin; IL, interleukin. Additional Declarations No competing interests reported. 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ratio \u003cstrong\u003e(G)\u003c/strong\u003e, and IL-12/IL-17 ratio \u003cstrong\u003e(H)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6562919/v1/05c39c20d48803f79ccc13f2.jpg"},{"id":83514243,"identity":"6b0b4885-77ec-45a5-9bc8-7a2dd0cd9770","added_by":"auto","created_at":"2025-05-27 17:52:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2012795,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‐Meier estimate of progress free survival in new diagnosed DLBCL patients according to age \u003cstrong\u003e(A)\u003c/strong\u003e, IPI \u003cstrong\u003e(B),\u003c/strong\u003e β2-MG \u003cstrong\u003e(C)\u003c/strong\u003e, CD4+ T cell count \u003cstrong\u003e(D)\u003c/strong\u003e, CD8+ T cell count \u003cstrong\u003e(E)\u003c/strong\u003e, IFN-γ level \u003cstrong\u003e(F)\u003c/strong\u003e, IFN-γ/IL-4 ratio \u003cstrong\u003e(G)\u003c/strong\u003e, and IL-12/IL-17 ratio \u003cstrong\u003e(H)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6562919/v1/699326d555ad3eb61602736e.jpg"},{"id":83514244,"identity":"406f0ab2-c008-4606-9111-279f3d42a707","added_by":"auto","created_at":"2025-05-27 17:52:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":969667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Clinical responses of DLBCL patients with high IFN-γ/IL-4 ratio vs. low IFN-γ/IL-4 ratio. The percentages of patients with a complete (CR) or partial (PR) clinical response, stable disease (SD), and progressive disease (PD) are presented. \u003cstrong\u003e(B) \u003c/strong\u003eDynamics of IFN-γ/IL-4 ratio in DLBCL patient occurred with death or long survival after initial diagnosis (T0), 2 cycles of chemotherapy (T2), 4 cycles of chemotherapy (T4), 6 cycles of chemotherapy (T6), ****p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6562919/v1/9c2168c783bfdc8d5ec20a8b.jpg"},{"id":83514245,"identity":"5af2a326-81f5-4e5e-bc6a-37ad1b8316ec","added_by":"auto","created_at":"2025-05-27 17:52:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1234317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003ePlotting the ROC curves for the newly created IRPS model vs. the IPI model, the AUC values for IRS and IPI were 0.7295 and 0.6095 respectively (95CI%:0.4974 to 0.7264).\u003cstrong\u003e (B) \u003c/strong\u003eKaplan-Meier curves of OS were plotted according to the IRPS.\u003cstrong\u003e (C) \u003c/strong\u003eKaplan-Meier curves of PFS were plotted according to the IRPS.\u003cstrong\u003e \u003c/strong\u003eAUC, area under the curve.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6562919/v1/5d3ed8c2724c8a1e3190b445.jpg"},{"id":83514246,"identity":"dea51a86-4042-414b-a146-05f98be5c0fd","added_by":"auto","created_at":"2025-05-27 17:52:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1871691,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival according to the IRPS model subdivided IPI. \u003cstrong\u003e(A, B) \u003c/strong\u003eThe Kaplan-Meier curves for OS and PFS in IPI-defined low-risk patients (IRPS Low vs. High). \u003cstrong\u003e(C, D) \u003c/strong\u003eThe Kaplan-Meier curves for OS and PFS in IPI-defined intermediate-risk patients (IRPS Low vs. High). \u003cstrong\u003e(E, F) \u003c/strong\u003eThe Kaplan-Meier curves for OS and PFS in IPI-defined high-risk patients (IRPS Low vs. High).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6562919/v1/6e0f97a5bf0ffa1df5df9919.jpg"},{"id":83514799,"identity":"1c52079e-6822-4eef-98eb-f6ff58597a1b","added_by":"auto","created_at":"2025-05-27 18:00:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1199682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eReceiver-operator characteristic curves of the IRPS model and IPI in the validation cohort from the First Affiliated Hospital of Zhengzhou University. \u0026nbsp;\u003cstrong\u003e(B, C) \u003c/strong\u003eThe K-M curves of OS and PFS were plotted for risk groups defined by the IRPS model in the validation cohort.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6562919/v1/6a8c4c14a4dc3b68bd1548ff.jpg"},{"id":90344949,"identity":"047246af-61cb-4094-b4b1-5e2ddb1035dd","added_by":"auto","created_at":"2025-09-01 16:08:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5717215,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6562919/v1/54935626-4fe2-4ec4-a64a-1c12f3f90dd4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of IFN-γ/IL-4 ratio as a new predictor and the significance of its based immune related prognostic model in diffuse large B-cell lymphoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is one of the most common types of aggressive lymphoma, which occupies approximately 30\u0026ndash;40% of all adult non-Hodgkin's lymphoma (NHL). Patients often show a rapidly growing tumour mass in one or more lymph nodes or extra-nodal tissues(1, 2). With the use of rituximab combined immunochemotherapy, the prognosis of patients has been greatly improved. Nevertheless, DLBCL is highly heterogeneous both clinically and prognostically, with nearly 40% of patients becoming refractory or relapsing(3). Therefore, it becomes particularly important to assess the accurate prognostic risk of each newly diagnosed patient and design the appropriate initial treatment. Currently, the most commonly used clinical predictive model is the International Prognostic Index (IPI), which involves five parameters: age\u0026thinsp;\u0026gt;\u0026thinsp;60, elevated serum lactate dehydrogenase (LDH), Eastern Cooperative Oncology Group (ECOG) performance status\u0026thinsp;\u0026ge;\u0026thinsp;2, Ann Arbor stage III or IV and number of involved extranodal sites\u0026thinsp;\u0026ge;\u0026thinsp;2. For patients with age\u0026thinsp;\u0026lt;\u0026thinsp;60, the aa-IPI score was derived. With the advent of the rituximab era, the IPI score has diminished and R-IPI, NCCN-IPI were proposed, which shows superior prediction in outcome of DLBCL patients treated with standard immunochemotherapy(4\u0026ndash;6). In spite of the fact that these scoring systems provided better prognostic guidance, novel biomarkers are still needed to better identify high-risk patients who could benefit from more aggressive therapeutic approaches.\u003c/p\u003e \u003cp\u003eDLBCL is a result of malignant B-cell development, which potentially involves the systemic immunology and the tumor microenvironment, including abnormal immune cells constitution and the aberrant immune responses(7, 8). For examples, a large study has shown that the absolute blood peripheral CD4\u0026thinsp;+\u0026thinsp;T cells is an strong independent poor predictor of survival in R-CHOP-treated patients with DLBCL(9). Low circulating CD4\u0026thinsp;+\u0026thinsp;T-cell levels predict poorer PFS outcomes in DLBCL(10). A low ratio of circulating CD8\u0026thinsp;+\u0026thinsp;T Lymphocytes to M- myeloid-derived suppressor cells (MDSCs) serves as a poor prognostic factor for both PFS and OS in treatment-na\u0026iuml;ve DLBCL patients(11). Lymphocyte to monocyte ratio (LMR) reflecting both the immune status in the peripheral blood as well as in the tumor micro-environment has been suggested as an effective prognostic factor for predicting clinical survival in DLBCL patients (12). In addition, cytokines may serve as indicators of tumor immune status and modulate the immune system during lymphoma progression. For examples, elevated pretreatment serum cytokines have been reported to associated with an increased likelihood of disease relapse and an inferior survival in patients with DLBCL (13). Serum levels of IL-6 and IL-10 positively correlated with high IPI score, bone marrow involvement, elevated LDH/β2-microglobulin (β2-MG), short PFS and OS in patients with DLBCL (14). Although numerous studies have demonstrated that several types of lymphocytes and cytokines could be used to predict prognosis of DLBCL, the clinical significance of the combination of these immunoregulation components remains indistinct.\u003c/p\u003e \u003cp\u003eTherefore, in this study, we retrospectively analyzed the count of peripheral lymphocyte subtypes and cytokines levels in 94 newly diagnosed DLBCL patients and explored their impact on the prognosis, aiming to discover better predictors and build a new prognostic model correlated with DLBCL.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003eThis retrospective study collected data from newly diagnosed DLBCL patients at the Henan Provincial People\u0026rsquo;s Hospital, Zhengzhou University People\u0026rsquo;s Hospital, between January 2017 and December 2023. The histological classification of DLBCL was performed according to the guidelines by the World Health Organization. The inclusion criteria were as follows: (1) newly diagnosed DLBCL patients; (2) at least 4 cycles of chemotherapy were completed. The exclusion criteria were as follows: (1) patients with other tumors; (2) patients with special types of lymphoma (primary central nervous system lymphoma, primary mediastinal large B-cell lymphoma, and transformed DLBCL). 94 patients with DLBCL, including 49 (53.2%) females and 45 (47.8%) males, were enrolled. The median age was 63 years, and the follow-up date was up to July 2024. A validation cohort included 47 patients with newly diagnosed DLBCL from the First Affiliated Hospital of Zhengzhou University during the same period. This research was carried out with approval from the Ethics Committee of the Henan Provincial People\u0026rsquo;s Hospital (Zhengzhou University People\u0026rsquo;s Hospital Zhengzhou University People\u0026rsquo;s Hospital) and the First Affiliated Hospital of Zhengzhou University and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eThe clinical baseline characteristics were collected from medical records, including age, gender, ECOG score, IPI score, B-symptoms (fever, night sweats, or weight loss), Ann Arbor stage, lactate dehydrogenase (LDH) level, extra-nodal involvement, IPI score, and treatment regimen. Likewise, laboratorial data were available from the hospital-based laboratory, such as serum β2-microglobulin (serum β2-MG), hemoglobin (HB), C-reactive protein (CRP), bone marrow involvement, lymphocyte subsets (CD 4\u0026thinsp;+\u0026thinsp;T cell, CD8\u0026thinsp;+\u0026thinsp;T cell, B cell, and NK-cell counts), serum cytokines (IL-4, IL-6, IL-10, IL-17, IL-12, IFN-γ) and other laboratory test results were analyzed, and CT, MRI, PET-CT and other imaging results were collected for evaluation of their efficacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Patients\u0026rsquo; follow-up\u003c/h2\u003e \u003cp\u003eThe follow-up data were obtained through electronic medical records and telephone interviews. Progression-free survival (PFS) was calculated from the date from diagnosis until disease progression, relapse, death, or the end of follow-up, while overall survival (OS) was defined as the interval between the date of diagnosis and the death of patient or the end of follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e \u003cp\u003eComparisons between groups were made using the Mann-Whitney U test, receiver operating characteristic (ROC) was used to define optimal cut-off values for lymphocyte subtypes (CD4\u0026thinsp;+\u0026thinsp;T cell, CD8\u0026thinsp;+\u0026thinsp;T cell, B cell, NK cell counts) and cytokines (IL-4, IL-6, IL-10, IL-17, IL-12, IFN-γ/IL-4, IL-12/IL-17). Univariate logistic regression and multivariate logistic regression were performed using Cox proportional risk regression models to assess prognostic variables for survival analysis. Kaplan\u0026ndash;Meier survival curves and log-rank tests were used to estimate survival time. Variables with statistical significance in the multivariate analysis were selected to build a new predictive model. ROC curves were used to calculate the AUC values when comparing the performance of different prognostic scores. The new model was compared with the existing prognostic model by plotting the receiver operating characteristic curve (ROC) to evaluate its predictive value and applicability. SPSS statistical software package 20.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism version 8.0.1 (GraphPad Software, CA, USA) were used for statistical analyses. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u0026nbsp; \u0026nbsp;\u003cstrong\u003e3.1 General clinical characteristics of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe newly diagnosed DLBCL patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Overall, 45 (47.8%) male and 49 (53.2%) female patients diagnosed with DLBCL were included in this study and their characteristics are presented in Table 1.\u0026nbsp;The median age at diagnosis was 63 (9–89) years and 51 (54.2%) were \u0026gt;60 years. 14 (14.8%) patients had ECOG PS score \u0026gt;2 and 39 (41.4%) had B symptoms. Regarding Ann Arbor stage, 26 (27.7%) patients had stage I or II, and 68 (72.3%) had stage III or IV. LDH level was elevated in 56 (58.9%) cases. \u0026nbsp;A total of 24 (35.7%) cases had ≥2 extranodal lesions and bone marrow invasion was involved in 27 (28.7%) cases. The patients were stratified into 2 risk-predicting groups by IPI value: \u0026nbsp;44 (46.8%) cases in low to intermediate risk group (0–2) and 50 (53.2%) cases in intermediate to high risk group (3–5). A total of 78 (82.9%) patients were treated with R-CHOP (rituximab,\u0026nbsp;cyclophosphamide, doxorubicin, vincristine, prednisone) or R-CHOP-like immunochemotherapy and 26 (17.1%) cases were treated with\u0026nbsp;R2 (rituximab, lenalidomide) regimen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 \u0026nbsp; Prognostic factors for OS and PFS of the newly diagnosed DLBCL patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Clinical indicators with significant impact on OS and PFS in the univariable analyses were presented in Table 2. The univariate analysis revealed that a age \u0026gt;60 years, IPI score ≥3, CD4+ T ≤263.5/ul, CD8+ T≤245/ul , IFN-γ/IL-4 ratio≤2.66, IFN-γ≤9.36pg/ml, IL-12/IL-17 ratio≥0.72 were all high-risk indicators affecting patients’ OS and PFS. However, a β2-MG level \u0026gt;2.3 mg/L was identified as a high-risk factor adversely affecting patients' PFS, but demonstrated minimal impact on OS. The Kaplan-Meier analysis presented in Figures 1 and 2 revealed significant variables related to inferior OS or PFS. To further determine the independent prognostic values, subsequent multivariate survival analysis was performed. As shown in Table 3, age\u0026gt;60 (p=0.030), CD4+ T ≤263.5/ul (p=0.022), and IFN-γ/IL-4 ratio≤2.66 (p=0.001) were significant prognostic factors for worse OS. CD4+ T ≤263.5/ul (p=0.037), CD8+ T≤245/ul (p=0.019), and IFN-γ/IL-4 ratio≤2.66 (p=0.001) were identified as predictors for inferior PFS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Relationship of IFN-γ/IL-4 to efficacy of treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Given that the IFN-γ/IL-4 ratio emerged as the strongest prognostic factor in survival analysis, we further investigated its value in efficacy assessment. Using the cut-off value of 2.66 at diagnosis, patients were divided into high IFN-γ/IL-4 (63/94, 67.0%) and low IFN-γ/IL-4 (31/94, 32.9%) groups. Among the high IFN-γ/IL-4 group, 22/63 (34.9%) patients achieved complete response (CR), 18/63 (28.6%) patients achieved PR, 8/63 (12.7%) patients achieved SD, and 15/63 (23.8%) patients experienced progressive disease (PD). In contrast, in the low IFN-γ/IL-4 \u0026nbsp;group, only (7/31, 22.6%) patients achieved CR, 5/31 (16.1%) patients achieved PR, 4/31 (12.9%) patients experienced SD, and 15/31 (48.4%) developed PD (Figure 3A). Therefore, these results showed that a significant higher overall response rate (ORR) was observed in the higher IFN-γ/IL-4 group at diagnosis compared to the lower IFN-γ/IL-4 group. To evaluate IFN-γ/IL-4 dynamic changes during treatment, we analyzed 61 patients with complete data, measuring peripheral plasma cytokines at initial diagnosis (n=61), after 2 cycles (n=61), 4 cycles (n=51), and 6 cycles (n=46) of chemotherapy. As shown in Figure 3B, patients who survived maintained significantly higher IFN-γ/IL-4 ratios than non-survivors throughout all chemotherapy cycles (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Construction of a new\u003c/strong\u003e \u003cstrong\u003eimmune-related prognostic score and integration with the IPI score in newly diagnosed DLBCL patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Using statistically significant predictors of shorter OS identified in multivariate analyses, we constructed a novel immune-related prognostic score (IRPS) assigning 1 point each for age \u0026gt;60 years, CD4+ T cells ≤263.5/μL, and IFN-γ/IL-4 ratio ≤2.66 with score range from 0–3. The new prognostic model stratified patients into low-risk (0-1 scores) and high-risk (2-3 scores) groups, with the low-risk group demonstrating significantly superior OS (p\u0026lt;0.0001) and PFS (p\u0026lt;0.0001) compared to the high-risk group (Figure 4A-B). Next, we performed ROC analysis to compare the sensitivity and specificity of survival prediction between the newly established IRPS model and the IPI model. The results that the IRPS model showed superior discriminative ability for OS prediction compared to the IPI score, with AUC values of 0.7295 and 0.6095, respectively (Figure 4C). \u0026nbsp;To further evaluate whether IRPS could refine IPI risk stratification, we reclassified patients across all IPI risk categories (low-risk: 0-1; intermediate-risk: 2-3; high-risk: 4-5) using the IRPS score. As shown in Figure 5, our novel model could effectively discriminate both OS and PFS across all IPI risk stratifications (low-risk group, p\u0026lt;0.01; intermediate-risk group, p\u0026lt;0.01; high-risk group, p\u0026lt;0.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Validation of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003enew\u003c/strong\u003e \u003cstrong\u003eimmune-related prognostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo confirm the predictive accuracy of this novel prognostic model patients with newly diagnosed DLBCL, we collected a validation cohort from the First Affiliated Hospital of Zhengzhou University during the same study period. A total of 47 patients with complete data meeting the inclusion criteria were enrolled, comprising 27 males (47.8%) and 20 females (53.2%) with a median age of 55 years and followed through July 2024. Survival probabilities were analyzed using Kaplan-Meier curves after stratifying patients into high- and low-risk groups based on IRPS scoring criteria. The results revealed statistically significant differences in both OS (Figure 6A; p\u0026lt;0.001) and PFS (Figure 6B; p\u0026lt;0.001) between high- and low-risk groups stratified by the IRPS model, confirming its robust prognostic validity. Notably, the IRPS model showed non-inferior prognostic performance relative to the IPI model, achieving a slightly higher AUC value (0.7941 vs 0.7818) in the validation cohort (Figure 6C).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDespite rapid therapeutic advances in DLBCL, patient outcomes remain highly heterogeneous, influenced by clinical factors, biologic and molecular subtypes, and other undiscovered factors (15). Dysfunctional anti-cancer immunity including the systemic immune response and tumor infiltrating immune response contributes to DLBCL pathogenesis and progression. Thereinto, low absolute lymphocyte counts, and aberrate peripheral lymphocyte subset distributions serve as indicators of systemic immune status and predict poor prognosis in DLBCL patients, while tumor infiltrating lymphocytes abnormity represents the local immune microenvironment (16, 17). Generally speaking, lymphocyte subsets are typically composed of CD4 + T cells, CD8 + T cells, B lymphocytes, and natural killer (NK) cells (18). CD4 + helper T cells promote the activation of cytotoxic CD8 + T cells and secrete a variety of cytokines that mediate anti-tumor immune responses, thereby playing a critical role in the anti-tumour immune response. CD8 + T cells are cytotoxic T cells that directly lyse tumor cells by releasing cytotoxins containing perforin and granzyme (19, 20).\u003c/p\u003e \u003cp\u003eSeveral studies of DLBCL have found that CD4 + T cells are an independent prognostic factor and that higher circulating and local intratumoral CD4 + T cells are associated with improved clinical prognosis (7, 10, 21). Consistent with these studies, our study similarly demonstrated that CD4 + T cell counts in peripheral blood at initial diagnosis was positively correlated with both OS and PFS. It was reported that DLBCL with high density of CD8 + infiltrating T cells seemed to have improved outcome. For examples, Rajnai H et al. found that the number of tumour-infiltrating CD8 + T was an independent favorable prognostic marker for survival in primary diffuse large B-cell lymphoma of bone (22). Shi and colleagues identified that high CD8 + T-cell infiltration density in tumor tissues correlated with prolonged progression-free survival (PFS) in Chinese DLBCL patients, independent of rituximab treatment status (23). Gergely et al. proposed that patients with low pretreatment CD8 + T cell counts in B-cell non-Hodgkin's lymphoma had significantly lower overall survival rates (24). Nevertheless, the prognostic significance of peripheral blood CD8 + T cell counts remains unexplored in broader DLBCL populations. Surprisingly, in our study we further observed that elevated peripheral blood CD8 + T cell counts at initial diagnosis were significantly associated with improved PFS and OS. Given that peripheral blood T cells correlate positively with tumor-infiltrating T cells in DLBCL and considering CD8 + T cells serve as precursors for cytotoxic T lymphocytes (CTLs) (25), we hypothesize that circulating CD8 + T cell depletion may result in inadequate CTL-mediated tumor cytotoxicity, thereby adversely affecting DLBCL patient survival. NK cells, serving as surrogate markers of immune status, demonstrate significant associations with clinical outcomes in DLBCL patients during the rituximab era (26). However, our analysis revealed no statistically significant correlation between peripheral NK-cell counts and survival outcomes in DLBCL patients. Despite limited data on peripheral B cells in DLBCL, Rusak M et al. discovered that diagnostic B cell counts lacked clinical value, but post-R-CHOP B-cell expansion predicted inferior treatment outcomes (17). Although post-treatment B cell levels were not analyzed in our study, we similarly demonstrated that baseline B cell counts at initial diagnosis showed no prognostic significance in DLBCL patients.\u003c/p\u003e \u003cp\u003eAs we know, T lymphocytes function as primary effector cells in mediating cellular immunity, with cytokine secretion representing a fundamental mechanism of their biological activity and immune regulation. According to the cytokine expression profiles and immune regulatory functions, CD4 + T cells differentiate into specialized subsets, including Th1, Th2, Th17, and T follicular helper (Tfh) cells, with Th1 and Th2 representing two pivotal effector lineages (27). Th1 cells exert direct antitumor effects by secreting cytokines such as IFN-γ, IL-2, and TNF-α, which induce tumor cell apoptosis, senescence, and functional inactivation. In contrast, Th2 cells promote tumour growth and metastasis by activating STAT6 through IL-4 (28–30). Thus, Th1/Th2 imbalance is a common mechanism for immune escape and is closely related to cancer development and prognosis. Abnormalities in the CD4 + T cell compartment, including the Th1 and Th2 subsets and related cytokines, have been shown to affect the occurrence of non-Hodgkin lymphoma (7). However, whether Th1/Th2 imbalance at diagnosis impacts prognosis in newly diagnosed DLBCL patients remains largely undetermined. Given that Th1 response activation is primarily driven by IFN-γ while Th2 differentiation depends on IL-4, we tried to use the peripheral blood IFN-γ/IL-4 ratio as a surrogate for Th1/Th2 balance and investigated its prognostic role in DLBCL. Here, our study identified the IFN-γ/IL-4 ratio as an independent significant prognostic biomarker of both OS and PFS in DLBCL. Notably, we stratified patients into high- and low-ratio groups using an optimal cutoff value for survival analysis. The efficacy assessment revealed that patients with high IFN-γ/IL-4 ratios showed superior treatment responses, with significantly increased CR/PR rates compared to low-ratio counterparts. In addition, the longitudinal analysis of IFN-γ/IL-4 ratio dynamics during treatment revealed sustained elevation in patients with favorable prognostic outcomes. Mori T et al. employed flow cytometric analysis of intracellular IFN-γ/IL-4 to evaluate Th1/Th2 balance, demonstrating Th2 polarization in untreated DLBCL patients versus Th1 dominance in complete remission cases (31). This conclusion together with our findings demonstrate that high IFN-γ/IL-4 levels (reflecting Th1 polarization) correlate with enhanced treatment responses, reduced relapse rates, and superior clinical outcomes, suggesting their potential involvement in DLBCL pathogenesis and progression. However, Mehdi et al. demonstrated in their cohort study of 47 Hodgkin's lymphoma and 48 DLBCL patients that higher peripheral blood IFN-γ-/IL-4 + Th2 lymphocytes might be associated with a favorable prognosis like lower rate of relapse (32). Therefore, our future study might investigate the level of IFN-γ/IL-4 in patients with sustained remission versus relapse to further determine its prognostic significance.\u003c/p\u003e \u003cp\u003eDue to the marked heterogeneity in DLBCL patient survival outcomes, comprehensive risk assessment prior to treatment initiation is essential for optimal therapeutic strategy selection. Currently, the IPI, R-IPI, and NCCN-IPI are the most commonly employed prognostic scoring systems in DLBCL. However, all 3 scoring systems failed to identify a patient subgroup with long-term survival clearly \u0026lt; 50% in the rituximab era (4, 33). Advances in molecular profiling have increasingly highlighted the critical roles of both systemic and tumor microenvironmental immunity in DLBCL pathogenesis, driving recent proposals to incorporate immune-related biomarkers into prognostic scoring systems (8, 34, 35). In this study, using multivariate regression analysis of clinical (age) and immunologic parameters (CD4 + T cell count, IFN-γ/IL-4 ratio), we established an immune-related prognostic score (IRPS) that effectively classifies DLBCL patients into low-risk group (0–1 scores) and high-risk group (2–3 scores). Encouragingly, validation at another independent center replicated the model’s predictive performance. What’s more surprisingly, ROC curve analysis demonstrated superior prognostic performance of our novel model compared to the IPI scoring system. Additionally, the IRPS model could further substratified each IPI risk category (low/intermediate/high) patients into distinct low- and high-risk subsets. Integrating the IRPS model with conventional IPI scoring improves risk stratification by detecting unfavorable-prognosis patients originally classified as IPI low-intermediate risk, thereby offering timely opportunities to apply appropriate therapeutic interventions to potentially enhancing survival and prognosis. Moreover, these clinical indicators are economical and readily available in clinical practice, making their integration into daily clinical practice feasible.\u003c/p\u003e \u003cp\u003eOur study has several limitations. Firstly, the single-center design and relatively small sample size may limit generalizability, future multi-center validation with larger cohorts is needed to confirm these findings. Secondly, due to the limited sample size in the validation cohort, we were unable to demonstrate the prognostic superiority of the IRPS model over the IPI scoring system. Thirdly, as a retrospective analysis, our findings warrant prospective confirmation to assess multifactorial interactions more rigorously. Finally, the use of plasma IFN-γ/IL-4 levels to represent the Th1/Th2 ratio in our research may not be accurate enough, and more studies are needed to validate the relationship between IFN-γ/IL-4 and Th1/Th2.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study demonstrates that the IFN-γ/IL-4 ratio serves as a significant independent predictor of clinical outcomes in newly diagnosed DLBCL patients. The Immune-Related Prognostic Score (IRPS) model, constructed from the IFN-γ/IL-4 ratio in conjunction with age and CD4 + T-cell counts, significantly improves IPI-based risk stratification and might lead to more accurate prognostic assessments and more optimized treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study was conducted in compliance with the Helsinki Declaration and was approved by the Medical Ethical Committees of both the Henan Provincial People\u0026rsquo;s Hospital (Zhengzhou University People\u0026rsquo;s Hospital Zhengzhou University People\u0026rsquo;s Hospital) and the First Affiliated Hospital of Zhengzhou University. Written informed consent to participate in the study was obtained from all the patients treated in the Henan Provincial People\u0026rsquo;s Hospital (Zhengzhou University People\u0026rsquo;s Hospital Zhengzhou University People\u0026rsquo;s Hospital) and the First Affiliated Hospital of Zhengzhou University.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was funded by the Youth Program of the National Natural Science Foundation of China (No. 82100222), the Henan Province Medical Science and Technology Project Constructed by the Provincial and Ministerial Departments, PR China (No. SBGJ202303005), and the Henan Province Medical Science and Technology Tackling Program Joint Co-construction Project, PR China (No. LHGJ20230023).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePan Zhou and Yunmeng Zhou collected, assembled the data and wrote the manuscript. Liu Yang and Chao Liu participated in the work of follow-up. Suqiong Zuo and Xiaohang Pei participated in the literature review and statistical analysis. Rongjun Ma and Yuqing Chen supervised and reexamined the search process. Xiaoli Yuan and Zunmin Zhu conceived, designed the study and revised the manuscript. All authors wrote and approved of the article and are accountable for publication.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQ. Cui, W. Tan, B. Song, R. J. Peng, L. Wang, R. Dorajoo, K. P. Ng, G. W. Lin, W. Y. Au, R. H. S. Liang, C. C. Khor, Q. L. Zhang, J. N. Foo, S. P. Li, F. R. Zhang, X. J. Zhang, X. Q. Yu, Q. Lan, S. Chanock, W. H. Jia, S. T. Lim, W. Y. Li, N. Rothman, J. X. Bei, J. Liu, D. Lin and J. J. Liu: Genetic susceptibility of diffuse large B-cell lymphoma: a meta genome-wide association study in Asian population. \u003cem\u003eLeukemia\u003c/em\u003e, 39(3), 694-702 (2025) doi:10.1038/s41375-024-02503-4\u003c/li\u003e\n\u003cli\u003eM. Martelli, A. J. Ferreri, C. Agostinelli, A. Di Rocco, M. Pfreundschuh and S. A. Pileri: Diffuse large B-cell lymphoma. \u003cem\u003eCrit Rev Oncol Hematol\u003c/em\u003e, 87(2), 146-71 (2013) doi:10.1016/j.critrevonc.2012.12.009\u003c/li\u003e\n\u003cli\u003eR. Vaidya and T. E. Witzig: Prognostic factors for diffuse large B-cell lymphoma in the R(X)CHOP era. \u003cem\u003eAnn Oncol\u003c/em\u003e, 25(11), 2124-2133 (2014) doi:10.1093/annonc/mdu109\u003c/li\u003e\n\u003cli\u003eA. S. Ruppert, J. G. Dixon, G. Salles, A. Wall, D. Cunningham, V. Poeschel, C. Haioun, H. Tilly, H. Ghesquieres, M. Ziepert, J. Flament, C. Flowers, Q. Shi and N. Schmitz: International prognostic indices in diffuse large B-cell lymphoma: a comparison of IPI, R-IPI, and NCCN-IPI. \u003cem\u003eBlood\u003c/em\u003e, 135(23), 2041-2048 (2020) doi:10.1182/blood.2019002729\u003c/li\u003e\n\u003cli\u003eL. H. Sehn, B. Berry, M. Chhanabhai, C. Fitzgerald, K. Gill, P. Hoskins, R. Klasa, K. J. Savage, T. Shenkier, J. Sutherland, R. D. Gascoyne and J. M. Connors: The revised International Prognostic Index (R-IPI) is a better predictor of outcome than the standard IPI for patients with diffuse large B-cell lymphoma treated with R-CHOP. \u003cem\u003eBlood\u003c/em\u003e, 109(5), 1857-61 (2007) doi:10.1182/blood-2006-08-038257\u003c/li\u003e\n\u003cli\u003eZ. Zhou, L. H. Sehn, A. W. Rademaker, L. I. Gordon, A. S. Lacasce, A. Crosby-Thompson, A. Vanderplas, A. D. Zelenetz, G. A. Abel, M. A. Rodriguez, A. Nademanee, M. S. Kaminski, M. S. Czuczman, M. Millenson, J. Niland, R. D. Gascoyne, J. M. Connors, J. W. Friedberg and J. N. Winter: An enhanced International Prognostic Index (NCCN-IPI) for patients with diffuse large B-cell lymphoma treated in the rituximab era. \u003cem\u003eBlood\u003c/em\u003e, 123(6), 837-42 (2014) doi:10.1182/blood-2013-09-524108\u003c/li\u003e\n\u003cli\u003eY. Kusano, M. Yokoyama, Y. Terui, N. Nishimura, Y. Mishima, K. Ueda, N. Tsuyama, H. Yamauchi, A. Takahashi, N. Inoue, K. Takeuchi and K. Hatake: Low absolute peripheral blood CD4+ T-cell count predicts poor prognosis in R-CHOP-treated patients with diffuse large B-cell lymphoma. \u003cem\u003eBlood Cancer J\u003c/em\u003e, 7(5), e561 (2017) doi:10.1038/bcj.2017.43\u003c/li\u003e\n\u003cli\u003eM. Autio, S. K. Leivonen, O. Bruck, S. Mustjoki, J. Meszaros Jorgensen, M. L. Karjalainen-Lindsberg, K. Beiske, H. Holte, T. Pellinen and S. Leppa: Immune cell constitution in the tumor microenvironment predicts the outcome in diffuse large B-cell lymphoma. \u003cem\u003eHaematologica\u003c/em\u003e, 106(3), 718-729 (2021) doi:10.3324/haematol.2019.243626\u003c/li\u003e\n\u003cli\u003eY. Kusano, M. Yokoyama, Y. Terui, N. Nishimura, Y. Mishima, K. Ueda, N. Tsuyama, H. Yamauchi, A. Takahashi, N. Inoue, K. Takeuchi and K. Hatake: Low absolute peripheral blood CD4+ T-cell count predicts poor prognosis in R-CHOP-treated patients with diffuse large B-cell lymphoma. \u003cem\u003eBlood Cancer J\u003c/em\u003e, 7(4), e558 (2017) doi:10.1038/bcj.2017.37\u003c/li\u003e\n\u003cli\u003eJ. Judd, E. Dulaimi, T. Li, M. M. Millenson, H. Borghaei, M. R. Smith and T. Al-Saleem: Low Level of Blood CD4(+) T Cells Is an Independent Predictor of Inferior Progression-free Survival in Diffuse Large B-cell Lymphoma. \u003cem\u003eClin Lymphoma Myeloma Leuk\u003c/em\u003e, 17(2), 83-88 (2017) doi:10.1016/j.clml.2016.11.005\u003c/li\u003e\n\u003cli\u003eH.-Y. Wang, F.-C. Yang, C.-F. Yang, C.-K. Tsai, P.-S. Ko, Y.-C. Liu and N.-J. Chen: Ratio of Circulating CD8+ T Lymphocytes to M-MDSCs (CD8MMR): A Novel Prognostic Predictor for Treatment-Na\u0026iuml;ve DLBCL Patients. \u003cem\u003eBlood\u003c/em\u003e, 142, 1763 (2023) \u003c/li\u003e\n\u003cli\u003eF. Gao, J. Hu, J. Zhang and Y. Xu: Prognostic Value of Peripheral Blood Lymphocyte/monocyte Ratio in Lymphoma. \u003cem\u003eJ Cancer\u003c/em\u003e, 12(12), 3407-3417 (2021) doi:10.7150/jca.50552\u003c/li\u003e\n\u003cli\u003eS. M. Ansell, M. J. Maurer, S. C. Ziesmer, S. L. Slager, T. M. Habermann, B. Link, T. E. Witzig, J. Cerhan and A. J. Novak: Pretreatment serum cytokines predict early disease relapse and a poor prognosis in diffuse large B-cell lymphoma (DLBCL) patients. \u003cem\u003eBlood\u003c/em\u003e, 116(21), 991 (2010) \u003c/li\u003e\n\u003cli\u003eC. Bao, J. Gu, X. Huang, L. You, Z. Zhou and J. Jin: Cytokine profiles in patients with newly diagnosed diffuse large B-cell lymphoma: IL-6 and IL-10 levels are associated with adverse clinical features and poor outcomes. \u003cem\u003eCytokine\u003c/em\u003e, 169, 156289 (2023) doi:10.1016/j.cyto.2023.156289\u003c/li\u003e\n\u003cli\u003eJ. L. Koff and C. R. Flowers: Prognostic modeling in diffuse large B-cell lymphoma in the era of immunochemotherapy: Where do we go from here? \u003cem\u003eCancer\u003c/em\u003e, 123(17), 3222-3225 (2017) doi:10.1002/cncr.30740\u003c/li\u003e\n\u003cli\u003eH. Hou, Y. Luo, G. Tang, B. Zhang, R. Ouyang, T. Wang, M. Huang, S. Wu, D. Li and F. Wang: Dynamic changes in peripheral blood lymphocyte subset counts and functions in patients with diffuse large B cell lymphoma during chemotherapy. \u003cem\u003eCancer Cell Int\u003c/em\u003e, 21(1), 282 (2021) doi:10.1186/s12935-021-01978-w\u003c/li\u003e\n\u003cli\u003eM. Rusak, L. Bolkun, J. Chociej-Stypulkowska, J. Pawlus, J. Kloczko and M. Dabrowska: Flow-cytometry-based evaluation of peripheral blood lymphocytes in prognostication of newly diagnosed DLBCL patients. \u003cem\u003eBlood Cells Mol Dis\u003c/em\u003e, 59, 92-6 (2016) doi:10.1016/j.bcmd.2016.04.004\u003c/li\u003e\n\u003cli\u003eD. F. LaRosa and J. S. Orange: 1. Lymphocytes. \u003cem\u003eJournal of Allergy and Clinical Immunology\u003c/em\u003e, 121(2), S364-S369 (2008) \u003c/li\u003e\n\u003cli\u003eJ. Zhu and W. E. Paul: CD4 T cells: fates, functions, and faults. \u003cem\u003eBlood\u003c/em\u003e, 112(5), 1557-69 (2008) doi:10.1182/blood-2008-05-078154\u003c/li\u003e\n\u003cli\u003eN. S. Nicholas, B. Apollonio and A. G. Ramsay: Tumor microenvironment (TME)-driven immune suppression in B cell malignancy. \u003cem\u003eBiochim Biophys Acta\u003c/em\u003e, 1863(3), 471-482 (2016) doi:10.1016/j.bbamcr.2015.11.003\u003c/li\u003e\n\u003cli\u003eC. Keane, D. Gill, F. Vari, D. Cross, L. Griffiths and M. Gandhi: CD4(+) tumor infiltrating lymphocytes are prognostic and independent of R-IPI in patients with DLBCL receiving R-CHOP chemo-immunotherapy. \u003cem\u003eAm J Hematol\u003c/em\u003e, 88(4), 273-6 (2013) doi:10.1002/ajh.23398\u003c/li\u003e\n\u003cli\u003eH. Rajnai, F. H. Heyning, L. Koens, A. Sebestyen, H. Andrikovics, P. C. Hogendoorn, A. Matolcsy and A. Szepesi: The density of CD8+ T-cell infiltration and expression of BCL2 predicts outcome of primary diffuse large B-cell lymphoma of bone. \u003cem\u003eVirchows Arch\u003c/em\u003e, 464(2), 229-39 (2014) doi:10.1007/s00428-013-1519-9\u003c/li\u003e\n\u003cli\u003eY. Shi, L. Deng, Y. Song, D. Lin, Y. Lai, L. Zhou, L. Yang and X. Li: CD3+/CD8+ T-cell density and tumoral PD-L1 predict survival irrespective of rituximab treatment in Chinese diffuse large B-cell lymphoma patients. \u003cem\u003eInt J Hematol\u003c/em\u003e, 108(3), 254-266 (2018) doi:10.1007/s12185-018-2466-7\u003c/li\u003e\n\u003cli\u003eL. Gergely, A. Vancsa, Z. Miltenyi, Z. Simon, S. Barath and A. Illes: Pretreatment T lymphocyte numbers are contributing to the prognostic significance of absolute lymphocyte numbers in B-cell non-Hodgkins lymphomas. \u003cem\u003ePathol Oncol Res\u003c/em\u003e, 17(2), 249-55 (2011) doi:10.1007/s12253-010-9306-2\u003c/li\u003e\n\u003cli\u003eF. E. Laddaga, G. Ingravallo, A. Mestice, R. Tamma, T. Perrone, E. Maiorano, D. Ribatti, G. Specchia and F. Gaudio: Correlation between circulating blood and microenvironment T lymphocytes in diffuse large B-cell lymphomas. \u003cem\u003eJ Clin Pathol\u003c/em\u003e, 75(7), 493-497 (2022) doi:10.1136/jclinpath-2020-207048\u003c/li\u003e\n\u003cli\u003eJ. K. Kim, J. S. Chung, H. J. Shin, M. K. Song, J. W. Yi, D. H. Shin, D. S. Lee and S. M. Baek: Influence of NK cell count on the survival of patients with diffuse large B-cell lymphoma treated with R-CHOP. \u003cem\u003eBlood Res\u003c/em\u003e, 49(3), 162-9 (2014) doi:10.5045/br.2014.49.3.162\u003c/li\u003e\n\u003cli\u003eC. Dong: Cytokine Regulation and Function in T Cells. \u003cem\u003eAnnu Rev Immunol\u003c/em\u003e, 39, 51-76 (2021) doi:10.1146/annurev-immunol-061020-053702\u003c/li\u003e\n\u003cli\u003eQ. Shang, X. Yu, Q. Sun, H. Li, C. Sun and L. Liu: Polysaccharides regulate Th1/Th2 balance: A new strategy for tumor immunotherapy. \u003cem\u003eBiomed Pharmacother\u003c/em\u003e, 170, 115976 (2024) doi:10.1016/j.biopha.2023.115976\u003c/li\u003e\n\u003cli\u003eO. Wurtz, M. Bajenoff and S. Guerder: IL-4-mediated inhibition of IFN-gamma production by CD4+ T cells proceeds by several developmentally regulated mechanisms. \u003cem\u003eInt Immunol\u003c/em\u003e, 16(3), 501-8 (2004) doi:10.1093/intimm/dxh050\u003c/li\u003e\n\u003cli\u003eA. Basu, G. Ramamoorthi, G. Albert, C. Gallen, A. Beyer, C. Snyder, G. Koski, M. L. Disis, B. J. Czerniecki and K. Kodumudi: Differentiation and Regulation of T(H) Cells: A Balancing Act for Cancer Immunotherapy. \u003cem\u003eFront Immunol\u003c/em\u003e, 12, 669474 (2021) doi:10.3389/fimmu.2021.669474\u003c/li\u003e\n\u003cli\u003eT. Mori, R. Takada, R. Watanabe, S. Okamoto and Y. Ikeda: T-helper (Th)1/Th2 imbalance in patients with previously untreated B-cell diffuse large cell lymphoma. \u003cem\u003eCancer Immunol Immunother\u003c/em\u003e, 50(10), 566-8 (2001) doi:10.1007/s00262-001-0232-8\u003c/li\u003e\n\u003cli\u003eM. Dehghani, M. Ramzi, M. Kalani, H. Golmoghaddam and N. Arandi: Higher Peripheral Blood IFN-gamma-/IL-4+ Th2 Lymphocytes Are Associated with Lower Rate of Relapse in Patients with Lymphoma. \u003cem\u003eImmunol Invest\u003c/em\u003e, 51(2), 452-463 (2022) doi:10.1080/08820139.2020.1840583\u003c/li\u003e\n\u003cli\u003eJ. C. Wight, G. Chong, A. P. Grigg and E. A. Hawkes: Prognostication of diffuse large B-cell lymphoma in the molecular era: moving beyond the IPI. \u003cem\u003eBlood Rev\u003c/em\u003e, 32(5), 400-415 (2018) doi:10.1016/j.blre.2018.03.005\u003c/li\u003e\n\u003cli\u003eA. I. Cioroianu, P. I. Stinga, L. Sticlaru, M. D. Cioplea, L. Nichita, C. Popp and F. Staniceanu: Tumor Microenvironment in Diffuse Large B-Cell Lymphoma: Role and Prognosis. \u003cem\u003eAnal Cell Pathol (Amst)\u003c/em\u003e, 2019, 8586354 (2019) doi:10.1155/2019/8586354\u003c/li\u003e\n\u003cli\u003eC. Jimenez-Cortegana, N. Palazon-Carrion, A. Martin Garcia-Sancho, E. Nogales-Fernandez, F. Carnicero-Gonzalez, E. Rios-Herranz, F. de la Cruz-Vicente, G. Rodriguez-Garcia, R. Fernandez-Alvarez, A. Rueda Dominguez, M. Casanova-Espinosa, N. Martinez-Banaclocha, J. Guma-Padro, J. Gomez-Codina, J. Labrador, A. Salar-Silvestre, D. Rodriguez-Abreu, L. Galvez-Carvajal, M. Provencio, M. Sanchez-Beato, M. Guirado-Risueno, P. Espejo-Garcia, M. Lejeune, T. Alvaro, V. Sanchez-Margalet, L. de la Cruz-Merino, G. Spanish Lymphoma Oncology, C. the Spanish Group for Immunobiotherapy of, G. Spanish Lymphoma Oncology, C. the Spanish Group for Immunobiotherapy of, G. Spanish Lymphoma Oncology and C. the Spanish Group for Immunobiotherapy of: Circulating myeloid-derived suppressor cells and regulatory T cells as immunological biomarkers in refractory/relapsed diffuse large B-cell lymphoma: translational results from the R2-GDP-GOTEL trial. \u003cem\u003eJ Immunother Cancer\u003c/em\u003e, 9(6) (2021) doi:10.1136/jitc-2020-002323\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable1. The general clinical features\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eClinical Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003eTotal patients (94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eMedian age(range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e63 (9-89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003e\u0026gt;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e51 (54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e45 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eECOG\u0026gt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e14 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eB symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e39 (41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eAnn Arbor stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e26 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e68 (72.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eLDH\u0026gt;250U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e56 (58.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eExtra nodal involvement\u0026gt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e24 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIPI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003e0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e44 (46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003e3-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e50 (53.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eBone marrow involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e27 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eCHOP-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e78 (82.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e16 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eCD4+T cell(/ul)\u0026le;263.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e17 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eCD8+T cell(/ul)\u0026le;245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e24 (25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eB cell(/ul)\u0026le;39.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e30 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eNK cell(/ul)\u0026le;10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e2 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIL-4(pg/ml)\u0026le;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e69 (73.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIL-6(pg/ml)\u0026le;10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e43 (46.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIL-10(pg/ml)\u0026le;3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e46 (48.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIL-17(pg/ml)\u0026le;6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e51 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIL-12(pg/ml)\u0026le;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e17 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIFN-\u0026gamma;(pg/ml)\u0026le;9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e25 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIFN-\u0026gamma;/IL-4\u0026le;2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e54 (56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.7009%;\"\u003e\n \u003cp\u003eIL-12/IL-17\u0026le;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44.2991%;\"\u003e\n \u003cp\u003e26 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ECOG, Eastern Cooperative Oncology Group; LDH, lactate dehydrogenase; IPI, International Prognostic Index; \u0026beta;2-MG, \u0026beta;2-microglobulin; IL, interleukin.\u003c/p\u003e\n\u003cp\u003eTable2. Univariate analysis of OS and PFS.\u003c/p\u003e\n\u003cp\u003e2.1 The general clinical characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"675\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 32.4828%;\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 33.0788%;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e95.0% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e95.0% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eAge\u0026gt;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.316\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.151-4.659\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.147\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.087-4.241)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.623-2.290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.629-2.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eECOG\u0026gt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.731-4.254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.716-4.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eIPI score\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.252\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.165-4.356)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.987\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.036-3.810)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eAnn Arbor(12/34）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.704-3.397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.685-3.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.973-4.608)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e2.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(0.960-2.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eB symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.660-2.465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.598-2.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eExtra nodal involvement\u0026gt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.052 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.993-3.690)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.901-3.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eBone marrow Involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.610-2.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.588-2.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eHb\u0026gt;100g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(305-1.475)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.349-1.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eLDH\u0026gt;250U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.654-2.621)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.656-2.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eAlb\u0026gt;35g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.287-1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.303-1.110)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003e\u0026beta;2-MG\u0026gt;2.3mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 .469\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.057-5.766)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e2.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.920-4.978)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.5226%;\"\u003e\n \u003cp\u003eCRP\u0026gt; mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3474%;\"\u003e\n \u003cp\u003e(.421-2.878)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4932%;\"\u003e\n \u003cp\u003e1.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9434%;\"\u003e\n \u003cp\u003e(.401-2.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ECOG, Eastern Cooperative Oncology Group; IPI, International Prognostic Index; Hb, haemoglobin; LDH, lactate dehydrogenase; Alb, lbumin; \u0026beta;2-MG, \u0026beta;2-microglobulin; CRP, C-reactive protein.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"675\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 36.5413%;\"\u003e\n \u003cp\u003e2.1 The general clinical characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 20.1982%;\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 23.9989%;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e95.0% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e95.0% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eCD4+T cell\u0026le;263.5/ul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.466\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.224-968)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.473\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.228-.981)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eCD8+T cell\u0026le;245/ul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.363\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.184-.718)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.465\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.237-.909)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eB cell \u0026le;39.95/ul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e1.764 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e(.830-3.749)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e(.810-3.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eNK cell \u0026le;10.7/ul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e20.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e(.001-471244.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.475 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e(.000-234.747)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eIL-6\u0026le;10.31pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e(.923-3.639)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.106 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e(.290-1.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eIL-4\u0026le;0.72 pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e2.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e(.810-5.422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e(.266-1.392)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eIL-10\u0026le;3.51pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e(.700-2.818)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.540 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e(.270-1.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eINF-\u0026gamma;\u0026le; 9.36pg/ml\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.015\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.428\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.217-.845)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.421\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.213-.832)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eIL-17\u0026le; 6.33pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e1.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e(.604-2.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e(.437-1.600)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eIL-12 \u0026le;0.72pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e2.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e(.765-8.291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e(.112-1.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eIFN-\u0026gamma;/IL-4\u0026le;2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.315\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.164-.606)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.304\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.158-.584)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2208%;\"\u003e\n \u003cp\u003eIL-12/IL-17 \u0026le;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.044-3.835)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.056\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.074-3.937)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"675\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 34.9853%;\"\u003e\n \u003cp\u003eTable3.\u0026nbsp;multifactorial analysis of OS and PFS.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16.3952%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 18.0974%;\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 22.0394%;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9301%;\"\u003e\n \u003cp\u003e95.0% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1093%;\"\u003e\n \u003cp\u003e95.0% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.3952%;\"\u003e\n \u003cp\u003eAge\u0026gt;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e2.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9301%;\"\u003e\n \u003cp\u003e(.917-6.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.918\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.107-7.688)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.3952%;\"\u003e\n \u003cp\u003eIPI score\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e1.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9301%;\"\u003e\n \u003cp\u003e(.640-4.601)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e1.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1093%;\"\u003e\n \u003cp\u003e(.553-3.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.3952%;\"\u003e\n \u003cp\u003e\u0026beta;2-MG\u0026gt;2.3mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9301%;\"\u003e\n \u003cp\u003e(.428-3.862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1093%;\"\u003e\n \u003cp\u003e(.351-3.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.3952%;\"\u003e\n \u003cp\u003eCD4+T cell\u0026le;263.5/ul\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.037 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.346\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.128-.936)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.312\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.116-.844)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.3952%;\"\u003e\n \u003cp\u003eCD8+T cell\u0026le;245/ul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.305\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.113-.823)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1093%;\"\u003e\n \u003cp\u003e(.153-1.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.3952%;\"\u003e\n \u003cp\u003eIFN-\u0026gamma;/IL-4\u0026le;2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.222\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.090-.545)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.226\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(.092-.555)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.3952%;\"\u003e\n \u003cp\u003eIL-12/IL-17\u0026le;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9301%;\"\u003e\n \u003cp\u003e(.436-2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.2859%;\"\u003e\n \u003cp\u003e.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.7338%;\"\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1093%;\"\u003e\n \u003cp\u003e(.458-2.678)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IPI, International Prognostic Index; \u0026beta;2-MG, \u0026beta;2-microglobulin; IL, interleukin.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diffuse large B-cell lymphoma, IFN-γ/IL-4 ratio, CD4 + T cell, Immune related prognostic model, International prognostic index","lastPublishedDoi":"10.21203/rs.3.rs-6562919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6562919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignant tumor of B-cell origin that is predisposed by abnormal immune function and is extremely challenging. Lymphocyte subsets and circulating cytokines are easily accessible immune indicators, which may be useful in prognostication of newly diagnosed DLBCL patients, independently. However, the association between these two clinical laboratory findings and prognostication of newly diagnosed DLBCL patients has not been studied.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eHere, we retrospectively analyzed 94 newly diagnosed DLBCL patients initially treated in our institution between 2017 and 2022. The prognostic influence of lymphocyte subsets, cytokines levels and other factors, including age, tumor stage on progression-free survival (PFS) and overall survival (OS) were studied by Kaplan\u0026ndash;Meier curves as well as univariate and multivariate Cox regression models. Based on the findings in our center, we constructed an immune-related prognostic model and further validated it in an independent cohort from another center.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results suggested that IFN-γ/IL-4 ratio and CD4\u0026thinsp;+\u0026thinsp;T cell count were independent risk variables for both PFS and OS in DLBCL patients. Besides, multivariate analysis showed that age was associated with the worse OS whereas CD8\u0026thinsp;+\u0026thinsp;T cell count was associated with the inferior PFS. Moreover, elevated pretreatment IFN-γ/IL-4 ratio was significantly correlated with poor clinical response efficacy. Compared to patients experienced with death, lower level of IFN-γ/IL-4 ratio was discovered in surviving patients during the subsequent treatment cycles. Additionally, a new immune-related prognostic score model (IRPS) was constructed based on age, CD4\u0026thinsp;+\u0026thinsp;T cell count and IFN-γ/IL-4 ratio, where high-risk patients had worse overall survival than low-risk patients. Meanwhile, the IRS could refine the IPI score well and validation of the IRS model in another independent cohort confirmed its effectiveness.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIFN-γ/IL-4 ratio is a simple, accessible but useful prognostic factor in newly diagnosed DLBCL patients, and the IRS model could better suggest PFS and OS of DLBCL, allowing for better risk stratification.\u003c/p\u003e","manuscriptTitle":"Identification of IFN-γ/IL-4 ratio as a new predictor and the significance of its based immune related prognostic model in diffuse large B-cell lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 17:52:40","doi":"10.21203/rs.3.rs-6562919/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-04T15:42:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T18:18:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-01T16:24:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T15:36:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253854742627162815583484672562955956173","date":"2025-05-28T22:05:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330215620431378278977019031982424221282","date":"2025-05-28T13:34:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256215317085846805312617128959879164703","date":"2025-05-28T06:34:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224699130686875819325334954719133219888","date":"2025-05-27T14:17:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T15:44:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122654665823659457411132713292329356071","date":"2025-05-23T06:06:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-23T05:15:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-08T03:10:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-08T03:07:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T14:14:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-05-06T14:13:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"248c17d8-eec3-4542-b6dc-a1164b4e4dce","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:03:38+00:00","versionOfRecord":{"articleIdentity":"rs-6562919","link":"https://doi.org/10.1186/s12885-025-14737-1","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-08-27 15:58:17","publishedOnDateReadable":"August 27th, 2025"},"versionCreatedAt":"2025-05-27 17:52:40","video":"","vorDoi":"10.1186/s12885-025-14737-1","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14737-1","workflowStages":[]},"version":"v1","identity":"rs-6562919","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6562919","identity":"rs-6562919","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}