Immunosenescence phenotypes and prognostic significance of CD8 + CD28− T cells in AIDS-related non-Hodgkin lymphoma

Abstract

Background

AIDS-related non-Hodgkin lymphoma (AR-NHL), a leading malignancy among people living with HIV (PLWH), may not receive standard intensive curative therapies as the general population due to immune deficiency concerns, even in the combined antiretroviral therapy (cART) and immunochemotherapy era. Thus, a comprehensive understanding of T-cell subsets landscapes is urgently needed to grasp the complex and dynamic role of the immune system in AR-NHL patients’ survival.

Materials and methods

This longitudinal cohort study enrolled 31 de novo adult AR-NHL patients and 52 HIV-negative NHL controls. Multi-color flow cytometry profiled peripheral blood immunophenotypes at baseline and during immunochemotherapy. Univariate and multivariate Cox regression analyses evaluated associations between baseline risk factors and clinical outcomes.

Results

AR-NHL patients exhibited comparable 1-year overall survival (71.0% vs. 74.8%) and progression-free survival (60.4% vs. 64.6%) to HIV-negative NHL individuals. Baseline β2-microglobulin (β2-MG), erythrocyte sedimentation rate, and EBER positivity were higher in AR-NHL. AR-NHL revealed persistent immune senescence during immunochemotherapy, including reduced CD4+ T cells, lower CD4/CD8 ratio, decreased Tregs, naïve CD45RA+, and memory CD45RO + CD4+ T cells, alongside elevated Tregs/CD4, CD8+, CD8 + CD28+, and CD8 + CD28− T cells, highlighting the complex HIV-driven immune compromise. Multivariate analysis identified baseline circulating CD8 + CD28− T cells as an independent predictor of inferior prognosis in AR-NHL, correlated with aggressive disease markers such as elevated β2-MG, hypoproteinemia, decreased CD4/CD8 ratio, high International Prognostic Index score, and EBER positivity.

Conclusions

AR-NHL patients warrant standard full-dose cancer therapy to improve prognosis despite immunocompromised and immunosenescence phenotypes. CD8 + CD28− T cells serve as an independent prognostic biomarker for AR-NHL, potentially associated with chronic immune activation and viral exposure. Our data suggest that characterizing T-cell subsets may enhance understanding of immune dynamics in AR-NHL, providing a foundation for exploring personalized approaches to infection prophylaxis.

Introduction

Human immunodeficiency virus (HIV), a global health challenge with 39.9 million infected populations in 2023, is classified as a carcinogenic virus by the International Agency for Research on Cancer [1]. People living with HIV (PLWH) are particularly susceptible to immune dysregulation, which predisposes them to AIDS-defining malignancies such as Kaposi’s sarcoma, aggressive B-cell lymphomas, and invasive cervical cancer. AIDS-related non-Hodgkin lymphoma (AR-NHL) is the most common malignancy and a major cause of mortality in PLWH. Compared with the age- and gender-matched general individuals, its incidence increased approximately 10–20 folds. AR-NHL typically presents at an advanced stage, exhibits aggressive features, and displays more extensive extranodal involvement [2–4]. Over the past three decades, the integration of immunochemotherapy with combined antiretroviral therapy (cART) has extended survival in AR-NHL patients [5]. However, persistent outcome disparities in comparison to the general NHL population remain a conundrum, partly attributable to treatment de-escalation in the fear of exacerbating immune deficiency through standard intensive anti-tumor therapy [6,7].

Multiple intertwined mechanisms, including immunosuppression, immunological imbalance and dysfunction, chronic antigenic stimulation, genetic abnormalities, cytokine dysregulation, and co-infection with oncogenic viruses, contribute to AR-NHL immune heterogeneity [6]. Despite advances in treatment, the immunological landscape of AR-NHL remains poorly understood, particularly the role of T-cell subsets in disease progression and prognosis. Given that T cells orchestrate anti-tumor immunity and maintain immune homeostasis, deciphering their phenotypic signatures is critical for improving clinical stratification, optimizing treatment approaches, and highlighting the potential of T cell-based strategies to enhance patient outcomes.

Previous investigations have established associations between T-cell profiles and NHL prognosis in HIV-seronegative populations. Low absolute lymphocyte counts predict inferior outcomes in diffuse large B-cell lymphoma (DLBCL) [8–13], while reduced CD4+ T cells, regulatory T (Treg) cells, and natural killer cells correlate with adverse survival in B-cell lymphomas [14–19]. Moreover, diminished proportions or numbers of tumor-infiltrating CD3+, CD4+, and PD-1+ T cells in the microenvironment promote immune escape [20–22], whereas elevated activated cytotoxic T lymphocytes independently signal a poor prognosis in DLBCL [23]. Nevertheless, these findings cannot be directly extrapolated to AR-NHL. Liévin et al. recently reported that baseline NK cell lymphopenia and reduced naïve B cell proportions serve as risk factors for mortality and disease progression in HIV-associated DLBCL [24]. In contrast to the rapid recovery of innate immune cells (e.g. neutrophils, NK cells, and monocytes), adaptive immune cells, particularly T cells, recover more slowly. This leads to prolonged lymphopenia and compromised immune function, thereby increasing patients’ susceptibility to infections following cytotoxic chemotherapy and radiotherapy. In the context of AR-NHL, the dynamic changes of specific T-cell immunophenotypes during immunochemotherapy remain unexplored comprehensively, creating significant knowledge gaps regarding prognostic biomarkers, risk stratification, and immunotherapy strategies.

This longitudinal observational study aims to compare the immunophenotypic profiles of de novo AR-NHL patients against HIV-seronegative controls during immunochemotherapy. By characterizing immune dynamics during cancer treatment, we aim to explore T-cell phenotypes potentially linked to prognosis and risk stratification. These preliminary findings may inform future personalized monitoring of the immune microenvironment in AR-NHL patients.

Methods

Study design and ethical approval

This observational and longitudinal cohort study was conducted at Nanfang Hospital, affiliated with Southern Medical University, and included 31 de novo adult AR-NHL patients and 52 of 150 HIV-negative NHL patients from July 2021 to June 2023 (Supplementary Figure 1). A 1:2 propensity score match (PSM) was developed, balancing for lymphoma histological subtype, age, sex, Ann Arbor stage, extranodal involvement, lactate dehydrogenase (LDH) levels, and Eastern Cooperative Oncology Group performance status (ECOG PS), with a caliper value of 0.03. AIDS diagnosis was based on the 1993 CDC classification, requiring either CD4+ T-cell count <200 cells/μL or the presence of an AIDS-defining opportunistic infection/cancer. All AR-NHL patients satisfied these criteria at enrollment. The pathological diagnosis of NHL was confirmed according to the 2016 World Health Organization classification.

Data collection, clinical assessments, and endpoints

Demographic and clinical data included age at lymphoma diagnosis, history of HIV/AIDS, sex, duration and initial regimen of cART, lymphoma histological subtype, cell of origin, Ann Arbor stage, number and site of extranodal involvement, ECOG PS, International Prognostic Index (IPI) score, presence of B symptoms, bulky tumor, and bone marrow and CNS involvement. Baseline laboratory parameters comprised LDH levels, β2 microglobulin (β2-MG), erythrocyte sedimentation rate (ESR), Epstein-Barr virus (EBV)-encoded RNA (EBER), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR), albumin (ALB), CD4 count, CD4/CD8 ratio, cART, and HIV-1 RNA. According to the ROC curve analysis, the optimal cutoff points were determined by the median as follows: LMR (2.12), PLR (204.82), ALB (35.70 g/L), CD4/CD8 ratio (0.31), CD3+ T cells (CD45 + CD3+, 77.10%), CD3 + CD4+ T cells (CD45 + CD3 + CD4+, 17.10%), CD3 + CD8+ T cells (CD45 + CD3 + CD8+,57.30%), CD8 + CD28− T cells (CD45 + CD3 + CD8 + CD28−, 30.48%), CD8 + CD28+ T cells (CD45 + CD3 + CD8 + CD28+, 26.67%), CD3 − CD16 + CD56+ NK cells (CD45 + CD3 − CD16 + CD56+, 12.50%), and CD4 + CD25 + CD127−/CD4+ (12.85%) in the AR-NHL cohort.

Radiological evaluations were conducted using computed tomography or 18F-fluorodeoxyglucose positron emission tomography/computed tomography. Brain magnetic resonance imaging and cerebrospinal fluid analysis were utilized to assess central nervous system involvement, while bone marrow biopsies were performed to evaluate bone marrow involvement. Treatment efficacy was assessed based on the Lugano 2014 criteria. PET-CT-based progressive disease (PD) was identified by (1) individual target nodes/nodal masses with a Deauville score of 4 or 5 and increased FDG uptake compared to baseline; (2) new FDG-avid extranodal foci consistent with lymphoma (excluding etiologies like infection or inflammation) at interim/end-of-treatment assessments; or (3) new/recurrent FDG-avid foci in the bone marrow. CT-based PD was confirmed by at least one of the following: (1) an abnormal node/lesion with longest transverse diameter (LDi) > 1.5 cm and a ≥ 50% increase from perpendicular diameters nadir, plus an increase in LDi or SDi (shortest axis perpendicular to the LDi) from nadir (≥ 0.5 cm for lesions ≤2 cm; ≥ 1.0 cm for lesions > 2 cm); (2) splenomegaly progression (≥ 50% increase in splenic length beyond prior baseline elevation, or ≥ 2 cm increase from baseline if previously normal); (3) new/progressing nonmeasured lesions; (4) regrowth of resolved lesions (new node > 1.5 cm in any axis, new extranodal site > 1.0 cm in any axis, or unequivocal smaller lesions attributable to lymphoma); (5) new assessable lesions of any size confirmed as lymphoma; or (6) new/recurrent bone marrow involvement. Interim efficacy assessments were conducted after 2–4 cycles of therapy, and final efficacy assessments were performed before regular follow-up at the end of the treatment.

The primary endpoints of this study were 1-year progression-free survival (PFS) and 1-year overall survival (OS). PFS was defined as the time from baseline diagnosis of AR-NHL to the first documented disease progression or death from any cause. For the purpose of PFS calculation, disease progression is determined by comparing subsequent tumor assessments to measurements obtained at best response (treatment nadir), not baseline, in adherence to the Lugano 2014 criteria. OS was defined as the time from the baseline diagnosis of AR-NHL to the last follow-up or death from any cause.

Lymphocyte phenotype analysis by flow cytometry

Multiparameter flow cytometry was performed on fresh whole blood within 24 h of sampling using a BD FACSCanto II flow cytometer to identify and quantify immunocyte phenotyping. Peripheral blood mononuclear cells were isolated and stained with fluorochrome-conjugated antibodies following standard procedures. The panels of antibodies were anti-CD3-PerCP, anti-CD4-APC-Cy7, anti-CD4-FITC, anti-CD8-PE-Cy7, anti-CD28-PE, anti-CD16-PE, anti-CD56-PE/Cy7, anti-CD25-APC, anti-CD127-PE, anti-CD45-PerCP, antiCD45RA-FITC, anti-CD45RO-APC, and anti-CD27-PE-Cy7 (BD Biosciences, San Jose, CA, USA). Cell suspensions were incubated for 20 min at room temperature. Following the lysis of red blood cells with FACS Lysing Solution, cells were resuspended and analyzed using a flow cytometer. Leukocytes were first gated on CD45+ cells, followed by CD3+ T-cell enumeration; CD8 + CD28− subsets were analyzed within the CD45 + CD3+ gate. For precision in gating visualization, full marker combinations (e.g. CD45 + CD3 + CD8 + CD28−) are retained in figures. For brevity and readability in tables and results text, T-cell subsets are denoted concisely (e.g. CD8 + CD28−), with the understanding that all T-cell populations are derived from the CD45 + CD3+ gate as specified in the table footnotes. Data analysis was conducted on compensated data using FlowJo V10.8.1 software (TreeStar, Oregon, USA). All experiments were conducted in a biosafety laboratory.

Statistical analysis

Statistical analyses and graphical rendering were performed using SPSS 26.0 (Chicago, IL, USA) and GraphPad Prism 9.0. PSM was conducted using the MatchIt package in R version 4.3.2. Continuous variables were described as medians with interquartile ranges (IQR) and compared using the Mann–Whitney U test. Categorical variables were expressed as frequencies and percentages, with comparisons made using the chi-square test or Fisher’s exact test, as appropriate. Survival curves were estimated by the Kaplan–Meier method and compared via the log-rank test. Univariate and multivariate analyses were conducted using the Cox proportional hazard model. Hazard ratios (HR) and corresponding 95% confidence intervals (CI) were calculated by dichotomizing each variable based on their median value. For univariate and multivariate Cox regression, IPI was entered as a dichotomous covariate, with scores 0–3 defined as low-risk and 4–5 as high-risk. Additionally, linear correlation analysis and the Mann–Whitney U test were employed to examine the relationship between the percentages of CD8 + CD28− T cells and other factors. All statistical tests were two-sided, and significance was defined as a P-value < 0.05.

Results

Patients’ baseline characteristics of demographics and clinical features

We recruited 31 newly diagnosed AR-NHL patients and 52 propensity score-matched HIV-negative NHL individuals. Patient baseline characteristics are summarized in Table 1. No significant difference was observed between the AR-NHL and HIV-seronegative NHL groups in terms of gender, age >60 years old, lymphoma subtypes, Ann Arbor stage, extra-nodal involvement, LDH, ECOG PS, IPI score, B symptoms, bulky tumor, bone marrow and central nervous system involvement, LMR, PLR, and ALB. It is noteworthy that lymphoma subtypes were balanced between groups via PSM, so numerical differences in DLBCL/BL/NKTL proportions reflect disease biology rather than statistical significance. In addition, compared to HIV-negative NHL controls, AR-NHL patients exhibited lower CD4+ T-cell counts, a decreased CD4/CD8 ratio, higher β₂-MG and ESR levels, and increased positive EBER expression.

Table 1.

Baseline demographics and clinical characteristics of patients with AR-NHL and HIV-negative NHL.

Characteristics, No. (%)	NHL (n = 52)	AR-NHL (n = 31)	P
Male	41 (78.8)	25 (80.6)	0.844
Age > 60 years	11 (21.2)	5 (16.1)	0.575
Lymphoma subtype			0.965
DLBCL	42 (80.8)	25 (80.6)
BL	6 (11.5)	4 (12.9)
NKTL	4 (7.7)	2 (6.5)
Ann Arbor stage III/IV	48 (92.3)	27 (87.1)	0.464
Extra-nodal involvement ≥2	36 (69.2)	21 (67.7)	0.888
LDH, U/L	254 [168–462]	358 [232–1 069]	0.068
>ULN	30 (57.7)	20 (64.5)	0.539
ECOG PS ≥ 2	20 (38.5)	11 (35.5)	0.786
IPI			0.711
Low risk (0–1)	10 (19.2)	6 (19.4)
Low-middle risk (2)	12 (23.1)	9 (29.0)
Middle-high risk (3)	6 (11.5)	3 (9.7)
High risk (4–5)	24 (46.2)	13 (41.9)
B symptoms (Yes)	20 (38.5)	10 (32.3)	0.569
Bulky tumor (≥7.5 cm)	10 (19.2)	9 (29.0)	0.304
Bone marrow involvement	7 (13.5)	8 (25.8)	0.157
CNS involvement	9 (17.3)	8 (25.8)	0.353
β2-MG, mg/L [median, IQR]	2.80 [2.24–4.21]	3.91 [3.16–6.05]	0.002
ESR, mm/h [median, IQR]	21 [11–50]	52 [15–85]	0.032
EBER (Positive)	6 (11.5)	12 (38.7)	0.004
LMR [median, IQR]	2.22 [1.50–3.79]	2.21 [0.91–3.72]	0.572
<2.12	24 (46.2)	15 (48.4)	0.844
PLR [median, IQR]	169.34 [110.52–272.83]	204.82 [132.45–459.46]	0.051
≥204.82	21 (40.1)	16 (51.6)	0.319
ALB, g/L [median, IQR]	36.55 [32.85–39.90]	35.70 [30.20–39.40]	0.164
<35.70	19 (36.5)	15 (48.4)	0.288
CD4 count, cells/μL [median, IQR]	467 [282–693]	172 [123–247]	< 0.001
<200 cells/μL	8 (18.2)	21 (67.8)	< 0.001
CD4/CD8 [median, IQR]	1.24 [0.85–2.05]	0.31 [0.22–0.38]	< 0.001
≥0.31	45 (86.5)	16 (51.6)	< 0.001
Current cART	–	29 (93.5)	–
HIV-1 RNA, copies/mL [median, IQR]	–	36 500 [1 416–505 000]	–
≥100,000 copies/mL	–	11 (35.5)	–
20–100,000, copies/mL	–	14 (45.1)	–
Undetectable	–	6 (19.4)	–

Abbreviations: DLBCL: diffuse large B-cell lymphoma; BL: Burkitt lymphoma; NKTL: natural killer/T-cell lymphoma; LDH: lactate dehydrogenase; ULN: upper limit of normal; ECOG PS: Eastern Cooperative Oncology Group performance status; IPI: International Prognostic Index; CNS: central nervous system; β2-MG: β2-microglobulin; ESR: erythrocyte sedimentation rate; EBER: Epstein–Barr virus (EBV)-encoded RNA; LMR: lymphocyte to monocyte ratio; PLR: platelet to lymphocyte ratio; ALB: albumin; cART: combined antiretroviral therapy. The bold values indicate statistically significant differences between NHL and AR-NHL (p < 0.05).

In the AR-NHL cohort, the median age was 41 years (IQR: 34–54), with 16.1% of patients over 60 and 80.6% male (Table 1). Most AR-NHL patients (80.6%) had DLBCL, predominantly of the germinal center B-cell (GCB) subtype (62.2%); 12.9% had Burkitt’s lymphomas, and 6.5% had natural killer/T-cell lymphomas (NKTL). At disease onset, 64.5% of patients presented with elevated LDH, 35.5% had an ECOG PS score ≥ 2, 67.7% exhibited extra-nodal invasion ≥ 2, 87.1% were at Ann Arbor stage III/IV, and 41.9% were categorized as high-risk (IPI score 4–5). Among them, B symptoms occurred in 32.3% of patients, bulky disease (diameter ≥ 7.5 cm) was documented in 29.0% of individuals, and 25.8% of patients were diagnosed with either bone marrow or central nervous system involvement, respectively (Table 1).

Before the onset of AR-NHL, 8 out of 31 patients (25.8%) had a history of HIV and had been on cART for over 3 months; of these, 75.0% (6/8) had an undetectable HIV-1 viral load (data not shown). In addition, 35.5% of patients demonstrated a baseline HIV-1 RNA > 100,000 copies/mL, and 93.5% initiated or continued cART during cancer therapy (Table 1), with 74.2% on INSTI-based regimens (Supplementary Table 1). The median CD4+ T-cell count was 172 cells/mm3 (IQR: 123–247), with 67.8% having counts below 200 cells/mm³ (Table 1).

Comparative analysis of treatment response and survival analysis

Lymphoma treatment characteristics for AR-NHL and HIV-negative NHL cohorts were detailed in Supplementary Table 1. The AR-NHL group demonstrated a higher pre-treatment mortality rate (16.1%, 5/31), lower proportions of autologous stem cell transplantation (3.2%, 1/31) and R-CHOP-like immunochemotherapy (35.5%, 11/31), but increased frequencies of R-EPOCH regimen (35.5%, 11/31) and preventive intrathecal injection (61.3%, 19/31), consistent with National Comprehensive Cancer Network (NCCN) guildlines for AR-NHL [25]. Five AR-NHL patients died before treatment initiation, all due to rapid clinical deterioration from severe complications at admission (acute tumor lysis syndrome and/or disseminated opportunistic infections). Despite aggressive supportive care, their conditions precluded the administration of immunochemotherapy. They had a substantially lower median baseline CD4+ T-cell count (51 cells/μL, IQR 29–245) compared to those who survived to receive therapy (181 cells/μL, IQR 127–247). While this difference did not reach statistical significance (p = 0.072, Mann–Whitney U test), the marked reduction in CD4+ T cells in the early death subgroup aligns with the hypothesis that more severe immune compromise likely heightened their susceptibility to life-threatening infections and rapid clinical deterioration. These deaths were categorized as baseline events in both PFS and OS analyses for AR-NHL and NHL cohorts. Among AR-NHL patients with B-cell origin (77.4%, 24/31), all cases with CD20-positive lymphomas received at least one cycle of rituximab-based immunochemotherapy, and 12.9% (4 patients) underwent radiotherapy.

With a median follow-up of 29 months (range: 0–63), the AR-NHL cohort demonstrated 1-year PFS, OS, and relapse/refractory rates of 60.4%, 71.0%, and 38.7%, respectively (Figure 1 and Supplementary Table 1). These rates showed no significant differences from those in the HIV-negative NHL cohort, which had corresponding rates of 64.6%, 74.8%, and 37.8% following a median follow-up of 24 months (range: 0–41). Treatment outcomes in the AR-NHL group included a 48.4% complete response (CR), 9.7% partial response (PR), 3.2% stable disease (SD), and 22.6% progressive disease (PD), with no significant differences from the NHL cohort (Supplementary Table 1).

Figure 1.

Survival analysis. Kaplan–Meier curves for 1-year progression-free survival (A) and overall survival (B) of 31 AIDS-related non-Hodgkin lymphoma (AR-NHL) patients compared to 52 HIV-negative NHL individuals.

Differences of lymphocyte subset dynamics during immunochemotherapy

Further analysis of lymphocyte subsets in peripheral blood revealed that de novo AR-NHL patients had increased proportions and absolute numbers of CD8+ T cells (CD45 + CD3 + CD8+), CD8 + CD28+ T cells (CD45 + CD3 + CD8 + CD28+), and CD8 + CD28− T cells (CD45 + CD3 + CD8 + CD28−) as compared with HIV-seronegative NHL controls (Table 2 and Figure 2(A)). In contrast, the percentage and count of CD4+ T cells (CD45 + CD3 + CD4+), naïve CD45RA + CD4+ T cells (CD45 + CD3 + CD4 + CD45RA + CD45RO−), and memory CD45RO + CD4+ T cells (CD45 + CD3 + CD4 + CD45RA − CD45RO+) were significantly reduced in the AR-NHL setting (p < 0.001). Additionally, the number of Treg cells (CD4 + CD25 + CD127−) was lower in AR-NHL patients, while the Tregs/T4 ratio (CD4 + CD25 + CD127−/CD4+) was increased. No significant differences were observed in the frequencies and numbers of CD3+ T cells (CD45 + CD3+) and NK cells (CD45 + CD3 − CD16 + CD56+, Supplementary Figure 2).

Table 2.

Peripheral lymphocyte immunophenotyping in de novo AR-NHL and HIV-negative NHL patients.

Lymphocyte subsets, median (IQR)	NHL (n = 52)	AR-NHL (n = 31)	P
CD45 + CD3+, %	76.90 (65.62–81.54)	77.10 (68.60–86.80)	0.466
CD4+, %*	39.00 (30.01–49.10)	17.10 (10.50–21.40)	<0.001
CD8+, %*	31.00 (23.10–38.35)	57.30 (45.80–64.80)	<0.001
CD45 + CD3 − CD16 + CD56+, %	14.50 (7.50–21.15)	12.50 (7.40–15.40)	0.133
CD4 + CD25 + CD127−/CD4+ (Treg/T4), %*	8.19 (6.20–12.20)	12.85 (9.76–18.97)	<0.001
CD8 + CD28+, %*	15.04 (10.53–20.80)	26.67 (20.17–34.69)	<0.001
CD8 + CD28−, %*	15.25 (9.63–19.37)	30.48 (21.26–37.35)	<0.001
CD4 + CD45RA + CD45RO−, %*	6.61 (3.98–14.80)	2.20 (1.10–5.05)	<0.001
CD4 + CD45RA − CD45RO+, %*	27.03 (23.2–40.07)	12.25 (9.54–16.40)	<0.001
CD45 + CD3+, cells/μL	794 (517–1267)	715 (548–1016)	0.598
CD4+, cells/μL*	467 (27–1221)	172 (123–247)	<0.001
CD8+, cells/μL*	344 (193–526)	519 (337–760)	0.003
CD45 + CD3-CD16 + CD56+, cells/μL	139 (78–243)	103 (70–180)	0.151
CD4 + CD25 + CD127− (Treg), cells/μL*	40.70 (26.73–57.24)	20.82 (12.78–29.30)	<0.001
CD8 + CD28+, cells/μL*	129.31 (89.18–261.00)	271.12 (158.17–358.12)	0.003
CD8 + CD28−, cells/μL*	147.18 (91.73–217.67)	238.19 (167.34–415.69)	0.001
CD4 + CD45RA + CD45RO−, cells/μL*	76.91 (33.88–140.84)	23.56 (9.13–58.22)	<0.001
CD4 + CD45RA − CD45RO+, cells/μL*	288.75 (165.79–537.99)	144.96 (90.94–188.59)	<0.001

*T-cell subsets were gated on CD45 + CD3+ lymphocytes by flow cytometry; NK cells were gated on CD45 + CD3 − CD16 + CD56+ cells. The bold values indicate statistically significant differences between NHL and AR-NHL (p < 0.05).

Figure 2.

Immunophenotypic characteristics. (A) Flow cytometry analysis of circulating T-cell subsets in newly diagnosed AR-NHL patients in comparison with HIV-negative NHL controls, including CD45 + CD3 + CD4+, CD45 + CD3 + CD8+, CD45 + CD3 + CD8 + CD28+, CD45 + CD3 + CD8 + CD28− cells, and tregs/T4 (CD4 + CD25 + CD127−/CD4+) ratio (median ± IQR). (B) Dynamic changes in CD45 + CD3 + CD4+, CD45 + CD3 + CD4 + CD45RA − CD45RO+, CD4 + CD25 + CD127− (treg), CD45 + CD3 + CD8+, CD45 + CD3 + CD8 + CD28+, and CD45 + CD3 + CD8 + CD28− cells at three time points during immunochemotherapy in the AR-NHL group comparing them with NHL controls (median ± IQR). T0: newly diagnosis; T1: after 3 cycles of treatment; T2, after 6 cycles of treatment.

The AR-NHL group consistently showed lower proportions of CD4+ T cells and memory CD45RO + CD4+ T cells, a decreased count of Treg cells, and higher percentages of CD8+, CD8 + CD28+, and CD8 + CD28− T cells compared to the HIV-uninfected NHL controls during immunochemotherapy (Figure 2(B)). Dynamic clinical monitoring revealed an elevation in total CD3+ T cells during therapy in both the AR-NHL and HIV-negative NHL groups (Supplementary Figure 2).

CD8 + CD28− T cells as an independent prognostic factor for OS and PFS in AR-NHL

Data from the 31 AR-NHL patients were further analyzed to identify independent prognostic factors (Table 3). Univariate analysis revealed several biomarkers associated with inferior OS, including high-risk IPI score (HR = 3.502, 95% CI = 1.054–11.694, p = 0.042), bulky tumor (HR = 3.739, 95% CI = 1.127–12.401, p = 0.031), decreased LMR (HR = 13.758, 95% CI = 1.753–107.948, p = 0.013), lower ALB level (HR = 14.507, 95% CI = 1.847–113.960, p = 0.011), and an elevated percentage of CD8 + CD28− T cells (HR = 5.478, 95% CI = 1.175–25.539, p = 0.030). To manage confounding variables, important prognostic factors such as age, CD4/CD8 ratio, and HIV-1 RNA are included in the multivariate analysis additionally, particularly to account for patient heterogeneity. Multivariate analysis identified CD8 + CD28− T cells (HR = 9.118, 95% CI = 1.307–63.620, p = 0.026) as an independent prognostic factor for OS. In a comparable fashion, univariate Cox regression analysis indicated that high-risk IPI score (HR = 3.612, 95% CI = 1.204–10.874, p = 0.023), LMR (HR = 5.409, 95% CI = 1.479–19.546, p = 0.010), ALB (HR = 6.330, 95% CI = 1.745–22.294, p = 0.005), and CD8 + CD28− T cells (HR = 4.827, 95% CI = 1.336–17.432, p = 0.016) each served as prognostic indicators for PFS, as detailed in Table 3. Similarly, multivariate Cox regression analysis identified CD8 + CD28− T cells (HR = 10.020, 95% CI = 2.027–49.535, p = 0.005) as an independent prognostic factor for PFS in patients with AR-NHL. Furthermore, Kaplan–Meier analysis revealed that a higher percentage of CD8 + CD28− T cells was significantly associated with worse PFS (p = 0.006) and OS (p = 0.012) (Figure 3). After excluding the 5 pre-treatment deaths that occurred at baseline (1 in the CD8 + CD28− T-cell < 30.48% group and 4 in the CD8 + CD28− T-cell ≥ 30.48% group), elevated CD8 + CD28− T-cell levels remained significantly associated with inferior PFS (HR = 4.537, 95% CI: 0.940–21.892, p = 0.046; data not shown). This sustained prognostic utility underscores a specific role of this biomarker in predicting treatment response, rather than simply reflecting baseline disease aggressiveness, with no such association observed for OS. To address this limitation, future prospective studies incorporating larger cohorts and more granular data collection will be essential. Additionally, AR-NHL patients with lower LMR and ALB had inferior PFS and OS (Supplementary Figure 3). Accordingly, univariate and multivariate Cox regression models were employed to evaluate the prognostic value of CD8 + CD28− T-cell proportions in HIV-negative NHL patients. These analyses failed to demonstrate independent prognostic significance for OS or PFS, suggesting that in the absence of HIV co-infection, CD8 + CD28− T cells may not contribute to NHL patient outcomes (Supplementary Table 2). Due to the pathological heterogeneity, small sample size, and limited outcome events of NKTL (2 cases in the AR-NHL cohort and 4 in the NHL cohort), we excluded these 2 NKTL cases and reanalyzed B-cell NHL subtypes in AR-NHL to improve model fit and address histological heterogeneity. Notably, univariate and multivariate Cox regression analyses confirmed the independent prognostic value of CD8 + CD28− T cells for OS (HR = 29.241, 95% CI = 1.275–670.790, p = 0.035) and PFS (HR = 9.990, 95% CI = 1.340–74.495, p = 0.025) in AIDS-related B-cell NHL subtypes (Supplementary Table 3), indicating that immune senescence marker has consistent prognostic utility across aggressive B-cell malignancies in the context of HIV. This finding validates our core conclusion while underscoring the need for larger NKTL cohorts in future research.

Table 3.

Cox regressions analysis for overall survival and progression-free survival in AR-NHL patients (n = 31).

Characteristics	OS				PFS
	Univariate analysis		Multivariate analysis		Univariate analysis		Multivariate analysis
	HR (95% CI)	P	HR (95% CI)	P	HR (95% CI)	P	HR (95% CI)	P
Male	1.117 (0.241–5.172)	0.887			1.593 (0.356–7.126)	0.543
Age > 60 years	2.610 (0.682–9.986)	0.161	3.526 (0.354–35.133)	0.283	2.064 (0.569–7.496)	0.271	3.454 (0.471–25.332)	0.223
Ann Arbor stage III/IV	27.523 (0.028–27401.230)	0.347			27.197 (0.060–12 250.370)	0.289
Extra-nodal involvement ≥ 2	2.752 (0.590–12.829)	0.197			2.119 (0.588–7.631)	0.251
LDH > ULN	6.333 (0.810–49.588)	0.079			2.521 (0.701–9.061)	0.157
ECOG PS ≥ 2	1.181 (0.345–4.048)	0.791			1.262 (0.421–3.782)	0.677
B symptoms (Yes)	2.799 (0.852–9.193)	0.090			2.813 (0.977–8.093)	0.055
IPI (4–5)	3.502 (1.054–11.694)	0.042	1.608 (0.078–33.162)	0.758	3.612 (1.204–10.874)	0.023	1.508 (0.168–13.553)	0.714
Bulky tumor (Yes)	3.739 (1.127–12.401)	0.031	1.086 (0.190–6.204)	0.926	2.370 (0.817–6.876)	0.112
EBER (Positive)	1.989 (0.605–6.533)	0.257			1.200 (0.416–3.458)	0.736
LMR < 2.12	13.758 (1.753–107.948)	0.013	9.816 (0.782–132.437)	0.078	5.409 (1.479–19.546)	0.010	8.540 (0.272–57.324)	0.184
PLR ≥ 204.82	1.765 (0.515–6.046)	0.366			0.943 (0.331–2.690)	0.913
ALB < 35.70 g/L	14.507 (1.847–113.960)	0.011	0.538 (0.033–8.678)	0.662	6.330 (1.745–22.294)	0.005	1.107 (0.143–8.568)	0.922
CD4 count < 200 cells/μL	5.502 (0.703–43.069)	0.104			1.911 (0.533–6.854)	0.320
CD4/CD8 < 0.31	2.409 (0.700–8.294)	0.163	4.095 (0.718–23.363)	0.113	1.870 (0.645–5.421)	0.249	1.631 (0.384–6.936)	0.508
HIV-1 RNA ≥ 100 000 copies/mL	1.539 (0.450–5.265)	0.492	0.276 (0.054–1.423)	0.124	1.668 (0.576–4.830)	0.346	0.414 (0.092–1.855)	0.249
CD45 + CD3+ ≥ 77.10%	2.482 (0.654–9.415)	0.181			1.961 (0.655–5.873)	0.229
CD4+ < 17.10%*	2.409 (0.700–8.294)	0.163			1.870 (0.645–5.421)	0.249
CD8+ ≥ 57.30%*	4.564 (0.984–21.172)	0.052			2.869 (0.897–9.181)	0.076
CD45 + CD3-CD16 + CD56+ ≥ 12.50%	0.804 (0.245–2.637)	0.718			1.028 (0.360–2.938)	0.958
CD4 + CD25 + CD127−/CD4+ ≥ 12.85%*	2.021 (0.589–6.940)	0.264			1.664 (0.573–4.831)	0.349
CD8 + CD28− ≥ 30.48%*	5.478 (1.175–25.539)	0.030	9.118 (1.307–63.620)	0.026	4.827 (1.336–17.432)	0.016	10.020 (2.027–49.535)	0.005
CD8 + CD28+ ≥ 26.67%*	0.952 (0.289–3.133)	0.935			0.595 (0.206–1.719)	0.337
CD8 + CD28−/CD8 + CD28+ ≥ 1.113	1.373 (0.418–4.508)	0.602			1.565 (0.542–4.515)	0.408

*T-cell subsets were gated on CD45 + CD3+ lymphocytes by flow cytometry; NK cells were gated on CD45 + CD3-CD16 + CD56+ cells. The bold values indicate statistically significant differences (p < 0.05).

Figure 3.

Prognostic significance of CD8 + CD28− T cells (CD45 + CD3 + CD8 + CD28−) in AR-NHL. Kaplan–Meier survival analyses depicting progression-free survival (A) and overall survival (B) stratified by CD8 + CD28− T cells.

Correlation between CD8 + CD28− T cells and clinical variables

The high proportion of CD8 + CD28− T cells was significantly correlated with elevated β2-MG (r = 0.586, p < 0.001, Figure 4(A)), decreased ALB levels (r = −0.553, p = 0.001, Figure 4(B)), and a reduced CD4/CD8 ratio (r = −0.571, p < 0.001, Figure 4(C)) in the AR-NHL cohort. The middle-high- and high-risk groups (IPI ≥ 3) exhibited an increased percentage of CD8 + CD28− T cells compared to the low- and low-middle-risk groups (IPI < 3, p = 0.033, Figure 4(D)). Additionally, among AR-NHL patients, 75.0% of those with EBER-positive status and 36.8% of those with EBER-negative status demonstrated a high proportion of CD8 + CD28− T cells, respectively (p = 0.038, Figure 4(E)). Chi-square analysis also revealed a significant correlation between the presence of CD8 + CD28− T cells and EBER status (p = 0.038, Table 4).

Figure 4.

Correlation of CD8 + CD28− T cells with clinical variables in AR-NHL. (A–C) The percentages of CD8 + CD28− T cells in relation to β2-microglobulin (β2-MG), albumin (ALB), and CD4/CD8 ratio quantification. (D) The average levels of CD8 + CD28− T cells across different international prognostic index (IPI) score groups. (E) The proportions of CD8 + CD28− T cells in Epstein–Barr virus (EBV)-encoded RNA (EBER) groups.

Table 4.

AR-NHL patient characteristics by CD8 + CD28− T-cell stratification.

Characteristics	CD8 + CD28− T cell (%)
	Low (n = 15)	High (n = 16)	P
Male	13 (86.7)	12 (75.0)	0.654
Age > 60 years	3 (20.0)	2 (12.5)	0.654
Ann Arbor stage III/IV	12 (80.0)	15 (93.8)	0.333
Extra-nodal involvement ≥ 2	9 (60.0)	12 (75.0)	0.458
LDH > ULN	9 (60.0)	11 (68.8)	0.611
ECOG PS ≥ 2	6 (40.0)	5 (31.3)	0.611
IPI ≥ 3	6 (40.0)	10 (62.5)	0.210
B symptoms (yes)	4 (26.7)	6 (37.5)	0.704
Bulky tumor (yes)	4 (26.7)	5 (31.3)	1.000
Bone marrow involvement	4 (26.7)	4 (25.0)	1.000
CNS involvement	4 (26.7)	4 (25.0)	1.000
β2-MG ≥ 3.96 mg/L	6 (40.0)	9 (56.3)	0.464
ESR ≥ 46 mm/h	5 (33.3)	10 (62.5)	0.143
EBER (Positive)	3 (20.0)	9 (56.3)	0.038
LMR < 2.12	7 (46.7)	8 (50.0)	0.853
PLR ≥ 204.82	8 (53.3)	8 (50.0)	0.853
ALB < 35.70 g/L	10 (66.7)	6 (37.5)	0.104
CD4 count < 200 cells/μL	9 (60.0)	12 (75.0)	0.458
CD4/CD8 ≥ 0.31	10 (66.7)	6 (37.5)	0.104
HIV-1 RNA ≥ 100,000 copies/mL	3 (20.0)	8 (50.0)	0.081

The bold values indicate statistically significant differences (p < 0.05).

Discussion

In this study, we longitudinally evaluated peripheral blood T-cell immune phenotypes in people with NHL with or without HIV before and after anticancer therapies. Our findings show that de novo AR-NHL have a comparable prognosis to HIV-negative NHL during the cART and immunochemotherapy era. In addition, AR-NHL patients exhibited characteristics of cellular senescence, with reduced CD4+ T-cell percentages, increased proportions of CD8+, CD8 + CD28+, and CD8 + CD28− T cells, and a higher Tregs/CD4 ratio compared to the general NHL populations, suggesting significantly compromised immune activity in the context of HIV infection. Moreover, multivariate analysis identified CD8 + CD28− T cells as an independent prognostic parameter for predicting inferior prognosis of AR-NHL.

In the present study, the PFS and OS rates, as well as therapeutic response, in AR-NHL, exhibit no significant differences compared to HIV-uninfected NHL, consistent with previous studies [26]. Dramatic improvements in HIV treatment over the past two decades have decreased the risk of AIDS and increased immune function and survival rates, making full-dose intensive therapies possible. Correspondingly, the prognosis for AR-NHL patients has significantly improved due to better HIV control and immune restoration. Consequently, survival is more influenced by NHL disease characteristics rather than HIV-specific factors such as CD4 count, prior AIDS-defining illnesses, and HIV viral load [6]. In the modern cART era, standard treatments for HIV-associated lymphomas are similar to those for HIV-negative patients, with a focus on multidisciplinary team management to address concurrent opportunistic infections, potential drug-drug interactions, and co-occurring cancers [6].

In the AR-NHL cohort, the predominant histological subtypes were B-cell lymphomas, primarily DLBCL and BL, consistent with the disease epidemiology in HIV-associated malignancies [4]. Compared to HIV-negative NHL controls, most AR-NHL patients exhibit clinically aggressive phenotypes, reduced CD4+ T-cell counts, elevated β₂-MG and ESR levels, and increased EBER positivity, in line with previous findings [27]. These aggressive phenotypes align with standard immunotherapy strategies in major international guidelines for HIV-associated lymphomas [25,28]. NCCN, European Hematology Association (EHA) and European Society for Medical Oncology (ESMO), and British HIV Association (BHIVA) guidelines recommend R-EPOCH as the first-line regimen for AR-NHL (predominantly DLBCL and BL), differing from the standard R-CHOP protocol for HIV-negative patients. Consequently, our cohort showed a higher proportion of R-EPOCH compared to R-CHOP (Supplementary Table 1). Variations in standard therapeutic regimens may potentially confound prognostic assessments. Future large-sample prospective multicenter studies are warranted to investigate first-line regimens for AR-NHL in the era of rituximab and cART. Additionally, similar inconsistencies exist in the low ASCT utilization among AR-NHL patients, which may derive from multiple factors as unequal ASCT resource allocation, patient financial burdens, and suboptimal treatment motivation. In fact, the treatment landscape for non-Hodgkin’s lymphoma has evolved significantly over the last decade, incorporating targeted therapies, immunotherapies, and cell therapies such as CAR-T cells, bispecific and trispecific antibodies, targeted drugs, and antibody-drug conjugates [29–31]. These chemo-free approaches are continually challenging established guidelines. Thus, identifying risk factors, performing hazard stratification, and optimizing first-line treatment strategies are crucial for AR-NHL in this era of burgeoning new drug development. It is known that HIV plays an indirect role in lymphomagenesis, primarily through immunosuppression and chronic immune activation. Additionally, chronic antigenic stimulation from co-infections with Epstein-Barr virus, human herpes virus 8, hepatitis C virus, and hepatitis B virus may contribute to lymphomagenesis [32]. These oncogenic viruses modulate the host immune system, decreasing cytotoxic T-cell response and immune surveillance [33–35]. Given the significance of virus-related immunity, we investigated the clinical correlation between AR-NHL and immune contexture for precision-stratified therapy.

Chronic HIV infection-induced aberrant host immune disorders and the global transcriptomic characterization of T cells have been extensively studied [36–38].a However, the phenotypic dynamics of peripheral blood T-cell subsets in AR-NHL have not been comprehensively evaluated, particularly in comparison with HIV-negative NHL. Lymphocyte subsets are essential for evaluating the host’s immune status, the extent of immune reconstitution, and predicting the prognosis of AR-NHL patients undergoing immunotherapy [39]. Here, we observed significant depletion of CD4+ T cells and memory CD45RO + CD4+ T cells in newly diagnosed AR-NHL patients, alongside a relative augmentation of CD8+ T cells with absolute increases in both CD8 + CD28+ and CD8 + CD28− T cells in the CD3+ subpopulation. These trends persisted post-chemoimmunotherapy, indicating a lasting imprint of HIV on the systemic adaptive immune system even beyond lymphoma eradication. Firstly, baseline CD4+ T-cell counts were recognized as a prognostic indicator in HIV-positive lymphomas historically. However, emerging data in the cART era – consistent with our results – suggest this association has weakened [40–44]. In our cohort, CD4+ T-cell counts were not significantly associated with OS or PFS (Table 3). This shift likely reflects improved HIV control and immune reconstitution under effective antiretroviral therapy, which have reshaped the prognostic relevance of baseline CD4+ T-cell levels in AR-NHL. Future studies should prioritize functional immune markers (e.g. T-cell senescence profiles) to better characterize disease risk. Secondly, the significant depletion of CD4+ T cells and memory CD45RO + CD4+ T cells indicates that severely compromised adaptive immune responses and antitumor immune surveillance may increase risks of infections and cancer recurrence during immunochemotherapy and long-term follow-up. These findings underscore the need for prospective studies to characterize how immune dysfunction modulates treatment toxicity and disease progression in AR-NHL. Thirdly, although baseline NK cell frequency and counts were analyzed, no significant prognostic association was observed in our cohort. This may be attributed to limited sample size, heterogeneity of NK cell subsets, or lack of functional assessments (e.g. degranulation or cytokine production). Larger future studies with detailed characterization of NK cell subsets and functions are warranted to clarify their role in the prognosis of AR-NHL or AR-DLBCL [24]. Finally, the relative increase of CD8+ T cells with absolute increases in both CD8 + CD28+ and CD8 + CD28− T cells in the CD3+ subpopulation may be a compensatory response to the decline in CD4+ T cells. These CD8+ T-cell subsets, involved in immune defense, surveillance, regulation, and immunosenescence, affect patients’ long-term prognosis and quality of life. This finding underscores the complexity of immune responses and the critical need to restore adaptive immune system balance.

Our study further identified CD8 + CD28− T cells in the circulation as an independent predictor correlated with an inferior prognosis in AR-NHL patients. This parameter was significantly associated with elevated β2-MG, hypoalbuminemia, reduced CD4/CD8 ratio, positive EBER status, and higher IPI score in AR-NHL patients, all of which were indicative of more aggressive disease characteristics. Notably, our analysis of HIV-negative NHL patients revealed that CD8 + CD28− T-cell proportions did not correlate with survival outcomes, contrasting sharply with their prognostic significance in AR-NHL. This divergence underscores the distinct immunological landscapes of HIV-associated and non-HIV-associated lymphomas, highlighting the need for disease-specific biomarker discovery. Future investigations should focus on elucidating the differential roles of CD8 + CD28− T cells in NHL pathogenesis based on HIV status. In addition, although univariate analysis showed higher IPI was associated with inferior survival in our AR-NHL cohort, it did not retain independent significance in multivariate analysis, possibly due to limited sample size and event count. Thus, absence of statistical significance for IPI in this study should be interpreted with caution and does not undermine the established prognostic role of IPI. Prospective studies with larger cohorts are needed to validate whether CD8 + CD28− T-cell proportion provides incremental prognostic value over IPI. Accumulating evidence indicates that CD8 + CD28− T cells constitute a heterogeneous population, containing cells with both effector/memory properties and regulatory capacities. These cells are associated with chronic antigenic stimulation such as aging, cancers, chronic viral infections, autoimmune diseases, chronic inflammatory diseases, and allotransplantation [45]. The CD8 + CD28− T-cell subset was increased in HIV, hepatitis C virus, human cytomegalovirus, and Epstein–Barr virus infections. In the context of HIV, chronic immune cell activation was associated with a poor clinical response to cART [46]. The elevated proportion of CD8 + CD28− T cells is well-documented in PLWH, reflecting immunosenescence and chronic immune activation [47,48]. As shown by Kalayjian et al., this phenomenon is associated with age- and HIV-related depletion of naïve CD8+ T cells, reduced CD28 expression on CD8+ cells, and decreased thymic volume, all of which contribute to immune dysfunction [48]. Deeks further elucidated the complex interplay between HIV infection, chronic inflammation, immunosenescence, and aging, underscoring how these factors collectively drive immune system dysregulation [47]. Moreover, CD8 + CD28− T-cell circulation was commonly elevated in lymphoma patients, correlating with advanced disease and immunosuppression [49]. This elevated phenotype was also detected in the tumor microenvironments of other malignancies [50]. Although CD8 + CD28− T cells helped predict prognosis in inflammatory bowel disease [50], their relationship to the outcome of AR-NHL remains unclear. Herein, we confirmed that circulating CD8 + CD28− T-cell was an independent predictor of poor outcome in patients with AR-NHL. Our work highlighted the recommendations for precise stratified management to customize personalized follow-up plans within AR-NHL patients. This finding first underscores the critical role of chronic immune activation in AR-NHL pathogenesis. These cells may serve as a biomarker to assess immune dysregulation intensity, offering insights into the disease’s underlying immunological mechanisms. Second, monitoring their dynamics may elucidate how immunosenescence progresses alongside disease development, clarifying whether accelerated immunosenescence drives tumor progression or vice versa. Finally, our analysis is restricted to baseline CD8 + CD28− T-cell levels, with no serial measurements during treatment or follow-up. Thus, we cannot comment on whether dynamic changes in this subset correlate with relapse. Future prospective studies incorporating scheduled immune profiling are warranted to investigate whether fluctuations in CD8 + CD28− T cells precede clinical recurrence – a finding that could, if validated, support their role in early relapse detection. This biomarker-driven approach holds promise to transform disease management, highlighting the need for immune-based monitoring in AR-NHL patient care. However, these findings are preliminary and necessitate further exploration, including prospective clinical trials, to determine how harnessing these immune landscapes can effectively improve patient outcomes in the evolving paradigm of AR-NHL therapy. Further research is also needed to elucidate their immunological function and the pathophysiological mechanisms underlying their imbalance.

Limitations

The current study is a single-center comparative study with small sample size. The characterization of CD28 − CD8+ T cells as a heterogeneous population with both effector/memory properties and regulatory capacities suggests the need for additional lymphocyte markers for better identification. We only assessed phenotypes without performing functional assays. The direct effect and regulatory mechanism of CD8 + CD28− T cells on T cells remain unclear in AR-NHL. Future studies employing more comprehensive lymphocyte phenotyping strategies and functional assays should elucidate the role of memory/effector CD8+ T cells in different AR-NHL histotypes.

Conclusions

In summary, this study characterized the unique immunological landscape of T-cell subpopulations in AR-NHL and identified peripheral blood CD8 + CD28− T cells as a novel independent prognostic marker for AR-NHL survival. These findings suggest a potential strategy to integrate immune dynamics into precise risk stratification and management decisions. Malignancy-induced T-cell exhaustion and senescence, critical dysfunctional states [51] are potentially linked to chronic immune activation and viral exposure. Understanding the relationship between immunological heterogeneity and AR-NHL pathophysiology is essential for defining the role of immunosenescence in AR-NHL pathogenesis and guiding the discovery of novel therapeutic targets. These insights necessitate further investigation, including prospective clinical trials, to determine how harnessing immune landscapes can improve patient outcomes within the evolving paradigm of AR-NHL therapy.

Funding Statement

This project was supported by grants from the Natural Science Foundation of Guangdong Province, China (2023A1515030252).

Ethical approval

This study adhered to the Declaration of Helsinki and received approval from the Nanfang Hospital Ethics Committee (NFEC-2021-178). All participants provided written informed consent in accordance with the World Medical Association Declaration of Helsinki.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.