Neutrophil Elastase as a Prognostic Biomarker and Driver of Tumor Progression in Diffuse Large B‐Cell Lymphoma

ABSTRACT

Introduction

With increasing recognition of the prognostic role of neutrophils in malignancies, this study investigated the clinical significance of neutrophil elastase (NE) expression in diffuse large B‐cell lymphoma (DLBCL).

Methods

Eighty‐seven patients with newly diagnosed primary DLBCL treated at Yancheng First People's Hospital between June 2020 and September 2024 were included. NE expression in tumor tissues was assessed by immunohistochemistry. Clinical and pathological characteristics were compared between NE‐positive (NE⁺) and NE‐negative (NE⁻) groups, and the association between NE expression and prognosis was analyzed. In vitro experiments using SU‐DHL‐4 cells evaluated the effects of NE and the NE inhibitor sivelestat on cell proliferation, apoptosis, and the expression of apoptosis‐related proteins.

Results

Positive NE expression was detected in 51.7% of DLBCL specimens. Compared with the NE⁻ group, the NE⁺ group showed a higher proportion of patients with International Prognostic Index (IPI) scores > 2 and intermediate–high/high‐risk Ann Arbor stages. Ki‐67 expression was also elevated in the NE⁺ group. Patients with NE positivity had significantly shorter overall survival (OS) and progression‐free survival (PFS) in the overall cohort, with similar findings in the non‐GCB subgroup (p < 0.05). Univariate analysis identified LDH level, B symptoms, Ann Arbor stage, NE expression, IPI score, and treatment regimen as prognostic factors. Multivariate analysis confirmed elevated LDH, positive NE expression, IPI score > 2, and non‐R‐CHOP therapy as independent predictors of poor OS. In vitro, NE promoted SU‐DHL‐4 cell proliferation and suppressed apoptosis‐related protein activation, whereas sivelestat inhibited proliferation, induced apoptosis, and reversed the effects of NE.

Conclusion

NE expression is associated with an unfavorable prognosis in DLBCL and may serve as a potential prognostic biomarker. Moreover, NE promotes lymphoma cell proliferation and inhibits apoptosis, effects that can be effectively reversed by sivelestat.

1. Introduction

Diffuse large B‐cell lymphoma (DLBCL) represents the most prevalent subtype of non‐Hodgkin lymphoma. It is an aggressive and potentially life‐threatening malignancy that remains challenging to manage clinically [1]. The clinical presentation of DLBCL is heterogeneous, with patients often exhibiting diverse symptoms and disease characteristics, which complicates therapeutic decision‐making. Although several treatment strategies are available, treatment failure, frequent relapse, and the development of chemotherapy resistance remain significant concerns [2]. Increasing evidence indicates that DLBCL comprises distinct molecular subtypes and is influenced by alterations in the tumor microenvironment. These biological variations play a crucial role in determining treatment response and ultimately influencing patient prognosis [3].

Signals present within the tumor microenvironment influence the polarization of tumor‐associated neutrophils (TANs). Owing to their inherent heterogeneity and functional plasticity, these cells can adopt phenotypes that either promote tumor progression or contribute to anti‐tumor immune responses [4]. The majority of metastatic stages can be facilitated by neutrophil extracellular traps (NETs), which are cytotoxic to the vasculature when they develop intravascularly and function as thrombosis mediators [5]. Neutrophil elastase (NE) is a serine protease released by TANs and represents a key component of NETs. Owing to its broad proteolytic activity, NE has attracted considerable interest for its dual, seemingly contradictory roles in cancer, as it can trigger tumor‐selective apoptosis while also promoting the proliferation of malignant cells [5]. Neutrophil‐mediated immune responses are directly regulated by NE expression levels, which are closely associated with pathological states such as inflammation, infection, and pneumonia. There is mounting evidence that NE influences cellular signaling pathways and modifies the tumor microenvironment, promoting the growth and progression of tumor cells [6, 7]. In vitro tumor growth in breast and gastric cancer models is suppressed by sivelestat (SV). This selective NE inhibitor inhibits its enzymatic activity through competitive inhibition [8].

In conclusion, although the prognostic significance of neutrophils in cancers has become more widely acknowledged, the role of NE in the course and outcomes of DLBCL remains unclear. Therefore, this work examines the clinical significance of NE expression in DLBCL patients and its mechanistic effects on lymphoma cell behavior. Thus, by linking clinical correlations with cellular functional analyses, this work aims to uncover novel insights into NE as both a prognostic biomarker and a potential therapeutic target, thereby advancing precision medicine strategies for the management of DLBCL.

2. Materials and Methods

2.1. Sample Collection

This study included 87 patients newly diagnosed with primary DLBCL at the Department of Hematology at Yancheng First People's Hospital, between January 2020 and June 2024. Clinical data, pathological features, and formalin‐fixed paraffin‐embedded (FFPE) tissue specimens were collected for analysis. Follow‐up was conducted until May 31, 2025. Overall survival (OS) was defined as the interval from diagnosis to death from any cause or to the last follow‐up. However, progression‐free survival (PFS) was defined as the time from diagnosis to disease progression, death from any cause, or the last follow‐up. The study was approved by the Ethics Committee of Yancheng First People's Hospital (Reference No. 2025‐K‐114). The study was also registered with the Chinese Clinical Trial Registry under registration number ChiCTR2500112903.

2.2. Immunohistochemical (IHC) Analysis

Sections from FFPE tissue blocks were cut at a thickness of 4 μm. IHC staining for NE was performed using a commercial kit (Elabscience, E‐IR‐R221) according to the manufacturer's protocol, with an anti‐NE primary antibody (Abcam, AB131260) applied at a 1:500 dilution. Two blindfolded pathologists separately examined stained slides under a light microscope, and any disagreements were settled by consensus. By multiplying the staining intensity score by the proportion score, NE expression was evaluated semi‐quantitatively. A score of 0 (negative), 1 (weak), or 2 (strong) was assigned to intensity. For cells that were less than 50% positive, the proportion score was set to 1; for cells that were more than 50%, it was set to 2. Positive expressiveness was deemed to be shown by a total score of at least two. Ki‐67 positivity was assessed by nuclear staining in ≥ 80% of tumor cells, whereas BCL‐2, BCL‐6, and c‐MYC positivity was defined as expression in ≥ 30% of tumor cells.

2.3. Cell Culture and Reagents

Shanghai Fuheng Biotechnology Co. Ltd. provided the human DLBCL cell line SU‐DHL‐4 (Product No. FH1124), which was verified to be free of mycoplasma contamination using short tandem repeat (STR) profiling. Under conventional growth conditions of 37°C in a humidified incubator with 5% CO₂, cells were kept in RPMI‐1640 media (KeyGEN, KGM31800) supplemented with 10% fetal bovine serum (KeyGEN, KGL3002‐5) and 1% penicillin–streptomycin. Cells in the logarithmic growth phase were used for all tests.

2.4. Cell Proliferation Assay

Cell viability was evaluated using the Cell Counting Kit‐8 (CCK‐8) assay (NCM Biotech, C6005). SU‐DHL‐4 cells were seeded into 96‐well plates at a density of 5 × 10⁵ cells/mL and treated with different concentrations of NE (MCE, HY‐E70356; 0, 12.5, 25, and 50 nmol/L) for 24 h. Cells were subsequently exposed to SV (OriLeaf, S80246) at 0, 2.5, 5, and 10 μg/L. Based on the viability results, the optimal concentrations were selected for further experiments. Cells were allocated into four groups: Control, NE, NE + SV, and SV. Absorbance at 450 nm was measured at 0, 12, 24, 36, and 48 h.

2.5. Western Blot Analysis

Cells were lysed using radioimmunoprecipitation assay (RIPA) buffer supplemented with protease inhibitors. Proteins were separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS‐PAGE) and subsequently transferred onto polyvinylidene difluoride (PVDF) membranes. The membranes were incubated overnight at 4°C with primary antibodies against β‐actin (Proteintech, 81,115–1‐RR; 1:10,000), cleaved caspase‐3 (HUABIO, ET1608‐64; 1:1000), BCL‐2 (HUABIO, ET1702‐53; 1:5000), and Bax (Servicebio, GB15690‐50; 1:1000). After washing, horseradish peroxidase (HRP)‐conjugated secondary antibodies were applied at room temperature for 1.5 h. Protein bands were visualized using an enhanced chemiluminescence (ECL) detection system.

2.6. Apoptosis Assay

Apoptosis was evaluated using an Annexin V‐FITC/PI detection kit (KeyGEN, KGA107). After 48 h of treatment as described in Section 2.4, cells were harvested, stained according to the manufacturer's protocol, and analyzed by flow cytometry to determine apoptotic rates.

2.7. Statistical Analysis

Data were analyzed using SPSS 25.0 and visualized with GraphPad Prism 10.0. Flow cytometry data were processed using FlowJo 10.0. Before analysis, continuous variables were evaluated for normality using the Shapiro–Wilk test and screened for outliers. Categorical data were presented as counts and percentages. Non‐normally distributed data were reported as medians with ranges, whereas normally distributed continuous variables were reported as mean ± standard deviation. The Student's t‐test was used to compare two groups, and one‐way ANOVA was used to compare more than two groups if the homogeneity of variance and normality assumptions were met. If not, the Mann–Whitney U test or Kruskal–Wallis test was used. When appropriate, the χ² test or Fisher's exact test was used to assess categorical variables. The related figure legends or tables display the sample size (n) for each analysis. The Kaplan–Meier method was used to estimate survival, and the log‐rank test was used to assess group differences. Cox regression analysis, both univariate and multivariate, was used to identify prognostic markers. Statistical significance was defined as a two‐sided p value < 0.05.

3. Results

3.1. NE Expression and Clinical Characteristics in DLBCL Patients

The study included 87 patients with DLBCL, of whom 38 were male (43.7%), and 49 were female (56.3%). The median age of the cohort was 67 years (range, 42–86 years), and 67 patients (77.0%) were aged 60 years or older. Of the total participants, 85 patients received treatment, while 2 either did not undergo therapy or had unclear treatment information. A total of 55 patients (63.2%) were treated with R‐CHOP or similar regimens, in which rituximab was the main targeted agent. At the end of the follow‐up period, 24 patients had died, whereas 63 were alive.

Positive NE expression was predominantly localized in the nucleus, presenting as a brown‐yellow signal on IHC staining (Figure 1B). Of the 87 DLBCL patients analyzed, 42 (48.3%) were NE‐negative. In contrast, 45 (51.7%) demonstrated NE positivity. Patients in the NE‐positive group showed significantly higher rates of an International Prognostic Index (IPI) score > 2 (66.7% vs. 33.3%, p = 0.002) and advanced Ann Arbor stage III–IV disease (75.6% vs. 53.4%, p = 0.024) compared with the NE‐negative group. No significant differences were observed between the groups for gender, age, Hans classification, primary tumor site, B symptoms, LDH levels, β2‐MG levels, ECOG performance status, or treatment regimens (p > 0.05). Please refer to Table 1. Analysis of patient pathological data revealed no significant differences in the expression of c‐MYC, BCL‐2, and BCL‐6 between the two groups (p > 0.05, Figure 2B–D). However, Ki‐67 expression was significantly higher in the NE‐positive group compared to the NE‐negative group (p = 0.043, Figure 2A). Collectively, these results suggest that NE positivity is associated with higher disease burden and adverse clinical features in DLBCL, highlighting its potential role as a biomarker of aggressive disease and poor prognosis.

FIGURE 1.

The IHC staining of DLBCL patients. (A) Representative IHC staining of a NE⁻ patient; (B) Representative IHC staining showing in a NE⁺DLBCL patient.

TABLE 1.

Association between clinical characteristics of DLBCL patients and NE expression.

Characteristic	Total (n, %)	Expression of NE		χ2	p
		Negative	Positive
Gender
Male	38 (43.7)	14 (33.3)	24 (53.3)	3.532	0.060
Female	49 (56.3)	28 (66.7)	21 (46.7)
Age (years)
< 60	20 (23.0)	13 (31.0)	7 (15.6)	2.909	0.088
≥ 60	67 (77.0)	29 (69.0)	38 (84.4)
Hans classification
GCB	22 (25.3)	11 (26.2)	11 (24.4)	0.153	0.696
Non‐GCB	65 (74.7)	31 (73.8)	34 (75.6)
Primary tumor site
Intranodal	42 (48.3)	21 (50.0)	21 (46.7)	0.097	0.756
Extranodal	45 (51.7)	21 (50.0)	24 (53.3)
B system
Yes	27 (31.0)	10 (23.8)	17 (37.8)	1.980	0.159
No	60 (68.0)	32 (76.2)	28 (62.2)
Ann Arbor stage
I–II	31 (35.6)	20 (47.6)	11 (24.4)	5.087	0.024
III–IV	56 (64.4)	22 (53.4)	34 (75.6)
IPI stage
0–2	43 (49.4)	28 (66.7)	15 (33.3)	9.656	0.002
3–5	44 (50.6)	14 (33.3)	30 (66.7)
LDH level
Normal	53 (60.9)	23 (54.8)	30 (66.7)	1.293	0.255
Abnormal high	34 (39.1)	19 (45.2)	15 (33.3)
β2‐MG level
Normal	59 (67.8)	29 (69.0)	30 (66.7)	0.056	0.812
Abnormal high	28 (32.2)	13 (31.0)	15 (33.3)
ECOG stage
0–1	68 (78.2)	33 (78.6)	35 (77.8)	0.008	0.929
≥ 2	19 (21.8)	9 (21.4)	10 (22.2)
Therapy
R‐CHOP	55 (63.2)	27 (64.3)	28 (62.2)	0.040	0.842
Others	32 (36.8)	15 (35.7)	17 (37.8)

Note: For 2 × 2 contingency tables, Fisher's exact test was employed if more than 20% of the cells had expected frequencies less than 5.

FIGURE 2.

NE across different pathological characteristics. (A) In the overall DLBCL (n = 87), the expression level of Ki‐67 was higher in NE⁺ patients; (B–D) The expression levels of BCL‐2, c‐Myc, and BCL‐6 had no significant correlation with the expression of NE.

3.2. Comparison of Prognosis and Prognostic Factor Analysis Between the Two Groups

The 87 DLBCL patients had a median follow‐up period of 23.5 months (range: 8–59). Based on NE expression, they were divided into NE‐negative (NE⁻) and NE‐positive (NE⁺) groups. Kaplan–Meier survival analysis revealed that NE⁺ patients had significantly shorter OS (p = 0.033, Figure 3A) and PFS (p = 0.012, Figure 3B) compared with NE⁻ patients. Stratification by the Hans classification showed that among non‐GCB patients (n = 65), the NE⁺ group was associated with significantly shorter OS (p = 0.034, Figure 3C) and PFS (p = 0.043, Figure 3D). In comparison, no significant difference in OS or PFS (p > 0.05, Figure 3E,F) was observed between NE⁺ and NE⁻ patients within the GCB subtype (n = 22). Collectively, these findings indicate that positive NE expression is associated with poorer survival, underscoring its potential as a prognostic biomarker in DLBCL.

FIGURE 3.

Prognostic analysis of patients with DLBCL. (A, B) The OS and PFS rate of overall patients (n = 87) with NE⁺ (n = 45) were significantly lower than that of NE⁻patients (n = 42) (p < 0.05); (C, D) The OS and PFS rate of non‐GCB patients (n = 65) with NE⁺ (n = 34) were significantly lower than that of patients with NE⁻patients (n = 31) (p < 0.05); (E, F) The OS and PFS rate of GCB patients (n = 22) was not significantly reduced (p > 0.05).

Univariate Cox regression analysis showed that abnormal LDH levels (p < 0.001), advanced Ann Arbor stage (p = 0.042), presence of B symptoms (p = 0.029), positive NE expression (p = 0.039), high IPI score (p = 0.004), and non‐R‐CHOP treatment (p = 0.002) were significantly associated with poorer OS in newly diagnosed DLBCL patients (Table 2). A multivariate Cox proportional hazards model was developed using clinicopathological variables that were significant in the univariate analysis. The results showed that elevated LDH levels elevated LDH level (HR = 3.181, 95% CI: 1.022–9.904, p = 0.046), positive NE expression (HR = 2.769, 95% CI: 1.103–7.573, p = 0.047), IPI score of 3–5 (HR = 4.216, 95% CI: 1.228–14.482, p = 0.022), and non‐R‐CHOP treatment (HR = 2.711, 95% CI: 1.057–6.996, p = 0.039) were independent prognostic factors for OS (see Table 2). Taken together, these results highlight NE positivity, high LDH, elevated IPI score, and the absence of R‐CHOP therapy as independent adverse prognostic factors, underscoring the clinical relevance of NE as a biomarker of poor survival in DLBCL.

TABLE 2.

Analysis of prognostic factors in DLBL patients with DLBCL.

Factor	Univariate Cox analysis			Multivariable Cox analysis
	HR	95% CI	p	HR	95% CI			p
Gender	0.653	0.282–1.510	0.319
Age	1.236	0.475–3.215	0.664
Hans classification	0.674	0.249–1.821	0.436
Primary tumor site	0.855	0.381–1.919	0.704
LDH level	4.280	1.816–10.085	0.001	4.138	1.265–13.535			0.019
β2‐MG level	0.504	0.173–1.248	0.128
Ann Arbor stage	2.513	0.928–6.807	0.070
B system	2.264	1.016–5.045	0.046	0.908	0.380–2.169			0.828
Expression of NE	0.385	0.155–0.953	0.039	0.284	0.094–0.859			0.026
ECOG	1.605	0.663–3.885	0.294
IPI stage	3.786	1.520–9.434	0.004	1.229	0.366–4.121			0.739
BCL‐2	0.581	0.236–1.431	0.238
BCL‐6	1.442	0.192–10.819	0.772
KI67	0.982	0.416–2.319	0.967
Therapy	3.817	1.622–8.982	0.002	2.648	1.057–6.636			0.038

3.3. Comparison of Proliferation and Apoptosis in SU‐DHL‐4 Cells Following NE and SV Treatment

When compared to the control group, the CCK‐8 assay showed that treatment with 25 nmol/L NE for 24 h significantly increased the proliferative activity of SU‐DHL‐4 cells (p < 0.05, Figure 4A). Subsequent exposure to 5 or 10 μg/L SV, on the other hand, significantly decreased the viability of NE‐pretreated cells, demonstrating a concentration‐dependent inhibitory impact (p < 0.05, Figure 4B).

FIGURE 4.

Comparison of proliferation and apoptosis capabilities in SU‐DHL‐4 cells after NE and SV treatment. (A) Proliferation of SU‐DHL‐4 cells was markedly enhanced following treatment with 25 nmol/L NE (n = 3). Data are shown as mean ± SD (one‐way ANOVA). (B) Treatment with 5 or 10 μg/L SV reduced the proliferative capacity of pretreated SU‐DHL‐4 cells (n = 3). Data are shown as mean ± SD (one‐way ANOVA). (C) Cell viability was compared in the four treatment groups at 12, 24, 36, and 48 h. At 48 h, cell proliferation was significantly greater in the NE group compared with the control and NE + SV groups, whereas the SV group showed the opposite. (n = 3). (D–H) Representative Western blots and quantification of apoptosis‐related proteins (BCL2, Bax, caspase‐3, and cleaved caspase‐3) in SU‐DHL4 cells subjected to various treatments (n = 3). β‐Actin served as the loading control. Data are shown as mean ± SD (one‐way ANOVA). (I–J) Flow cytometry demonstrated that the apoptotic rate was significantly reduced in the NE group compared with the other groups, while SV treatment increased apoptosis, counteracting the effect of NE (n = 3). Data are shown as mean ± SD (one‐way ANOVA).

SU‐DHL‐4 cells were assigned to four groups: Untreated control, NE (25 nmol/L), NE + SV (25 nmol/L NE combined with 10 μg/L SV), and SV (10 μg/L SV). At 12, 24, 36, and 48 h, the NE group consistently demonstrated greater cell viability than both the control and NE + SV groups. However, the SV group exhibited lower viability compared with the control and NE + SV groups. The NE + SV group showed intermediate viability, significantly lower than that of the NE group but higher than that of the SV group. These intergroup differences reached statistical significance at 48 h (p < 0.05, Figure 4C).

After 48 h of treatment, the NE group displayed significantly reduced expression of cleaved caspase‐3 (c‐caspase‐3) and Bax compared with the control and NE + SV groups (p < 0.05), along with significantly increased levels of pro‐caspase‐3 and BCL‐2 (p < 0.05). The SV group showed the opposite pattern of protein expression (p < 0.05, Figure 4D–H). The NE + SV group exhibited an intermediate profile: Cleaved caspase‐3 and Bax levels were significantly higher than in the NE group but lower than in the SV group, whereas pro‐caspase‐3 and BCL‐2 expression was greater than in the SV group yet remained lower than in the NE group (p < 0.05). Consistent with these observations, flow cytometric analysis showed that apoptosis was markedly reduced in the NE group compared with the control and NE + SV groups, whereas the SV group exhibited a significantly higher rate of apoptosis (p < 0.05; Figure 4I,J). These findings indicate that NE promotes the survival of lymphoma cells, whereas sivelestat may be a potential therapeutic agent that counteracts NE‐mediated tumor progression. All of the findings point to NE as a factor in lymphoma cell survival and to sivelestat as a possible treatment to stop NE‐driven tumor growth.

4. Discussion

DLBCL is one of the most prevalent subtypes of non‐Hodgkin lymphoma, accounting for about 30%–40% of all cases [1]. Moreover, its development is associated with immunosuppression, viral infections, and environmental factors. It is characterized by significant tumor heterogeneity and aggressiveness [2, 9]. DLBCL is divided into molecular subtypes, such as germinal center B‐cell‐like (GCB) and activated B‐cell‐like (ABC), according to the most recent WHO classification. These subtypes differ significantly in gene expression patterns and signaling pathways [10]. Interestingly, chronic activation of B‐cell receptor signaling is present in about 40% of patients with the ABC subtype, which increases resistance to standard chemotherapy [11]. The 5‐year OS rate for DLBCL patients in China is 38.4% [12]. The 5‐ and 10‐year OS rates for patients receiving first‐line therapy combining rituximab with anthracycline‐based chemotherapy increase to 69% and 55.6%, respectively [13]. Although first‐line therapy frequently results in remission, patients with refractory or relapsed DLBCL who are not eligible for autologous stem cell transplantation (ASCT) or who relapse after ASCT face a bleak outlook, with median survival usually restricted to just 6 to 12 months [14, 15]. A total of 87 newly diagnosed DLBCL patients were assessed in this study; follow‐up data showed 5‐year OS and PFS rates of 72.4% and 64.5%, respectively. The urgent need to enhance long‐term PFS and OS results is highlighted by the fact that a sizable minority of patients continue to relapse or experience disease progression even though routine immunochemotherapy is still successful for a sizable portion of patients. This underscores the importance of ongoing research into the molecular mechanisms underlying DLBCL progression and treatment resistance, as well as the discovery of new biomarkers and therapeutic targets. All things considered, these developments are essential for improving risk assessment, tailoring treatment plans, and, ultimately, enhancing patient outcomes.

Neutrophils play a variety of context‐dependent functions in cancer immunology. Emerging evidence highlights the pronounced heterogeneity of TANs, whose plasticity is associated with immunosuppressive activity, an unfavorable prognosis [16, 17, 18], and a predominantly tumor‐promoting role [19]. Further, NE, a serine protease produced from TANs, has two functions in the tumor microenvironment, particularly in regulating tumor cell survival. While NE can hydrolyze the CD95 death domain protein to cause tumor cell apoptosis [20], it can also degrade laminin‐111, which activates the integrin–p38α–MSK1/SOX signaling cascade and reactivates latent tumor cells [21, 22]. In the present study, NE expression was assessed for the first time in lymphoid tissues of DLBCL patients using immunohistochemistry, with a positivity rate of 51.7%. Using a semi‐quantitative scoring system, NE expression can be identified even at relatively low levels. NE positivity in lymphoma cells may reflect intrinsic expression; alternatively, it may result from internalization of NE released by infiltrating TANs, a hypothesis supported by the parallel evaluation of TAN infiltration markers. However, our preliminary data indicate that NE is frequently detectable in a subset of DLBCL cases.

Clinically, elevated NE expression has been documented in multiple solid tumors, where it has emerged as a potential biomarker. Its levels are closely associated with tumor stage, grade, and patient survival outcomes in cancers such as breast [23] and lung cancer [24], and it has been identified as an independent prognostic factor [25, 26]. Higher IPI scores and an advanced Ann Arbor stage were more common in patients with NE‐positive, which is similar to findings in other cancers [5, 27]. In addition, we examined the prognostic significance of NE expression in patients with DLBCL. The mortality rate at five years of follow‐up was 37.78% in the NE‐positive group and 16.67% in the NE‐negative group. Both overall and in non‐GCB patients, Kaplan–Meier survival analysis showed that NE‐positive patients had significantly lower OS and PFS than NE‐negative patients.

Furthermore, a comparison of the two groups' clinicopathological features revealed that individuals with positive NE expression had higher Ki‐67 levels. The results of this study suggest that NE positivity may be a predictive biomarker and is associated with unfavorable clinical outcomes. Furthermore, incorporating NE status into risk‐adapted treatment plans may help identify high‐risk individuals who need closer observation and possibly more extensive or focused treatment.

According to earlier research, NE stimulates tumor growth by activating the IRS‐1/PI3K–Akt pathway [26], transactivating EGFR/TLR4‐mediated MAPK/ERK signaling, and inducing the release of TGF‐α [28, 29]. Our future research will focus on elucidating the specific pathways by which NE contributes to DLBCL pathogenesis, based on prior pathway findings. Selective NE inhibitors may be promising targeted treatments because elevated NE levels have been shown to promote leukemia cell proliferation while preventing apoptosis [30]. Acute respiratory distress syndrome (ARDS) and sepsis are already being treated in China using SV, a highly selective NE inhibitor that successfully inhibits NE activity [31]. These results suggest that SV may be repurposed as a potential therapeutic strategy for DLBCL by inhibiting NE's tumor‐promoting actions. SV's potential as an anticancer agent has been supported by clinical trials examining its use in combination therapy for breast cancer, which have shown that it can suppress TGF‐α, hence lowering tumor growth and overcoming drug resistance [32, 33]. Since cell proliferation and apoptosis interact to drive cancer growth, treatment approaches that trigger apoptosis remain highly effective [34]. NE treatment of SU‐DHL‐4 lymphoma cells in our in vitro studies validated its tumor‐promoting role in DLBCL. NE significantly raised cell viability and proliferation within a specified concentration range, in line with earlier findings in solid tumors [21, 35]. These results imply that focusing on NE activity may be a viable therapeutic strategy for DLBCL and that SV may have clinical value as a component of precision therapy. NE suppressed apoptosis in DLBCL cells at specific doses, as assessed by flow cytometry and analysis of apoptosis‐related proteins.

By inhibiting cell division and increasing the expression of pro‐apoptotic proteins, such as cleaved caspase‐3 and Bax, SV offsets these protumor effects. Collectively, our findings demonstrate that NE and its inhibitor, SV, contribute to the development of DLBCL. SV has great potential to slow the proliferation of lymphoma cells, underscoring the need for further research to clarify its underlying molecular mechanisms. Moreover, SV treatment alone increased the expression of pro‐apoptotic proteins in DLBCL cells, suggesting that its action may involve pathways beyond NE‐driven proliferation. The development of more precise and focused treatment strategies for DLBCL patients with increased NE expression may be influenced by these findings. In DLBCL cells, SV monotherapy was sufficient to alter both cell viability and the expression of apoptosis‐related proteins, suggesting the presence of underlying regulatory mechanisms warranting further research.

The predictive value of NE in DLBCL needs to be confirmed in larger, independent clinical cohorts to enhance its clinical utility, despite the study's encouraging results. A key limitation of this study is that NE was evaluated as an isolated experimental factor, without fully accounting for TANs' broader biological roles in the tumor microenvironment. In addition, the particular molecular pathways through which NE promotes tumor proliferation, as well as the mechanisms by which sivelestat controls these effects, are not fully understood. In conclusion, DLBCL continues to present unsatisfactory long‐term outcomes, highlighting the urgent need for novel biomarkers and therapeutic strategies. Our results demonstrate that NE is an independent risk factor in DLBCL, with its positive expression closely associated with advanced disease features and poorer prognosis. Importantly, SV, an established NE inhibitor, holds promise as a therapeutic adjunct, particularly in NE‐positive DLBCL, where it could be integrated into combination regimens to restore apoptosis, inhibit tumor proliferation, and ultimately advance precision treatment approaches.

5. Conclusion

NE expression correlates with unfavorable prognosis in DLBCL and may serve as a valuable prognostic biomarker. It enhances lymphoma cell proliferation and suppresses apoptosis, effects that are effectively reversed by sivelestat.

Author Contributions

Xinyu Zheng: writing – original draft. Ling Shu: data curation. Yunye Qiu: software. Lingling Wang: supervision. Jinbo Lu: investigation. Chuanhai Xu: methodology. Meiling Zhou: data curation. Yuexin Cheng: writing – review and editing, supervision, project administration. Yuqing Miao: data curation.

Funding

This work was supported by the Open Research Fund of the Key Laboratory of Universities in Jiangsu Province (XZSYSKF2025009) and the Key Research and Development Program Project of Yancheng City (YCBE202558).

Disclosure

The authors have nothing to report.

Ethics Statement

The requirement for informed consent was waived by the Ethics Committee of Yancheng First People's Hospital (Reference No: 2025‐K‐114). Chinese clinical trial registry: ChiCTR2500112903.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.