Association of regulatory B, T cells and cytokine production with disease stage in non-small cell lung cancer patients: prospective case control study

Abstract

Background and aim

The role of regulatory B cells (Bregs) cells and regulatory T cells (Tregs) has been little investigated in non-small cell lung cancer (NSCLC). Therefore, we investigated the frequency of circulating Bregs, Tregs cells, and the serum cytokine profile in NSCLC and their association with disease progression.

Materials and methods

In this prospective case-control study, peripheral blood from 40 patients with NSCLC and 40 healthy controls was examined, the frequency of various lymphocyte subsets including Bregs and Tregs was evaluated using flow cytometry. Serum cytokine profile including IL1β, IL6, IL-17, TNF‐α, IL10, IL35 and TGF-β1 was measured using Luminex assay.

Results

An increased proportion of Bregs, Tregs, CD8 + T cells, and decreased CD4+/CD8 + ratio was observed in NSCLC patients compared to healthy controls. NSCLC patients with metastasis had a higher frequency of Bregs and Tregs than patients without metastasis. The frequency of Bregs and Tregs was related to the stage and histopathological status. NSCLC patients had significantly higher levels of serum cytokine profile when compared to healthy controls along with a significant association with lymph node metastasis and different TNM stages.

Conclusions

Peripheral Bregs, Tregs, and serum cytokine profiles, including proinflammatory and anti-inflammatory cytokines were increased among NSCLC patients and may be associated with disease progression and metastasis.

Introduction

Lung cancer is one of the most prevalent malignant tumors worldwide, among which 85% of lung cancer cases are non-small cell lung cancer (NSCLC) [1–4]. Recently, there has been an increasing recognition of the role of the immune system in cancer development and progression, with a corresponding focus on utilizing immunotherapy in the clinic and regulatory approval of immunotherapy for different kinds of cancers [5]. NSCLC can evade the specific active antitumor immune responses [6, 7].

Regulatory T lymphocytes (Tregs) play a pivotal role in the regulation of immune homeostasis in many diseases and the prevention of the development of autoimmune diseases [8–13]. Tregs positively contribute to the prevalence of immunosuppressive mechanisms against a variety of cancer cells [14–17]. Foxp3⁺ Tregs interfere by releasing cytolytic granules from cytotoxic T lymphocytes (CTLs) and thus essentially suppress the anti-tumor responses [18, 19]. Conflicting data exist about the prognostic impact of Foxp3⁺ Tregs infiltration in NSCLC [20, 21].

In line with the well-established role of Tregs in immune homeostasis, regulatory B cells (Bregs), are a newly discovered subpopulation of B cells. Bregs can also efficiently downregulate inflammatory reactions and induce tolerance by production of IL-10 and/or TGF-β [22–24]. They also interact with pathogenic T cells to prevent harmful immune responses and are believed to play a role in cancer [23, 25–27]. Moreover, IL-35 was reported to be produced by Tregs and Bregs to maximize the inhibitory role of Tregs, and can induce Bregs to secrete IL-10 and IL-35 [28, 29]. Several distinct phenotypes subpopulations of Bregs have been recognized in both human and mice and have a distinct clusters of differnation (CD) as cell surface markers. One of these group is the immature/transitional B cells CD19⁺CD24^{+ high} CD38^+ high which is the target phenotype in this study. These cells are characterized by the production of high level of the anti-inflammatory cytokine IL10 that suppress the T cell proliferation [30].

Recent studies highlighted the inclusion of lipid-based nanoparticles and immunotherapy as an alternative approach for treatment of lung cancer. However, Li, et al., 2025, emphasize the importance of conducting new researches that explore the immune microenvironment, developing personalized treatment strategies based on tumor profiles, and integrating new technologies for patient screening. Immunotherapy holds immense potential to modify lung cancer treatment and enhance clinical results [31, 32].

This study objective was to analyze the frequency of circulating Bregs and Tregs and to analyze the serum cytokine profile, including proinflammatory and anti-inflammatory cytokines among NSCLC patients and to find its association with disease progression.

Patients and Methods

Ethics approval and consent to participate

All procedures followed were in accordance with the ethical standards of the responsible committee of the faculty of medicine, Sohag University (Soh.Med-20.12.16).), and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all subjects involved in the study.

Study Design

This was a prospective case- control study. It included 40 NSCLC patients and 40 healthy controls. The NSCLC patients were recruited from Chest Department and Department of Clinical Oncology, Sohag University Hospitals, Sohag, Egypt from January to December 2021 .

All patients underwent thorough clinical examination and routine laboratory investigations followed by plain X-ray and CT scan of chest to detect primary lesions in addition to CT abdomen, pelvis and brain or MRI brain to evaluate the presence or absence of distant metastasis. Bone scintigraphy to detect the presence of bone metastasis was done. All patients underwent Fibreoptic Bronchoscopy (Pentax EB 1970TK) with or without image guided tru-cut needle biopsy .Clinical staging was done using TNM staging system of the American Joint Committee on Cancer Staging Manual, Eighth Edition (2017) [33] (Fig. 1).

Fig. 1.

Study flow chart for Breg and Treg cells in non-small cell lung cancer: frequency, cytokine profile and association with disease stage

Exclusion criteria

Patients with chest infection, T.B, heart disease, renal or hepatic insufficiency, active GIT bleeding, or a history of peptic ulcer, patients being affected by malignancy elsewhere and patients previously treated for lung cancer prior to the study were excluded from this study.

Flow cytometric detection of T- lymphocytes and regulatory T cells

T-lymphocytes were detected using 100 µl of blood sample, which was stained with 10 µl of fluoroisothiocyanate (FITC)-conjugated CD3, phycoerythrin (PE) conjugated CD8, and phycoerythrin conjugated CD4 antibodies (Becton Dickinson Biosciences, USA). Tregs were enumerated using Foxp3-FITC (e-Biosχienχe, USA), PE-conjugated CD25 (IQ Product, The Netherland) and CD4-Per-CP (Becton Dickinson, Bioscience, USA). Flow cytometric analysis was performed using the Fluorescence Activated Cell Sorter (FACS) Calibur flow cytometry with Cell Quest software (Becton Dickinson Biosciences, USA). Isotype control of anti-human IgG was used as a negative control for each sample. Forward and side scatter histogram was used to define the lymphocyte population and the percentages of CD3⁺ (T-lymphocytes), CD4⁺ (T-helper cells) and CD8⁺ (T-cytotoxic) were assessed from the lymphocyte population. In addition, CD4⁺CD25^-, CD4⁺CD25^{+intermediate}, CD4 + CD25+high, and CD4⁺CD25^+high Foxp3⁺regulatory T cells were evaluated on both CD4⁺. The mean fluorescence intensity (MFI) was used for Foxp3 expression as shown in Fig. 2.

Fig. 2.

Flow cytometric detection of T- lymphocytes and regulatory T cells: A Forward and side scatter plot was used to define the lymphocyte population (R1). B The expression of and CD3, CD4+, and CD8 + was assessed in lymphocytes population (R1), (CD3 not shown). C, D CD4 + T cells were gated and the expression of CD25 in CD4 + T cells (R2) was detected, compared with the negative isotype control (not shown) and different gates were used to define CD4 + CD25- cells (R3), CD4 + CD25+intermediate (med) cells (R4), and CD4 + CD25+High cells (R5). E The percentage of CD4 + CD25+high FoxP3 + cells in CD4 + T cells was determined. F Show the expression of FoxP3 + in CD4 + CD25+high cells

Flow cytometric detection of regulatory B cells

Circulating Bregs were assessed by using FITC-conjugated-CD38 (IQ Product, the Netherland), PE-conjugated-CD24 (Bioscience, USA) and Per-CP-conjugated CD19 antibodies (BD Bioscience, USA). The lymphocyte population was defined using forward and side scatter histograms. Then, CD19⁺ B cells were gated. Bregs defined as CD19⁺CD24^{+ high} CD38^+ high cells as shown in Fig. 3.

Fig. 3.

Flow cytometric detection of regulatory B cells. A Side and forward scatter plot was used to define the lymphocyte population (R1). B Cd19 + cells (R2) were assessed within the lymphocyte population. C CD24 and CD38 expression was assessed in CD19 + cell population to define CD19 + CD24+highCD38 + high cells (Regulatory B cells)

Cytokine measurement

Selected panels of plasma cytokines concentrations, including IL1β, IL6, IL-17, TNF‐α, IL10, IL35 and TGF-β1 were determined by multiplex Luminex assay using Milliplex TM Multiplex kits (Millipore, Billerica, MA, USA) according to the manufacturer’s protocol. The lower detection limit was determined to be 0.1 to 10.0 pg/ml according to the estimated cytokine.

Statistical analysis

Statistical analysis was performed using GraphPad Prism 8 version 8.0.1 software (Graph Pad Software Inc., San Diego, CA, USA). Mann-Whitney test was used to measure the significance differences between groups, while the correlation between parameters was done by Spearman’s correlation. P value ˂ 0.05 was considered significant.

Results

Table 1 summarizes the demographic characteristics of the patients in the study group. The NSCLC patients group included 23 males and 17 females (20 smokers and 20 non- smokers). Their mean age was 63.65 ± 5.15. The healthy controls were matched to the patients group in terms of age (mean age was 61.34_+3.15) and gender (21 males and 19 females). The control group included 17 smokers and 23 non- smokers.

Table 1.

Characterization of Non-small cell lung cancer patients

Variables	Non-small cell lung cancer patients (n = 40)
Age* (Years)	63.65 ± 5.15
Gender Male : female	23 : 17
Smoking history (n)	No = 20 Yes = 20
Pathological type (n)	Adenocarcinoma = 19 Squamous cell carcinoma = 14 Undifferentiated = 4 Large cell carcinoma = 3
TNM Staging (n)	Stage II = 5 Stage III = 15 Stage IV = 20
Regional lymph node metastasis (n)	Yes = 20 No = 20

*(Mean + SD)

TNM classification revealed 5 patients were in stage II, 15 patients were in stage III, and 20 in stage IV. Regional lymph node metastasis was found in 20 patients.

Furthermore, the histopathological examination of tumor mass biopsy diagnosed adenocarcinoma in 19, squamous cell carcinomas of malignant type in 14 patients, undifferentiated carcinoma in 4 patients and large cell carcinoma in 3 patients. Furthermore, the patients were classified according to stage into 20 with advanced disease (stage IV) and 20 with stages II and. stages III.

Lymphocytes subsets, Tregs, and Breg cells among NSCLC patients

As shown in Table 2, the frequencies of CD8 + T-cells were significantly increased in NSCLC patients than in controls (P = 0.0001). However, the increase in CD4 + T-cells in our patients compared to the healthy control group was non-significant. Consequently, the ratio of CD4/CD8 was significantly decreased in NSCLC compared with the healthy control group (P = 0.0001), with a Significant higher frequency of CD4 + CD25high T-cells (P = 0.002), FoxP3 (P = 0.03) were found in patients with NSCLC. Interestingly, patients with NSCLC exhibited no significant difference in the frequency of CD25, CD25intermediate T-cells, and CD19 + B cells compared to the healthy control group.

Table 2.

Frequency of lymphocytes subsets, Treg and Breg between NSCLC patients and healthy control group

Percentage (%)	NSCLC (n = 40)	Healthy control (n = 40)	P-value
CD 3+T-cells	65.65 ± 7.22	57.31 ± 10.53	0.006
CD 4+ T-cells	39.34 ± 8.54	37.46 ± 6.69	Ns
CD 8+ T-cells	32.08 ± 5.56	20.67 ± 8.06	0.0001
CD4/CD8 ratio	1.35 ± 0.14	1.75 ± 0.20	0.0001
CD4+CD25negative	27.53 ± 5.40	36.19 ± 6.60	0.0001
CD4+CD25+high	6.02 ± 1.77	4.49 ± 1.06	0.002
Foxp3 expression on CD4+CD25+high	133.99 ± 9.4	122.89 ± 8.2	0.001
CD4+CD25+	21.43 ± 2.70	20.80 ± 2.30	Ns
CD4+CD25+Intermidiate	14.69 ± 1.94	15.66 ± 1.95	Ns
CD4+CD25+highFoxp3+(Treg)	1.57 ± 0.71	1.16 ± 0.36	0.03
B Lymphocytes CD 19+	13.94 ± 3.16	12.49 ± 3.20	Ns
Breg cells	5.83 ± 2.29	3.55 ± 0.97	0.0001

Data were expressed as Mean ± SD

Abbreviations: NSCLC non-small cell lung cancer, Treg regulatory T cells, Breg regulatory B cells

The peripheral Treg cells were significantly higher in NSCLC patients compared to healthy controls (P = 0.03). Furthermore, the frequency of Bregs was significantly higher in NSCLC patients (P = 0.0001) (Table 2).

Association of Breg and Treg cells with different clinical status classifications

The frequency of Bregs and Tregs was significantly higher in NSCLC patients with distant metastasis compared to patients without metastasis (P = 0.02, P = 0.03) respectively (Fig. 4).

Fig. 4.

Difference in the frequency of circulating Breg cells and Treg cells in 40 NSCLC patients with different clinical status. There was a significant difference in the frequency of (a) Breg cells (P = 0.02) (b) Treg cells (P = 0.03) between NSCLC patients with distant metastasis (n = 20) and NSCLC patients without distant metastasis (n = 20)

Association of Breg and Treg cells with histopathological status

The histopathological examination classified the patients in the cohort under study into 4 groups; lung adenocarcinoma (n = 19), lung squamous cell cancer (SCC) (n = 14), undifferentiated carcinoma (n = 4) and large cell carcinoma (n = 3). The frequency of Breg and Treg cells was significantly associated with the clinicopathological status (P = 0.02, P = 0.01), respectively, with a significant increase in both Treg and Breg cells in NSCLC patients with adenocarcinoma histopathological type (Fig. 5).

Fig. 5.

Difference in the frequency of circulating Breg cells and Treg cells in NSCLC patients with different histopathological classification. There was a significant difference in the frequency of (a) Breg cells (P = 0.02) (b) Treg cells (P = 0.01) among NSCLC patients with different histopathological classification (adenocarcinoma n = 19), (Squamous cell carcinoma n = 14), (Undifferentiated carcinoma n = 4) and (large cell carcinoma n = 3). Kruska-Walis test used to calculate P-value, P value considered significant when < 0.05

Association of Breg and Treg cells with different TNM stages

The circulating Breg cells and Treg cells were significantly increased in NSCLC patients with stage IV (n = 20) than stage III (n = 15) and stage II (n = 5) (P = 0.02), (P = 0.04) respectively (Fig. 6). Interestingly, a significant positive correlation was observed between the frequency of Bregs and the frequency of Tregs (r = 0.37, P = 0.01) (Fig. 7).

Fig. 6.

Difference in the frequency of circulating Breg cells and Treg cells in 40 NSCLC patients with different TNM stages. There was a significant difference in the frequency of (a) Breg cells (P = 0.02) (b) Treg cells (P = 0.04) among NSCLC patients with different TNM stages, (stage II, n = 5), (stage III, n = 15) and (stage IV, n = 20). Kruska-Walis test used to calculate P-value, P value considered significant when < 0.05

Fig. 7.

Correlation between the percentage of Breg cells and Treg cells in NSCLC patients. A significant positive correlation was found between percentage of Breg cells and percentage of Treg cells (r = 0.37, P = 0.01). P value considered significant when < 0.05 r; spearman correlation factor

Cytokine profile levels in the cohorts under study

Table 3, comparatively showed the serum cytokine levels in patients and healthy control group. NSCLC patients had statistically significant higher values of all serum cytokine levels when compared with the healthy control group (P < 0.0001) for each.

Table 3.

Serum levels of cytokine profile between patients and control group

Cytokines pg/ml	NSCLC (n = 40)	Healthy controls (n = 40)	P-value
IL10	94.71 ± 7.89	17.10 ± 5.44	0.0001
IL35	37.42 ± 4.15	23.44 ± 3.39	0.0001
IL6	137.10 ± 18.01	19.03 ± 4.26	0.0001
IL17	31.54 ± 3.64	5.59 ± 1.61	0.0001
TGF-β1	90.40 ± 8.29	28.26 ± 6.06	0.0001
TNF-α	253.70 ± 122.70	11.01 ± 7.77	0.0001
IL1β	77.89 ± 10.09	7.03 ± 3.53	0.0001

In addition, NSCLC patients with distant metastasis had significantly higher serum levels of IL10 (P = 0.04), IL35 (P = 0.02), IL6 (P = 0.02), IL17 (P = 0.03), TGFβ1(P = 0.04) TNF-α (P = 0.03) than NSCLC patients without distant metastasis except for IL1β (Table 4).

Table 4.

Serum cytokines levels obtained in NSCLC patients according to distant metastasis

Cytokines pg/ml	NSCLC with distant metastasis (n = 20)	NSCLC without distant metastasis (n = 20)	P-value
IL10	96.76 ± 8.14	92.06 ± 6.95	0.04
IL35	39.70 ± 4.24	36.89 ± 4.06	0.02
IL6	142.8 ± 14.85	130.8 ± 19.13	0.02
IL17	32.75 ± 3.14	30.52 ± 3.02	0.03
TGF-β1	91.87 ± 6.46	88.02 ± 8.89	0.04
TNF-α	289.80 ± 106.40	218.40 ± 104.30	0.03
IL1β	78.05 ± 10.15	77.18 ± 9.37	Ns

No significant difference in the mean serum levels of all cytokines between NSCLC patients with different histopathological classifications.

There was a significant difference in the mean of IL10 (P = 0.03), IL6 (P = 0.03), IL17 (P = 0.02), TNF ( P = 0.01) and TGF ( P = 0.04) but not in IL35 and IL1β between NSCLC patients with different TNM stages (Table 5).

Table 5.

Serum cytokines levels in NSCLC patients according to TNM stages

Cytokines pg/ml	NSCLC with stage II (n = 5)	NSCLC with stage III (n = 15)	NSCLC with stage IV (n = 20)	P-value
IL10	84.76 ± 90.56	74 ± 111.13	86 ± 112.20	0.03
IL35	34.55 ± 2.80	36.75 ± 3.96	38.42 ± 4.41	Ns
IL6	120.30 ± 7.33	136.40 ± 17.72	140.50 ± 15.11	0.03
IL17	29.07 ± 1.82	30.05 ± 2.20	31.89 ± 3.37	0.02
TGF-β1	88.68 ± 7.30	89.88 ± 6.14	95.33 ± 6.64	0.04
TNF-α	149.60 ± 4.53	245.10 ± 98.11	290.90 ± 99.74	0.01
IL1β	78.36 ± 6.50	75.31 ± 11.75	79.56 ± 11.81	Ns

Univariate logistic regression analysis

In the univariate analysis, Bregs were significantly and inversely associated with lymph node involvement (OR = 0.662, 95% CI: 0.444–0.987, p = 0.043), indicating that higher Breg frequencies were associated with reduced odds of nodal spread. CD8⁺ T cells were significantly inversely associated with advanced tumor grade (OR = 0.923, 95% CI: 0.853–0.998, p = 0.045), suggesting that higher CD8⁺ T cell counts were linked to lower-grade disease. The CD4/CD8 ratio was significantly and positively associated with advanced tumor grade (OR = 14.385, 95% CI: 1.025–201.848, p = 0.048), indicating that an elevated CD4/CD8 ratio was associated with higher-grade tumors. Additionally, CD8⁺ T cells demonstrated a borderline trend toward association with distant metastasis (OR = 1.065, 95% CI: 0.989–1.148, p = 0.097), which did not reach statistical significance. No other variables reached significance in the univariate analysis (all p > 0.05). Full univariate results are presented in Table 6.

Table 6.

Univariate Logistic Regression Analysis of Predictors of Disease Progression in NSCLC Patients (n = 40)

Variable	Outcome	OR	95% CI	p-value
Distant Metastasis
Age	Distant Metastasis	1.033	0.966–1.104	0.346
Sex (Male)	Distant Metastasis	0.815	0.232–2.860	0.749
Smoking	Distant Metastasis	0.444	0.125–1.575	0.209
Bregs	Distant Metastasis	1.184	0.859–1.632	0.304
Tregs (FoxP3)	Distant Metastasis	1.012	0.984–1.040	0.421
Tregs (CD25 + FoxP3)	Distant Metastasis	1.638	0.540–4.972	0.384
CD4⁺ T cells	Distant Metastasis	1.009	0.929–1.095	0.840
CD8⁺ T cells	Distant Metastasis	1.065	0.989–1.148	0.097
CD4/CD8 Ratio	Distant Metastasis	0.136	0.011–1.630	0.115
T Lymphocytes	Distant Metastasis	1.021	0.957–1.089	0.537
B Lymphocytes	Distant Metastasis	1.044	0.858–1.270	0.668
Total Lymphocytes	Distant Metastasis	1.001	0.944–1.060	0.986
Lymph Node Involvement
Age	Lymph Node Involvement	0.955	0.873–1.046	0.322
Sex (Male)	Lymph Node Involvement	0.771	0.157–3.793	0.750
Smoking	Lymph Node Involvement	0.259	0.045–1.486	0.130
Bregs	Lymph Node Involvement	0.662	0.444–0.987	0.043*
Tregs (FoxP3)	Lymph Node Involvement	1.030	0.983–1.079	0.219
Tregs (CD25 + FoxP3)	Lymph Node Involvement	0.511	0.143–1.822	0.301
CD4⁺ T cells	Lymph Node Involvement	0.935	0.840–1.040	0.214
CD8⁺ T cells	Lymph Node Involvement	0.982	0.899–1.073	0.688
CD4/CD8 Ratio	Lymph Node Involvement	7.272	0.322–164.397	0.212
T Lymphocytes	Lymph Node Involvement	0.994	0.918–1.077	0.892
B Lymphocytes	Lymph Node Involvement	0.916	0.724–1.159	0.466
Total Lymphocytes	Lymph Node Involvement	1.046	0.967–1.133	0.263
Advanced Tumor Grade (III–IV vs. II)
Age	Advanced Tumor Grade	0.998	0.935–1.065	0.951
Sex (Male)	Advanced Tumor Grade	2.109	0.585–7.601	0.254
Smoking	Advanced Tumor Grade	1.857	0.522–6.612	0.339
Bregs	Advanced Tumor Grade	1.006	0.742–1.363	0.972
Tregs (FoxP3)	Advanced Tumor Grade	0.965	0.925–1.007	0.103
Tregs (CD25 + FoxP3)	Advanced Tumor Grade	0.835	0.288–2.424	0.741
CD4⁺ T cells	Advanced Tumor Grade	1.046	0.959–1.140	0.310
CD8⁺ T cells	Advanced Tumor Grade	0.923	0.853–0.998	0.045*
CD4/CD8 Ratio	Advanced Tumor Grade	14.385	1.025–201.848	0.048*
T Lymphocytes	Advanced Tumor Grade	1.027	0.962–1.096	0.431
B Lymphocytes	Advanced Tumor Grade	1.111	0.900–1.371	0.328
Total Lymphocytes	Advanced Tumor Grade	1.013	0.955–1.075	0.671
Advanced T Stage (T3–T4 vs. T2)
Age	Advanced T Stage	0.989	0.911–1.074	0.793
Sex (Male)	Advanced T Stage	0.378	0.066–2.162	0.274
Smoking	Advanced T Stage	0.529	0.108–2.598	0.433
Bregs	Advanced T Stage	0.912	0.637–1.304	0.610
Tregs (FoxP3)	Advanced T Stage	0.988	0.944–1.033	0.594
CD4⁺ T cells	Advanced T Stage	0.949	0.870–1.035	0.239
CD8⁺ T cells	Advanced T Stage	1.003	0.924–1.089	0.937
CD4/CD8 Ratio	Advanced T Stage	1.203	0.057–25.38	0.900
T Lymphocytes	Advanced T Stage	0.977	0.898–1.062	0.589
B Lymphocytes	Advanced T Stage	0.977	0.780–1.224	0.840
Total Lymphocytes	Advanced T Stage	1.023	0.946–1.107	0.558

OR Odds Ratio, CI Confidence Interval

* p < 0.05 (statistically significant)

Multivariate logistic regression

Following univariate screening, multivariate logistic regression models were constructed for each outcome. Results are summarized in Table 7.

Table 7.

Multivariate Logistic Regression Analysis of Independent Predictors of Disease Progression in NSCLC Patients (n = 40)

Outcome	Variable	β	SE	OR	95% CI	Wald Z	p-value
Distant Metastasis — AIC = 61.91; Nagelkerke R² = 0.113; HL χ² = 3.415, df = 8
Distant Metastasis	CD8⁺ T cells	0.048	0.062	1.049	0.928–1.186	0.775	0.441
Distant Metastasis	CD4/CD8 Ratio	0.026	2.964	1.026	0.004–272.1	0.009	0.993
Distant Metastasis	Bregs	0.121	0.204	1.129	0.756–1.686	0.593	0.553
Distant Metastasis	Tregs (FoxP3)	0.009	0.018	1.009	0.974–1.045	0.490	0.624
Lymph Node Involvement — AIC = 38.98; Nagelkerke R² = 0.382; HL χ² = 13.900, df = 8
Lymph Node Involvement	Bregs	−0.408	0.259	0.665	0.400–1.106	−1.575	0.116
Lymph Node Involvement	Smoking	−2.179	1.172	0.113	0.011–1.133	−1.860	0.064
Lymph Node Involvement	Tregs (FoxP3)	0.021	0.031	1.021	0.960–1.086	0.673	0.501
Lymph Node Involvement	CD4⁺ T cells	−0.090	0.061	0.914	0.811–1.031	−1.469	0.142
Advanced Tumor Grade – III–IV vs. II — AIC = 57.56; Nagelkerke R² = 0.215; HL χ² = 3.008, df = 8
Advanced Tumor Grade	CD8⁺ T cells	−0.045	0.066	0.956	0.840–1.088	−0.675	0.500
Advanced Tumor Grade	CD4/CD8 Ratio	1.294	2.630	3.648	0.021–628.9	0.492	0.623
Advanced Tumor Grade	Tregs (FoxP3)	−0.020	0.023	0.981	0.937–1.027	−0.845	0.398
Advanced Tumor Grade	Bregs	0.108	0.225	1.114	0.716–1.733	0.480	0.631
Advanced T Stage – T3–T4 vs. T2 — AIC = 46.64; Nagelkerke R² = 0.128; HL χ² = 12.250, df = 8
Advanced T Stage	CD4⁺ T cells	−0.077	0.054	0.926	0.833–1.030	−1.418	0.156
Advanced T Stage	Smoking	−0.861	0.866	0.423	0.077–2.310	−0.994	0.320
Advanced T Stage	Bregs	−0.067	0.185	0.935	0.650–1.346	−0.362	0.717
Advanced T Stage	T Lymphocytes	−0.027	0.047	0.974	0.887–1.069	−0.567	0.571

No variable reached p < 0.05 after multivariate adjustment

OR Odds Ratio, CI Confidence Interval, SE Standard Error, β = regression coefficient, AIC Akaike Information Criterion, HL Hosmer–Lemeshow

None of the immune variables reached statistical significance in any of the multivariate models after adjustment (all p > 0.05). The lymph node involvement model demonstrated the best overall fit (Nagelkerke R² = 0.382; AIC = 38.98), with Bregs (OR = 0.665, 95% CI: 0.400–1.106, p = 0.116) and smoking status (OR = 0.113, 95% CI: 0.011–1.133, p = 0.064) showing the strongest trends. In the distant metastasis model, CD8⁺ T cells and Bregs showed trends in the expected biological directions, though without reaching significance (Nagelkerke R² = 0.113). Similarly, in the advanced tumor grade model, a higher CD4/CD8 ratio and lower Tregs (FoxP3) trended toward association with more advanced grade (Nagelkerke R² = 0.215). The absence of statistical significance in multivariate models is likely attributable to the limited sample size (n = 40), which reduces power when multiple predictors are modeled simultaneously. Nonetheless, the consistent directionality of OR estimates across all models for Bregs, Tregs, and the CD4/CD8 ratio is biologically plausible and aligned with established immunosuppressive mechanisms in NSCLC. These findings support the potential prognostic relevance of regulatory immune cell populations in NSCLC and highlight the need for validation in larger prospective cohorts.

Discussion

The immune system plays a pivotal role in the control of tumor progression. Even though the regulatory mechanism of interaction between innate and adaptive immune systems remains unclear. Considering the tumor-promoting function of B cells along with the immune-regulating role of Bregs, current studies have focused on the part of Bregs in cancer [34]. Herein, we evaluated the numeric frequency of circulating Breg cells and Treg cells, and we analyzed serum cytokine levels and their association with disease progression.

In the present study, patients with NSCLC showed a higher number of CD8 + T-cells and decreased CD4/CD8 ratio. The association between CD8 + T lymphocyte expression and overall survival of NSCLC patients has been established in different studies [35, 36]. In contrast, no significant correlation was found between CD8 + tumor-infiltrating lymphocytes (until) infiltration and the outcome of patients with NSCLC [37].

The function of CD8 + T cells in the tumor microenvironment (TME) depends on their interaction with tumor cells and or other cells of the TME through multiple mechanisms, e.g., downregulation, or loss of expression of cell-surface MHC Class II protein expression (immunoediting), the release of immunosuppressive factors (IL-10 and transforming growth factor-β), i.e., CD8 + T cells might have diverse functions in a tumor microenvironment [38]. These mechanisms all together contribute to CD8 + T-cell dysfunction and immune escape, which are key the features of tumor immune evasion in NSCLC and may partially explain disease progression despite the presence of tumor-reactive lymphocytes [39, 40].

Increased circulating Breg cell frequency, namely, CD19 + CD24hiCD38hi in the peripheral blood of lung cancer patients, was detected in the study cohort. Several studies found that Breg cells reside in different tumor types, including breast, colorectal, cervical, ovarian, and prostate carcinomas [41]. A study found an increase in Breg cells’ frequency among lung cancer patients, which was modulated directly by lung cancer cells [42]. Cancer-derived factors or inflammation-activated cancer cells or their products and the interaction of lung cancer cells with peripheral blood mononuclear cell (PBMCs) can play a critical role in expanding Bregs and the frequency of CD19 + B cells, which explain the increased proportion of Breg cells and CD19 + B cells. The influencing functions of inflammation-activated cancer cells in the frequency of Breg cells are associated with inflammation severities [42].

Human Breg phenotypes include transitional B cells (CD19 + CD24hiCD38hi), B regulatory 1 cells (Br1) (CD19 + CD25+CD71 + CD73−), plasma blasts (CD19 + CD27intCD38+), and B10 cells with the CD19 + CD24hiCD27+ phenotype [26]. Studies also revealed the immune-suppressing activity of B cells was mainly through IL-10 secretion. B cells secreting IL-35 can impair the immune response of Breg cells and contribute to T cell exhaustion with subsequent weak antitumor immune responses in NSCLC [43]. Furthermore, Bregs may contribute to the establishment of an immunosuppressive tumor microenvironment through the induction of regulatory T cells and suppression of effector T-cell responses, thereby facilitating tumor growth and metastatic dissemination [44].

In this study, circulating Treg cells, especially CD4⁺CD25⁺Foxp3⁺ in NSCLC patients, increased significantly compared with healthy groups, which supported previous reports [45]. Foxp3 expressing Treg cells mediate their inhibitory function in the tumor microenvironment through perforins and granzyme B production [46]. Our findings in the peripheral blood compartment are concordant with this tissue-based literature: the elevated circulating Bregs and Tregs, their significant positive inter-correlation, and their stepwise increase across TNM stages reflect the same immunosuppressive Breg–Treg axis documented within tumors one in which Bregs promote Treg expansion and FoxP3/CTLA-4 upregulation through direct cell contact and IL-10/IL-35 secretion Breg trafficking from peripheral reservoirs into the TME has been directly confirmed in NSCLC, supporting the interpretation that elevated circulating Bregs represent functionally relevant precursors to the intra-tumoral suppressor pool While tissue-resident populations offer spatial resolution and direct mechanistic insight into TME immunosuppression, circulating markers provide the critical advantages of non-invasive accessibility, suitability for serial longitudinal monitoring, and applicability across all disease stages — including inoperable advanced disease. The serum cytokine signature documented here (particularly elevated IL-10, IL-35, and TGF-β1 in metastatic patients) captures the soluble output of both compartments simultaneously and adds functional depth to phenotypic cell-counting alone. Future studies integrating paired peripheral blood and tumor tissue sampling will be essential to formally establish the fidelity of circulating Bregs/Tregs as surrogate biomarkers for their tissue-resident counterparts in NSCLC [47, 48].

Of interest, a significant association between Breg cell frequency and advanced stages (metastatic) in lung cancer patients was observed in the present study. Previous studies have found that the primary tumor’s potent B cell infiltrates were associated with a lower risk of distant metastasis [49]. Simultaneously, others found the exact opposite [50, 51]. Moreover, we also found that Treg cells’ frequency was significantly associated with advanced stages (those with metastasis) in NSCLC patients, in agreement with the observation that peripheral blood Treg numbers were increased only during later cancer stages [52]. However, in a recent study, it was found that tumor-specific increases in Treg numbers within lung LNs, even in early stages, when LNs are free of metastasis. Tregs may induce the expansion of B10 cells, which facilitate the interplay between these two cells. The cross-talk between Breg cells and Treg cells can enhance tumor growth and metastasis by promoting the expression of CTLA-4 (marker for the suppressive capability of the function of Tregs) and Foxp3 in Tregs via cell-to-cell contact [53–55]. The observed increase in both Breg and Treg frequencies in advanced NSCLC may reflect the development of a systemic immunosuppressive environment associated with disease progression. The interaction between these regulatory cell populations may contribute to immune checkpoint activation, including PD-1/PD-L1 signaling pathways, resulting in impaired antitumor immunity and enhanced tumor immune escape [39, 40].

Inflammatory mediators such as cytokines and growth factors released by immune cells play an essential role in promoting tumor progression and metastasis [56].Th1 cells produce proinflammatory cytokines (e.g., IFN-γ, TNF-α) as part of the cell-mediated immune response while Th2 cells regulate humoral immunity through anti-inflammatory cytokines (e.g., IL-4 and IL-10) expression.

Some studies have shown that cytokines measured in various biological fluids, like plasma, can reflect several diseases, including cancer [57, 58]. We studied the association between the serum levels of cytokines and lymph node metastasis among NSCLC patients. We found significant higher serum levels of IL10, IL35, IL6, IL17, TNF- a, and TGF-b in patients with distant metastasis. Previous data showed that the tumor tissue from NSCLC patients has a higher expression of Th2 cytokine genes than Th1 cytokine genes [59].

Several studies have reported that overexpression of IL-6 is associated with tumor stage or metastases, which came in line with our results [60, 61]. Additionally, IL 17a, a proinflammatory cytokine produced by Th17 cells, exerts a complex effect on the growth and metastasis of lung cancer cells [62, 63].

Interleukin (IL)-35 is a newly identified IL-12 cytokine family member and showed immunosuppressive activity on CD8⁺ T cells in inflammation, infectious diseases, and cancer. A recent study reported that IL-35 attributed to CD8⁺ T-cell dysfunction and controlled the antitumor immune response in osteosarcoma [64]. Moreover, IL-35 contributes to T cells’ exhaustion/dysfunction and limited antitumor immune responses in NSCLC [43].

TGFβ is the most potent contributor of epithelial-mesenchymal transition in non-small cell lung cancer cells and progression of the tumor-promoting microenvironment in the lung cancer tissue [65]. Elevations in TGFβ1 levels in plasma have been shown in patients with lung cancer compared with nonmalignant pulmonary disease and healthy individuals [66].

Collectively, the observed cytokine profile suggests a tumor-promoting inflammatory milieu that may cooperate with regulatory immune cells to facilitate disease progression. Furthermore, several of these cytokines have been linked to VEGF-mediated angiogenesis and vascular remodeling, processes that promote tumor growth, metastatic spread, and immune exclusion within the tumor microenvironment [67].

Despite our study focusing on circulating immune cells, previous investigations of tumor-infiltrating regulatory immune populations have reported similar associations with poor clinical outcomes. Therefore, circulating Bregs and Tregs may represent accessible surrogate biomarkers reflecting immune alterations occurring within the tumor microenvironment [68].

Similar interactions between immune regulatory cells, cytokine signaling, and stromal remodeling have been described in several aggressive malignancies, underscoring the role of immune dysregulation in tumor progression across cancer types.

Emerging evidence also suggests that autophagy plays an important role in tumor immune regulation and cancer progression by modulating inflammatory signaling, immune cell survival, and tumor microenvironment remodeling. Dysregulated autophagy has been implicated in non-small cell lung cancer progression and may interact with cytokine networks and immune checkpoint pathways, thereby contributing to immune escape and tumor aggressiveness. These findings provide an additional layer of complexity linking cellular stress responses with immune dysregulation in NSCLC [69–73].

The limitation of our study was the small number of patients with NSCLC that were included in the study. Second, we don’t have cases with stage 1 disease, because unfortunately most of lung cancer patients in our locality present at later stage of the disease. This in agreement with recent study in our community that recruited lung cancer patients in more than 3 years study where stage I disease was diagnosed in only 25% of the study population [74]. Additionally, the absence of stage I patients limits our ability to characterize immune alterations during the earliest phases of NSCLC development. Future prospective studies including larger cohorts and early-stage disease are needed to validate these findings and determine their potential prognostic utility. Furthermore, the observational nature of the study allows identification of associations but does not establish causal relationships between immune alterations and disease progression. The strength of our study was the use of non-invasive blood test to correlate with the diagnosis and the progression of malignant disease.

Conclusions

Peripheral blood Breg cells, Treg cells, and serum cytokine profiles, including proinflammatory and anti-inflammatory cytokines were increased in NSCLC patients along with an association with advanced stages of NSCLC.

Abbreviations

Bregs

Regulatory B cells

CTLS

Cytotoxic T lymphocyte

FACS

Fluorescence Activated Cell Sorter

FITC

Fluoroisothiocyanate

IL

Interleukin

MHC

Major histocompatibility

MFI

Mean fluorescence intensity

NSCLC

Non-small cell lung cancer

PE

Phycoerythrin

PMCs

Peripheral blood mononuclear cells

TGF-β2

Transforming growth factor –beta

TME

Tumor microenvironment

Tregs

Regulatory T cells

Authors’ contributions

All authors contributed to conceptualization, design, investigations, methodology, analysis, funding acquisition, Writing, and reviewing. All authors approved the manuscript for submission.

Funding

None.

Data availability

All generated data are included in the published manuscript.

Declarations

Ethics approval and consent to participate

The study was approved by the ethics committee of the faculty of medicine, Sohag University. (Reference number No. Soh.Med-20.12.16)

All procedures followed were in accordance with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all participants in the study.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.