Dynamic changes in peripheral B and NK cells reflect treatment response in breast cancer subtypes

Abstract

Background

The immune microenvironment plays a critical role in breast cancer progression and therapeutic response. Among circulating immune cells, B cells and natural killer (NK) cells have shown distinct and potentially opposing roles in tumor dynamics. However, whether dynamic changes in peripheral blood B- and NK-cell percentages are associated with treatment response across molecular subtypes of breast cancer remains unclear.

Methods

Peripheral blood samples were collected from breast cancer patients before and after standardized subtype-guided therapy (chemotherapy, anti-HER2 therapy, and/or endocrine therapy according to clinical guidelines). Flow cytometry was performed to quantify the percentage of B cells and NK cells within CD45 + lymphocytes. Spearman’s rank correlation was used to evaluate associations between B and NK cell percentages. Paired comparisons were performed using the Wilcoxon signed-rank test. Treatment efficacy was defined as objective response rate (complete response + partial response, CR + PR) according to RECIST criteria. Patients with decreased B cell percentage and increased NK cell percentage after treatment were defined as the B-NK group.

Results

Subtype A (-- group) exhibited significantly higher B cells percentages and lower NK cells percentages compared with other subtypes (P < 0.05). A significant negative correlation between B and NK cell percentages was observed in overall cohort and in most subtypes. In subtype A, B cell percentage significantly decreased after treatment (P < 0.05), whereas NK cell percentage showed variable changes. Patients classified into the B-NK group tended to have a higher objective response rate compared with non-B-NK patients across subtypes.

Conclusions

Peripheral blood B and NK cell percentages exhibit dynamic and partially coordinated changes during treatment. A pattern characterized by decreased B cell percentage and increased NK cell percentage may be associated with treatment response. These findings are exploratory and hypothesis-generating, and further validation in prospective, well characterized cohorts is required before potential clinical application.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-026-05119-5.

Introduction

Breast cancer is one of the most prevalent malignancies among women worldwide and remains a leading cause of cancer-related mortality [1]. It is a heterogeneous disease with varying clinical and molecular characteristics, necessitating individualized treatment approaches. Based on gene expression profiling, this disease is commonly categorized into four major molecular subtypes: luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple-negative breast cancer (TNBC) [2]. Luminal A tumors are estrogen receptor (ER)-positive and exhibit low proliferation rates, generally associated with favorable prognoses. Luminal B tumors are also ER-positive but have higher proliferation rates and worse outcomes compared to luminal A. HER2-enriched subtypes are characterized by overexpression of HER2 and are often more aggressive, though targeted therapies have improved outcomes significantly. TNBC, lacking ER, progesterone receptor (PR), and HER2 expression, is associated with poor prognosis, early relapse, and limited treatment options [3]. In addition to these molecular subtypes, recent studies have underscored the importance of the tumor immune microenvironment in breast cancer pathogenesis. Among the immune components, B cells have emerged as significant players, potentially influencing disease progression and patient outcomes [4].

B cells play a key role in adaptive immunity by mediating humoral immune responses. In cancer, their roles extend beyond antibody production to include antigen presentation, cytokine secretion, and interactions with other immune cells. Their influence on tumor progression is dichotomous, with evidence supporting both tumor-promoting and tumor-inhibiting roles depending on the context [5, 6]. B cells can exert protective effects by producing antibodies against tumor-associated antigens, facilitating the destruction of tumor cells via complement activation or antibody-dependent cell-mediated cytotoxicity (ADCC) [7, 8]. Additionally, they present antigens to T cells and secrete pro-inflammatory cytokines such as IL-6 and TNF-α, which enhance anti-tumor immunity [9].

On the other hand, some subsets, such as regulatory B cells (Bregs), promote immune evasion by suppressing T-cell-mediated responses through the secretion of immunosuppressive cytokines like Interleukin-10 (IL-10) and transforming growth factor-beta (TGF-β) [10, 11]. These mechanisms facilitate an immunosuppressive tumor microenvironment, supporting tumor growth and metastasis. In breast cancer, the presence of tumor-infiltrating Bregs has been correlated with poor clinical outcomes [12–14].

In addition to B cells, natural killer (NK) cells are also key components of the innate immune system with well-documented roles in tumor surveillance. NK cells can recognize and kill malignant cells without prior sensitization and are particularly important for mediating ADCC, especially in HER2-positive breast cancer where monoclonal antibodies like trastuzumab are commonly used [15]. Moreover, the activity and abundance of NK cells have been linked to clinical outcomes in TNBC, making them a relevant focus for peripheral immune profiling [16].

Recent studies have explored the relationship between B cells and various molecular subtypes of breast cancer. In HER2-enriched breast cancer, tumor-infiltrating B cells have been shown to correlate with better survival outcomes, highlighting their potential role in immune surveillance [17, 18]. In contrast, in TNBC, high levels of regulatory B cells are associated with poor prognosis, suggesting that Bregs may contribute to the immunosuppressive tumor microenvironment. This study aims to investigate the percentages of B cells and NK cells in the peripheral blood of patients with different breast cancer subtypes before and after treatment, as well as the changes in these proportions. By identifying potential patterns, the findings are expected to provide a reliable foundation for the development of future clinical treatment strategies.

Materials and methods

Subjects

Following written informed permission, 265 Breast cancer patients (ages 29–83) who were initially hospitalized to Tianjin Cancer Hospital Airport Hospital had peripheral blood samples taken. Post-treatment peripheral blood samples were collected approximately 4–6 weeks after the completion of initial treatment (surgery and/or systemic therapy) during routine clinical follow-up. Standard treatment strategies were applied according to current clinical guidelines and based on molecular subtype classification. These included surgery, chemotherapy, radiotherapy, endocrine therapy, and targeted therapy (e.g., anti-HER2 treatment for HER2-positive patients). All patients underwent surgery and chemotherapy. Endocrine therapy was administered to hormone receptor-positive patients. Anti-HER2 targeted therapy was administered to HER2-positive patients. Radiotherapy was delivered when clinically indicated. A summary of treatment modalities by subtype is presented in Table 1. After standardized treatment, all patients were re-evaluated for the percentages of B cells and NK cells in their peripheral blood. Additionally, their treatment efficacy was assessed during subsequent hospital visits. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was also approved by the Ethics Committee of Tianjin Cancer Hospital Airport Hospital in September 2024. The approval number is No. 2024BWKZ09. All patients provided written informed consent upon hospital admission, acknowledging that their test data could be used for clinical research conducted at our institution.

Table 1.

Clinical subtype distribution and treatment regimens of breast cancer patients

Subtype	A	B	C	D
Definition	ER- and PR-negative, HER2-negative; --; TNBC	ER- or PR-positive, HER2-negative; +-; Luminal A	ER- and PR-negative, HER2-positive; -+; HER2-enriched	ER- or PR-positive, HER2-positive; ++; Luminal B
Numbers of Patients	44	83	28	110
Surgery	√	√	√	√
Chemotherapy	√	√	√	√
Radiotherapy	±	±	±	±
Endocrine Therapy	×	√	×	√
Anti-HER2 Therapy	×	×	√	√
B cells(%)	16.67 ± 8.944	12.83 ± 5.617	12.38 ± 5.625	13.18 ± 6.369
NK Cells(%)	10.61 ± 4.975	12.80 ± 6.168	15.25 ± 7.900	12.00(7.900,17.00)

√ = received; × = not received; ± = given as indicated depending on individual clinical factors

ER: estrogen receptor; PR: progesterone receptor; HER2: human epidermal growth factor receptor 2; NK: natural killer

Methods

All PBMC samples were freshly isolated from peripheral blood on the same day of collection using Ficoll density gradient centrifugation. Following FcR blocking, cells were treated with antibodies that had been diluted properly before being rinsed with PBS. Peripheral blood cells were first counted, and the cell concentration was adjusted to 1 × 10⁶ cells/mL. Then, 100 µL of peripheral blood was added to each flow cytometry tube, followed by the addition of 10 µL of antibody cocktail for cell surface staining. After incubation in the dark at room temperature, cells were washed with 1 mL PBS, centrifuged at 1000 rpm for 5 min, and the supernatant was discarded. The cell pellet was then resuspended in 200 uL PBS, gently mixed, and analyzed by flow cytometry. Anti-human CD45-FITC/CD3-PC5.5/CD19-ECD/CD(16 + 56)-PE (Beckman Coulter) were among the antibodies utilized for surface staining in this investigation. The Navios flow cytometer (Beckman Coulter) was used for acquisition. To analyze the data, Beckman Coulter’s Kaluza software was used. Peripheral blood was collected from all patients at baseline (prior to the initiation of any treatment) and again post-treatment. Mononuclear cells were isolated via density gradient centrifugation and stained with antibodies against CD45, CD3, CD19, CD16, and CD56. B cells were defined as CD3-CD19+, and NK cells were defined as CD3-CD16 + CD56+. The percentages of B and NK cells were calculated as a percentage of total CD45 + lymphocytes. Figure 1 illustrates the flow cytometry analysis procedure. Delta B and Delta NK values were calculated as the absolute difference between post-treatment and pre-treatment percentages of B cells and NK cells, respectively. These proportions are expressed as percentages of CD45 + lymphocytes, not as relative percentage changes.

Fig. 1.

Representative flow cytometry gating strategies for B cells and NK cells in the peripheral blood of breast cancer patients

Statistical analysis

All statistical analyses were conducted using SPSS Statistics 21 (IBM Corporation, NY, USA). The mean and standard deviation were employed to describe the numerical data since the Kolmogorov-Smirnov test indicated that the data had a normal distribution. To compare numerical data, the independent sample t-test was employed. The nonparametric t-test and Kruskal-Wallis test were used to determine statistical significance when the distribution matched the nonnormal distribution. The median and interquartile range were used to display the data. Spearman`s rank correlation was used to assess the relationship between B cell and NK cell proportions. The threshold for statistical significance was set at P < 0.05.

Results

Distinct immune profile of Group A breast cancer subtype: elevated B cells and reduced NK cells in peripheral blood

The classification of breast cancer subtypes is primarily based on the expression of three key biomarkers: ER, PR, and HER2. In this study, breast cancer patients were classified into four molecular subtypes based on ER, PR, and HER2 status: Group A (ER- and PR-negative, HER2-negative; ER-/PR-, HER2-; --; TNBC), Group B (ER- or PR-positive, HER2-negative; ER+/PR+, HER2-; +-; Luminal A), Group C (ER- and PR-negative, HER2-positive; ER-/PR-, HER2+; -+; HER2-enriched) and Group D (ER- or PR-positive, HER2-positive; ER+/PR+, HER2+; ++; Luminal B). This classification allows for a systematic understanding of the molecular heterogeneity of breast cancer and provides a basis for tailored therapeutic strategies.

We analyzed the percentages of B cells and NK cells in the peripheral blood of breast cancer patients using flow cytometry and assessed their expression levels across the four molecular subtypes of breast cancer. As shown in Fig. 2A, the study found that Group A (-- subtype) exhibited significantly higher B cell percentages compared to Group B (P = 0.0050), Group C (P = 0.0341), and Group D (P = 0.0079), respectively, while no significant differences were observed among the other three subtypes. Similarly, the percentage of NK cells in Group A was significantly lower than in the other three groups (P values: 0.0484, 0.0033, and 0.0482, respectively), while no significant differences were observed among the other subtypes, as shown in Fig. 2B.

Fig. 2.

Percentages of B cells (A) and NK cells (B) in the peripheral blood of patients across different breast cancer subtypes. Distribution of patients from different breast cancer subtypes when classified into high and low groups based on B cell percentages (C) and NK cell percentages (D) *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Consistent with these findings, patients were stratified into high and low groups based on the median percentages of B cells and NK cells. Group A was more frequently represented in the high B cell group and less frequently represented in the high NK cell group (Fig. 2C and D). These results suggest that Group A may exhibit a distinct peripheral immune profile characterized by relatively higher B cell percentages and lower NK cell percentages compared with other subtypes.

Correlation between B cells and NK cells: a immune profile across molecular groups

In this study, we analyzed the correlation between the percentages of B cells and NK cells in the peripheral blood of all breast cancer patients. The results revealed a negative correlation between the two cell populations (Fig. 3A, r=−0.2093, p = 0.0011). When the correlation analysis was performed separately for the four molecular subtypes of breast cancer, we observed a negative correlation between B cells and NK cells in three subtypes, except for Group B. As shown in Fig. 3B–E, the correlation coefficients and p-values are as follows: Group A: r=−0.3338, p = 0.0434; Group B: r=−0.1231, p = 0.3098; Group C: r=−0.4295, p = 0.0322; Group D: r=−0.2259, p = 0.0211.

Fig. 3.

Correlation analysis of B cell and NK cell percentages in peripheral blood among all breast cancer patients (A) and within each subtype (B–E). Spearman`s rank correlation was used to assess the relationship between B cell and NK cell percentages *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Taken together, these findings suggest a generally inverse relationship between B and NK cell percentages in peripheral blood across most molecular subtypes. However, this pattern was not exclusive to a single subtype and should be interpreted with caution.

Dynamics of B and NK cells in subtype A patients: identification of the B-NK group

In this study, we focused on patients from breast cancer subtype A as the observation group. The percentages of B cells and NK cells in peripheral blood were measured before and after treatment, and paired comparisons were conducted. The results showed that, in patients with subtype A breast cancer, the percentage of B cells in peripheral blood decreased significantly following standardized treatment (Fig. 4a). However, no consistent pattern was observed in the changes in NK cell percentages (Fig. 4b).

Fig. 4.

Changes in the percentages of B cells (A) and NK cells (B) in the peripheral blood of breast cancer subtype Group A (ER- and PR-negative, HER2-negative; ER-/PR-, HER2-; --) before and after treatment. Quadrant plot (C) and heatmap (D) illustrating the dynamics of B cell and NK cell changes *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

To further illustrate the changes in B cells and NK cells before and after treatment, we generated a heatmap summarizing these dynamics for subtype A patients, as shown in Fig. 4c. Delta B represents the difference between the post-treatment and pre-treatment percentages of B cells, while Delta NK refers to the difference between the post-treatment and pre-treatment percentages of NK cells. Red indicates a positive delta, meaning the proportion of cells increased after treatment, whereas blue indicates a negative delta, reflecting a decrease in cell proportions following treatment. Based on the changes in B cell and NK cell proportions, we classified subtype A patients into four subgroups, as depicted in Fig. 4d. Among these subgroups, we selected the group characterized by a decreased percentage of B cells and an increased percentage of NK cells after treatment (the blue group in Fig. 4c, defined as the B-NK group) for further exploratory analysis of treatment response.

Treatment response patterns in the B-NK group across breast cancer subtypes

Based on the aforementioned findings, we designated patients with a decreased percentage of B cells and an increased percentage of NK cells in peripheral blood after treatment as the B-NK group and conducted a comparative evaluation of treatment efficacy. First, we assessed the post-treatment response rate (complete response (CR) + partial response (PR)) in B-NK group patients within breast cancer subtype Group A and compared it with the overall response rate of all Group A patients after treatment. The results showed that the response rate in the B-NK group was 53.33%, higher than the 37.93% observed in the overall Group A, as shown in Fig. 5A–B. This suggests that patients in the B-NK group experienced better treatment outcomes and derived greater benefit from therapy compared to the broader Group A cohort.

Fig. 5.

Evaluation of treatment efficacy in each breast cancer subtype and the B-NK group within each subtype. A Treatment response (CR + PR) in the overall population of breast cancer subtype A. B Treatment response in the B-NK group within subtype A. C Treatment response in the overall population of breast cancer subtype B. D Treatment response in the B-NK group within subtype B. E Treatment response in the overall population of breast cancer subtype C. F Treatment response in the B-NK group within subtype C. G Treatment response in the overall population of breast cancer subtype D. H Treatment response in the B-NK group within subtype D CR: complete response; PR: partial response; SD: stable disease; PD: progressive disease

We further investigated whether the B-NK group exhibited similar effects in other breast cancer subtype groups. Specifically, for each Group (B, C, and D), patients meeting the criteria for the B-NK group were separately evaluated for their treatment efficacy and compared with the overall efficacy of their respective Group. The results showed that in all three Groups (B, C, and D), the treatment efficacy of the B-NK group was higher than the overall efficacy of the corresponding Group, as shown in Fig. 5C–H.

To further evaluate the predictive value of the B-NK group, we performed a direct comparison between the B-NK group and the non-B-NK group (i.e., patients who did not exhibit a simultaneous decrease in B cells and increase in NK cells after treatment) within each molecular subtype. The results demonstrated that across all subtypes, the B-NK group tended to show higher response rates (CR + PR) compared to the non-B-NK group. These findings are presented in Supplementary Figure A–D.

The overall results are shown in Fig. 6. In all breast cancer subtypes, based on the conclusions of this study, we further identified the B-NK pattern may be associated with improved treatment response. However, given the descriptive nature of the analysis and the lack of multivariate adjustment, this observation should be considered exploratory.

Fig. 6.

Comparative analysis of treatment efficacy between each breast cancer subtype and the B-NK group within each subtype Supplement Figure Evaluation of treatment efficacy in each non-B-NK group within each subtype. A Treatment response (CR + PR) in the non-B-NK group within subtype A. B Treatment response in the non-B-NK group within subtype B. C Treatment response in the non-B-NK group within subtype C. D Treatment response in the non-B-NK group within subtype D CR: complete response; PR: partial response; SD: stable disease; PD: progressive disease

Discussion

In this study, we systematically investigated the proportions of B cells and NK cells in the peripheral blood of breast cancer patients across different molecular subtypes, with a specific focus on their potential association with treatment response. Systemic therapies such as chemotherapy, endocrine therapy, and targeted therapy may influence circulating immune cell populations. Therefore, part of the observed changes in peripheral B and NK cell percentages may reflect treatment-induced immune remodeling rather than tumor-intrinsic biology alone. Our findings provide novel insights into the immune profiles of breast cancer patients and suggest that B and NK cell dynamics may be associated with therapeutic response.

Emerging evidence suggests that peripheral blood B cells may be associated with breast cancer progression and treatment response. Alterations in circulating B cell populations have been associated with disease progression and response to therapy [19]. For example, higher frequencies of circulating B cells, including memory B cells and plasmablasts, have been linked to advanced disease stages and metastasis [20]. Additionally, the presence of B cells in the blood may reflect the immune status of the patient and could be used to predict therapeutic responses, particularly in patients undergoing immunotherapy [21]. In the context of metastatic breast cancer, B cells may play a significant role in immune evasion by promoting tumor cell dissemination and suppressing anti-tumor immunity [22–24]. Conversely, in early-stage breast cancer, circulating B cells may indicate a more robust immune response and better clinical outcomes [25, 26]. Emerging evidence highlights the significance of peripheral B cells in breast cancer progression. Alterations in circulating B cell subsets, such as increased CD19 + or CD38 + B cells, have been linked to advanced disease stages and poor prognosis [10, 27, 28]. For instance, elevated frequencies of plasmablasts and memory B cells in the blood of breast cancer patients have been associated with immune dysregulation and worse outcomes [29, 30]. Furthermore, B cells within tertiary lymphoid structures (TLS) have been associated with improved survival in certain breast cancer subtypes, such as HER2-enriched and TNBC, suggesting their role in fostering anti-tumor immunity [31–33].

B cells in TNBC may contribute to the formation of TLS and are often associated with favorable prognosis in some contexts, but may also exhibit immunosuppressive functions in others. NK cells play a critical role in immune surveillance of TNBC and are particularly relevant in the context of antibody-based therapies and immune checkpoint inhibition, though their functional exhaustion is also reported in advanced TNBC. We first observed that the A subtype (-- group) exhibited a distinct immune profile, characterized by a significantly higher proportion of B cells and a lower proportion of NK cells compared to other subtypes. This pattern may reflect differences in systemic immune status; however, it should not be considered exclusive to this subtype. Further analysis revealed a negative correlation between B cells and NK cells in the peripheral blood of all breast cancer patients, except for subtype B. This correlation persisted across subtypes A, C, and D, indicating a potential interplay between these two immune cell populations. Based on these observations, we explored dynamic changes in B and NK cell percentages in Group A patients before and after treatment. We found that B cell percentages decreased following treatment, while NK cell percentages showed variable changes without a consistent trend. By categorizing patients based on these changes, we identified a subset of patients (the B-NK group) with decreased B cells and increased NK cells post-treatment. Interestingly, this group demonstrated a significantly higher response rate (53.33%) compared to the overall A group (37.93%), suggesting that patients with this immune profile are more likely to benefit from therapy. Similar trends were observed in other molecular subtypes, where the B-NK group tended to demonstrate higher response rates compared with the corresponding overall or non-B-NK populations.

These findings suggest that the B-NK pattern may be associated with treatment response across breast cancer subtypes. The observed association between the B-NK immune profile and improved treatment outcomes raises important questions about the underlying mechanisms. It is possible that a reduction in B cells, particularly immunosuppressive subsets such as regulatory B cells, alleviates immune suppression, thereby enhancing the anti-tumor activity of NK cells. Conversely, the increase in NK cells may reflect a shift towards a more activated immune state, capable of directly targeting tumor cells. These mechanisms warrant further investigation to elucidate the precise role of B and NK cells in modulating treatment response. From a clinical perspective, longitudinal monitoring of peripheral immune cell percentages may provide additional information regarding systemic immune changes during therapy. However, the clinical utility of the B-NK pattern remains to be established. Future studies are needed to determine whether this immune pattern has independent predictive value and whether it can be integrated into clinical decision-making. Among all immune dynamic patterns observed, the B-NK group, characterized by decreased B cell percentage and increased NK cell percentage after treatment, demonstrated the most consistent and favorable therapeutic response across molecular subtypes. This finding suggests that coordinated modulation of humoral and innate immunity may reflect an effective anti-tumor immune shift during therapy.

Despite the promising findings, this study has several limitations. First, the sample size, particularly within certain subgroups such as the B-NK group, was relatively small, which may limit the statistical power and generalizability of the results. Second, detailed clinicopathological parameters, including tumor grade, histological subtype, and full staging information, were not uniformly available for all patients, limiting our ability to perform comprehensive correlation analyses between peripheral immune cell percentages and tumor characteristics. Third, this was a single-center study, which could introduce selection bias; thus, multicenter validation studies are needed to confirm our observations. Forth, the study focused on clinical immune profiling through flow cytometry and did not include mechanistic or functional experiments, such as cytokine assays or single-cell analysis, to elucidate the roles of B and NK cell subsets in tumor progression or therapy response. Finally, as an observational study, we cannot establish causality between changes in immune cell populations and treatment outcomes. Future prospective and mechanistic studies are warranted to build upon our initial findings.

In conclusion, this study suggests that dynamic changes in peripheral B and NK cell percentages may be associated with treatment response in breast cancer patients. These findings are exploratory and hypothesis-generating, and further validation in prospective, well-characterized cohorts is required before potential clinical application. In particular, future studies incorporating longitudinal immune monitoring and long-term clinical outcomes, such as relapse-free survival and overall survival, will be important to clarify the clinical relevance of these observations.

Abbreviations

NK cells

Natural killer cells

TNBC

Triple negative breast cancer

ADCC

Antibody dependent cellular cytotoxicity

ER

Estrogen receptor

PR

Progesterone receptor

Her2

Human epidermal growth factor receptor s

TLS

Tertiary lymphoid structure

Author contributions

Hao Jin, Chunfang Hao, Zhongsheng Tong designed the work. Hao Wang, Wei Du, and Hao Jin supervised the work. Zichen Xu and Bochang Wang provided resources, and materials. Wei Du, Jifeng Sun, and Hao Jin collected and processed the data. Hao Wang, Zhao Yan, Jingyi Li and Hao Jin conducted the analysis and interpretation. Hao Wang, Wei Du, Hairong Wang, and Hao Jin performed the literature search. Hao Jin, and Zhongsheng Tong critically reviewed the manuscript. All authors reviewed and approved the final manuscript.

Funding

This work was supported by grants from Tianjin Binhai New Area Health Research Project (No. 2024BWKZ09).

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy concerns, but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was also approved by the Ethics Committee of Tianjin Cancer Hospital Airport Hospital in September 2024. The approval number is No. 2024BWKZ09. All patients provided written informed consent upon hospital admission, acknowledging that their test data could be used for clinical research conducted at our institution.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.