Abstract
Purpose
The purpose of this study was to analyze the expression patterns of immune cells in renal cancer patients, including myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), CD3 + /CD4 + T cells, CD3 + / CD8 + T cells, and CD3- CD16 + CD56 + cells. In addition, this study will explore the correlation between these immune markers and the progression of renal cell carcinoma and evaluate their potential application in predicting the therapeutic effect of renal cell carcinoma.
Methods
In this study, 80 renal cancer patients who received treatment in our hospital from October 2022 to December 2023 were selected as the research object and 50 healthy people who underwent a physical examination at the same time were selected as the control group. All participants had a 3 ml venous blood sample taken in the morning on an empty stomach. All patients with renal cell carcinoma have been confirmed by histopathological diagnosis. Clinicopathological data including age, gender, BMI, clinical stage, tumor size and pathological type were collected.MDSC, Treg, CD3 + /CD4 + T cells, CD3 + /CD8 + T cells, the ratio of CD3 + /CD4 + T cells/CD3 + /CD8 + T cell and the expression level of CD3-CD16 + CD56 + cells were detected by flow cytometry.
Results
Through the detection of flow cytometry, we observed that there was no significant difference in gender, age, BMI and other baseline characteristics between renal cancer patients and healthy controls, and the P value was greater than 0.05. However, in the analysis of peripheral blood immune cell subsets, including CD3 + /CD4 + , CD3 + /CD8 + , CD3 + /CD4 + /CD3 + /CD8 + ratio, NK cells, regulatory T cells (T-reg), polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC) and mononuclear myeloid-derived suppressor cells (M-MDSC) were significantly different between renal cell carcinoma group and normal control group (P < 0.05). Specifically, the expression levels of CD3 + /CD4 + and CD3 + /CD8 + cells in renal cancer patients were lower than those in normal subjects, while the expression levels of T-reg, PMN-MDSC and M-MDSC were relatively high. (2) In the flow cytometry analysis, the expression level of immune cell subsets in the peripheral blood of renal cancer patients was detected.The results showed that there was no significant correlation between the expression of CD3 + /CD4 + , CD3 + /CD8 + , CD3 + /CD4 + /CD3 + /CD8 + ratio, NK cells, T-reg cells, PMN-MDSC and M-MDSC and the sex, age, BMI and pathological type of the patients. These differences were not statistically significant (P > 0.05).At the same time, CD3 + /CD8 + T cells, the ratio of CD3 + /CD4 + /CD3 + /CD8 + and the expression level of NK cells were not significantly correlated with tumor size and clinical stage (P > 0.05). However, the expression levels of CD3 + /CD4 + cells, M-MDSC, PMN-MDSC, and T-reg cells were statistically significantly different with tumor size and clinical stage (P < 0.05).There was a significant difference between these indexes and lymph node metastasis (P < 0.05). (3) The results of Logistic regression analysis showed that the low expression of CD3 + /CD4 + lymphocytes and the high expression of T-reg, PMN-MDSC and M-MDSC in peripheral blood may be related to the clinical stage of renal cell carcinoma.
Conclusion
(1) Compared with healthy individuals, patients with renal cell carcinoma showed a significant decrease in CD3 + /CD4 + T cells, CD3 + /CD8 + T cells and CD3-CD16 + CD56 + cells, while the CD4 + /CD8 + ratio increased. In addition, the number of PMN-MDSC, M-MDSC and T-reg cells was significantly increased compared with the normal population, indicating that the immune system function of patients was impaired. (2) The expression levels of CD3 + /CD4 + , PMN-MDSC, M-MDSC and T-reg were different in tumor size and clinical stage. Specifically, the expression levels of PMN-MDSC, M-MDSC, and T-reg increased correspondingly with the increase in tumor diameter and the progression of the clinical stage.
Keywords: Renal cell carcinoma, Myeloid-derived suppressor cell, Lymphocyte
Introduction
Renal cell carcinoma, arising from the urothelial system of the renal parenchyma, is a malignant tumor. Globally, its incidence ranks 14th among all cancers. Among them, renal cell carcinoma (RCC) accounts for 80–85% of all cases of renal cancer, becoming one of the most common genitourinary malignancies, ranking third [1]. This disease mainly affects the age group of 60 to 70 years old, and most of the patients are male. Geographically, developed regions such as North America and Europe have higher incidence rates, while Asia and Africa have relatively low incidence rates [2, 3].
Studies have shown that the accumulation of bone marrow-derived suppressor cells (MDSC) in the tumor microenvironment has a significant correlation with the development and adverse prognosis of the disease [4, 5]. MDSC is mainly divided into polymorphonuclear MDSC (PMN-MDSC), monocytic MDSC (M-MDSC) and immature MDSC (e-MDSC). They all have immunosuppressive functions, such as inhibiting the activation of T cells, preventing the maturation of dendritic cells, inducing the loss of function of natural killer cells, and promoting the proliferation of regulatory T cells (T-reg) in a variety of malignant tumors [5, 6]. In addition, MDSCs in the tumor microenvironment can also differentiate into endothelial cells, fibroblasts and tumor-associated macrophages (TAMs) under the influence of chemokines released by tumor cells. And osteoclasts, further exacerbating their role in the tumor microenvironment [7, 8].
Numerous studies have confirmed that immune status is significantly associated with the prognosis and clinical outcome of malignant tumors. Historically, studies have focused on analyzing the association between counts of various types of cells in peripheral blood and cancer [8, 9]. However, the levels of lymphocyte subsets, regulatory T cells (T-reg), and myeloid-derived suppressor cells (MDSCs) in the peripheral blood more accurately reflect the adaptive immune status of cancer patients. The activity of the immune system has an important impact on clinical outcomes, so it is important to identify patients before and after surgery and to monitor the state of response to immunotherapy, chemotherapy, and radiotherapy [10, 11]. Studies have shown that the development of tumors is closely related to the immune function of the body. Cellular immunity plays a central role in the body's immune response against cancer [11].
Although significant progress has been made in the surgical treatment of renal cell carcinoma, how to eradicate residual lesions and prevent recurrence and metastasis is still one of the current challenges. Therefore, it has become a new focus to explore a new adjuvant therapy for renal cell carcinoma after surgery [12]. With the rapid development of immunology and genetic engineering technology, immunotherapy is considered a potential therapeutic strategy to improve the survival rate of renal cell carcinoma patients, which makes the study of renal cell carcinoma immunity attract the wide attention of the scientific community [13]. Studies have confirmed that there are abnormal changes in immune function indicators such as T cells, natural killer (NK) cells, regulatory T cells and bone marrow-derived suppressor cells in patients with malignant tumors. MDSC is of great value in predicting the therapeutic effect of multiple solid tumors [14, 15]. However, there are relatively few clinical studies on MDSC and T-reg in renal cancer patients. The purpose of this study is to compare the changes of peripheral blood lymphocyte subsets, T-reg and MDSC in patients with renal cell carcinoma and healthy people, and analyze their clinical significance combined with clinical data, to analyze their role in the occurrence and development of renal cell carcinoma, and provide new thinking direction for future immunotherapy of renal cell carcinoma (see Fig. 1).
Fig. 1.
expression levels of lymphocyte subsets, T-reg and MDSC in whole peripheral blood of normal and renal carcinoma patients
Materials and methods
Object of study
In this study, 80 renal cancer patients treated in our hospital from October 2022 to December 2023 were selected as the subjects, and 50 healthy individuals who underwent physical examination during the same period were selected as the control group. Clinicopathological data including age, gender, body mass index (BMI), clinical stage, tumor size and pathological type were collected.
Inclusion criteria
(1) The diagnosis was in line with the 2017 edition of the diagnosis and treatment criteria for renal cell carcinoma, and the gross tumor was visible; (2) All patients were confirmed by histopathology; (3) No preoperative chemotherapy, radiotherapy and other treatment; (4) Patients with a history of autoimmune diseases were excluded. The study was approved by the ethics committee of our hospital, and all participants signed a written informed consent form.
Exclusion criteria
(1) Patients with acute or chronic inflammation and acute abdomen. (2) Patients with chemotherapy or immunosuppressants. (3) Patients with incomplete medical records. (4) Patients who were lost to follow-up. (5) Patients who refused to participate in the study.
Experimental method
Peripheral blood samples were collected
For each enrolled patient and healthy physical examinee, 3 ml of peripheral venous whole blood was drawn in the morning under fasting conditions.
MDSC detection of peripheral blood
(1) Add 7 μl of each antibody in the test tube successively, including CD45-KO, CD3-FITC, CD19-FITC, CD20-FITC, CD56-PC5.5, CD16-PC7, HLA-DR-ECD, CD33-PE, CD11b-APC750, CD14-APC, and CD15-PB. Then 100 μl anticoagulant whole blood was added, mixed by shock, and reacted at room temperature for 15 min away from light. (2) 800 μl of flow cytometry hemolysis agent (ELS) was added into the test tube and the reaction was carried out at room temperature for 13 min without light. Then add 1000 μl PBS to the test tube, shake and mix well, centrifuge at 1000 rpm for 5 min, and discard the supernatant. (3) Add 1000 μl PBS again, shake and mix well, centrifuge at 1000 rpm for 5 min, and discard the supernatant. (4) At last, 500 μl PBS was added, oscillated and mixed, and filtered for subsequent flow cytometry.
T-reg detection of peripheral blood
(1) The following antibodies were added to the test tube respectively: CD45-KO, CD4-FITC, CD25-PC5.5, CD3-PC7, CD88-PE and CD127-APC. Then 50 μl whole blood was added and mixed with oscillations. After the mixture was fully mixed, the blood was incubated at room temperature for 15 min away from light. (2) Then 300 μl flow cytometry special hemolysis agent (ELS) was added, oscillated to achieve uniform mixing, and incubated at room temperature for 13 min without light. After that, 1000 μl PBS was added, the mixture was oscillated again, and centrifuged at 1000 rpm for 5 min, and then the supernatant was discarded. (3) Repeat the cleaning process, that is, add 1000 μl PBS again, oscillate and mix, centrifuge under the same conditions, and discard the supernatant. (4) Finally, 500 μl PBS was added, and the cells were oscillated to ensure suspension. After treatment by the filter device, flow cytometry analysis could be performed.
The detection of peripheral blood cd3 + cd4 + T cells and cd3 + cd8 + T cells
(1) 3 ml of peripheral blood was drawn from each enrolled patient and healthy control group in the morning on an empty stomach. (2) Prepare two test tubes, labeled number 1 and number 2 respectively. 2 µl CD45-FITC, CD4-RD, CD8-ECD, and CD3-PC5 were added to tube 1, while 2 µl CD45-FITC, CD56-RD1, CD19-ECD, CD3-PC5 were added to tube 2. (3) 20 µl of anticoagulant whole blood was added to test tubes No. 1 and No. 2, and after mixing by shaking, it was reacted at room temperature without light for 15 min. (4) After that, 150 µl flow cytometry hemolytic agents (ELS) were added to test tubes No. 1 and No. 2. After shaking and mixing, the reaction was carried out at room temperature without light for 13 min until the liquid in the test tube became transparent. (5) Finally, 200 µl of PBS were added to test tubes No. 1 and No. 2, and after oscillating and mixing, 20 microspheres were added, and then the test was carried out on the machine.
Flow cytometry analysis
Specifically, blood samples were collected in EDTA-coated tubes and processed within 2 h of collection. Samples were stained with fluorescently labeled antibodies against specific markers (e.g., CD3, CD4, CD8, CD11b, CD14, HLA-DR) at room temperature (20–25 °C) for 30 min in the dark, followed by washing twice with PBS containing 2% fetal bovine serum. For intracellular staining, samples were fixed and permeabilized using a commercial kit (BD Cytofix/Cytoperm Kit) and incubated with antibodies targeting intracellular markers at 4 °C for 30 min in the dark. Data were acquired using a flow cytometer (BD FACSCanto II) at a flow rate of 200–500 events per second, with compensation controls included. Data analysis was performed using FlowJo software with standardized gating strategies. Unstained samples were stored at 4 °C in the dark until processing, and stained samples were stored at 4 °C in the dark until analysis. Centrifugation was performed at 300–500 × g for 5–10 min at 4 °C.
Statistical analysis
In this study, SPSS 26.0 software was used to process and analyze relevant data. Measurement data inconsistent with normal distribution were expressed as median and quartile distance, and measurement data consistent with normal distribution were expressed as mean ± standard deviation (x ± s). Independent sample t-tests were used for comparisons between two groups, and paired t-tests were used for comparisons within groups. Categorical data were expressed as rates or composition ratios, and the chi-square test or Fisher's exact test was used for comparisons between two groups. Rank data were analyzed using the Wilcoxon rank sum test. P < 0.05 was considered statistically significant.
Results
Comparison of expression levels of lymphocyte subsets, T-reg and MDSC in whole peripheral blood of normal and renal carcinoma patients
In the comparative analysis of renal cancer patients and healthy controls, it was observed that there was no significant difference in gender, age, BMI and other basic characteristics between the two groups (P > 0.05). However, there were significant differences between the two groups in the levels of immune cell subsets in peripheral blood, including CD3 + /CD4 + , CD3 + /CD8 + , CD3 + /CD4 + /CD3 + /CD8 + ratio, NK cells, and T-reg cells (P < 0.05). Specifically, the levels of peripheral blood CD3 + / CD4 + , CD3 + / CD8 + , CD3 + /CD4 + /CD3 + /CD8 + ratio, NK cells, and T-reg cells were (42.8 ± 6.7)%, (28.2 ± 7.8)%, 1.6 ± 0.6, 10.9 ± 6.3, and 4.09 ± 1.2, respectively. In contrast, the corresponding level in the healthy control group was (49.1 ± 7.2)%、 (31.4 ± 3.9) %、2.0 ± 0.3、16.2 ± 2.1、2.01 ± 1.3. In addition, patients in the renal cancer group showed a significant increase in the total number of MDSCs and PMN-MDSCs compared to the healthy control group. Further analysis of MDSC subsets showed that the mean number of M-MDSC was 1.2 ± 1.1 in patients with renal cell carcinoma, but only 0.2 ± 0.1 in the control group, indicating that there was a significant difference in M-MDSC levels between the two groups (P < 0.05). Similarly, the mean PMN-MDSC was 0.4 ± 0.2 in patients with renal cell carcinoma and 0.1 ± 0.1 in controls, and the difference in PMN-MDSC levels was also statistically significant (P < 0.05), as shown in Table 1.
Table 1.
Comparison of expression levels of lymphocyte subsets, T-reg and MDSC in whole peripheral blood of normal and renal carcinoma patients
| Clinical index | Renal cancer group | Normal group | t/χ2 value | P value |
|---|---|---|---|---|
| Cases | n = 80 | n = 50 | ||
| Gender(n, %) | 0.425 | 0.581 | ||
| Male | 52(65.00) | 34(68.00) | ||
| Female | 28(35.00) | 16(32.00) | ||
| Age(year) | 0.684 | 0.328 | ||
| ≥ 60 | 45(56.25) | 26(52.00) | ||
| < 60 | 35(43.75) | 24(48.00) | ||
| BMI(kg/m2) | 0.856 | 0.159 | ||
| 18–24 | 28(35.00) | 15(30.00) | ||
| 24 ~ 28 | 34(42.50) | 22(44.00) | ||
| > 28 | 18(22.50) | 13(26.00) | ||
| Lymphocyte subsets | ||||
| CD3 + /CD4 + | 42.8 ± 6.7 | 49.1 ± 7.2 | 3.756 | 0.018 |
| CD3 + /CD8 + | 28.2 ± 7.8 | 31.4 ± 3.9 | 2.643 | 0.041 |
| CD4 + /CD8 + | 1.6 ± 0.6 | 2.0 ± 0.3 | 2.824 | 0.037 |
| NK | 10.9 ± 6.3 | 16.2 ± 2.1 | 4.921 | 0.005 |
| T-reg | 4.09 ± 1.2 | 2.01 ± 1.3 | 5.312 | 0.002 |
| M-MDSC | 1.2 ± 1.1 | 0.2 ± 0.1 | 4.875 | 0.006 |
| PMN-MDSC | 0.4 ± 0.2 | 0.1 ± 0.1 | 4.186 | 0.012 |
Expression levels of lymphocyte subsets, T-reg and MDSC in different clinical feature groups of patients with renal carcinoma
Patients with renal cell carcinoma were divided into groups according to their gender, age, BMI, tumor size, clinical stage, and pathological type. The results showed that the expression levels of CD3 + /CD4 + , CD3 + /CD8 + , CD3 + /CD4 + /CD3 + /CD8 + , NK cells, T-reg cells, PMN-MDSC and M-MDSC in peripheral blood of patients with renal cancer were not significantly correlated with gender, age, BMI and pathological types. The difference was not statistically significant (P > 0.05).In addition, CD3 + /CD8 + T cells, CD3 + /CD4 + /CD3 + /CD8 + ratio, and the number of NK cells was not significantly correlated with tumor size and clinical stage (P > 0.05). However, the levels of CD3 + /CD4 + cells, Treg cells, M-MDSC and PMN-MDSC were significantly correlated with tumor size and clinical stage (P < 0.05). See Tables 2, 3, 4, 5, 6, 7 for specific data.
Table 2.
Relationship between peripheral blood lymphocyte subsets, T-reg, MDSC and sex
| Clinical index | Gender | |||
|---|---|---|---|---|
| Male | Female | t value | P value | |
| Cases | n = 52 | n = 28 | ||
| CD3 + CD4 + (Mean ± SD) | 42.9 ± 8.5 | 40.3 ± 7.8 | 0.872 | 0.143 |
| CD3 + CD8 + (Mean ± SD) | 27.8 ± 8.2 | 28.6 ± 8.5 | 0.758 | 0.259 |
| CD4 + /CD8 + (Mean ± SD) | 1.8 ± 0.9 | 1.7 ± 0.8 | 0.356 | 0.661 |
| NK(Mean ± SD) | 9.8 ± 6.7 | 11.4 ± 7.1 | 0.369 | 0.648 |
| T-reg(Mean ± SD) | 4.18 ± 1.26 | 3.97 ± 1.05 | 0.834 | 0.169 |
| M-MDSC(Mean ± SD) | 1.1 ± 1.3 | 1.0 ± 1.1 | 0.598 | 0.406 |
| PMN-MDSC(Mean ± SD) | 0.3 ± 0.2 | 0.3 ± 0.3 | 0.271 | 0.731 |
Table 3.
Relationship between peripheral blood lymphocyte subsets, T-reg, MDSC and age
| Clinical index | Age(year) | |||
|---|---|---|---|---|
| < 60 | ≥ 60 | t value | P value | |
| Cases | n = 45 | n = 35 | ||
| CD3 + CD4 + (Mean ± SD) | 44.5 ± 8.6 | 42.8 ± 7.9 | 0.864 | 0.148 |
| CD3 + CD8 + (Mean ± SD) | 28.1 ± 8.2 | 29.7 ± 8.9 | 0.738 | 0.275 |
| CD4 + /CD8 + (Mean ± SD) | 1.7 ± 0.8 | 1.6 ± 0.9 | 0.638 | 0.376 |
| NK(Mean ± SD) | 11.4 ± 7.5 | 10.1 ± 6.2 | 0.436 | 0.578 |
| T-reg(Mean ± SD) | 4.3 ± 1.3 | 4.2 ± 1.5 | 0.546 | 0.461 |
| M-MDSC(Mean ± SD) | 1.0 ± 1.2 | 1.1 ± 0.9 | 0.246 | 0.768 |
| PMN-MDSC(Mean ± SD) | 0.3 ± 0.2 | 0.2 ± 0.4 | 0.183 | 0.826 |
Table 4.
Relationship between peripheral blood lymphocyte subsets, T-reg, MDSC and BMI
| Clinical index | BMI(kg/m2) | ||||
|---|---|---|---|---|---|
| 18–24 | 24 ~ 28 | > 28 | t value | P value | |
| Cases | n = 28 | n = 34 | n = 18 | ||
| CD3 + CD4 + (Mean ± SD) | 41.1 ± 6.3 | 42.5 ± 7.1 | 43.9 ± 8.5 | 0.643 | 0.368 |
| CD3 + CD8 + (Mean ± SD) | 29.3 ± 7.9 | 27.8 ± 6.4 | 26.6 ± 6.1 | 0.586 | 0.426 |
| CD4 + /CD8 + (Mean ± SD) | 1.6 ± 0.6 | 1.7 ± 0.9 | 1.8 ± 0.8 | 0.745 | 0.268 |
| NK(Mean ± SD) | 11.8 ± 7.4 | 11.1 ± 6.7 | 10.9 ± 6.2 | 0.825 | 0.186 |
| T-reg(Mean ± SD) | 4.3 ± 1.4 | 3.8 ± 1.2 | 4.5 ± 1.5 | 0.382 | 0.627 |
| M-MDSC(Mean ± SD) | 1.1 ± 1.2 | 0.9 ± 1.1 | 1.3 ± 1.2 | 0.748 | 0.263 |
| PMN-MDSC(Mean ± SD) | 0.3 ± 0.4 | 0.2 ± 0.3 | 0.4 ± 0.5 | 0.871 | 0.138 |
Table 5.
Relationship between peripheral blood lymphocyte subsets, T-reg, MDSC and tumor size
| Clinical index | Tumor size(cm) | |||
|---|---|---|---|---|
| ≤ 7 | > 7 | t value | P value | |
| Cases | n = 27 | n = 53 | ||
| CD3 + CD4 + (Mean ± SD) | 46.1 ± 9.8 | 37.1 ± 6.8 | 3.425 | 0.021 |
| CD3 + CD8 + (Mean ± SD) | 27.6 ± 7.3 | 28.9 ± 8.2 | 0.462 | 0.547 |
| CD4 + /CD8 + (Mean ± SD) | 1.7 ± 0.9 | 1.5 ± 0.7 | 0.856 | 0.158 |
| NK(Mean ± SD) | 11.3 ± 7.5 | 11.1 ± 6.8 | 0.136 | 0.874 |
| T-reg(Mean ± SD) | 4.2 ± 1.3 | 4.4 ± 1.5 | 0.278 | 0.734 |
| M-MDSC(Mean ± SD) | 0.7 ± 1.2 | 1.7 ± 1.3 | 4.278 | 0.01 |
| PMN-MDSC(Mean ± SD) | 0.2 ± 0.3 | 0.4 ± 0.4 | 0.865 | 0.148 |
Table 6.
Relationship between peripheral blood lymphocyte subsets, T-reg, MDSC and clinical stage
| Clinical index | Clinical stage | |||||
|---|---|---|---|---|---|---|
| stage I | Stage II | Stage III | Stage IV | t value | P value | |
| Cases | n = 24 | n = 25 | n = 18 | n = 13 | ||
| CD3 + CD4 + (Mean ± SD) | 44.6 ± 8.3 | 41.4 ± 9.1 | 39.5 ± 7.6 | 38.1 ± 6.9 | 0.873 | 0.136 |
| CD3 + CD8 + (Mean ± SD) | 27.8 ± 6.4 | 29.6 ± 5.8 | 30.8 ± 8.7 | 32.4 ± 9.3 | 0.673 | 0.338 |
| CD4 + /CD8 + (Mean ± SD) | 1.9 ± 0.9 | 1.7 ± 0.7 | 1.6 ± 0.5 | 1.4 ± 0.6 | 0.763 | 0.248 |
| NK(Mean ± SD) | 11.2 ± 7.4 | 13.5 ± 9.3 | 9.1 ± 7.7 | 10.4 ± 5.8 | 0.582 | 0.427 |
| T-reg(Mean ± SD) | 3.8 ± 1.2 | 4.2 ± 0.9 | 3.3 ± 0.8 | 5.6 ± 1.8 | 4.765 | 0.007 |
| M-MDSC(Mean ± SD) | 0.6 ± 1.1 | 1.4 ± 1.3 | 0.9 ± 1.0 | 1.9 ± 1.5 | 4.026 | 0.014 |
| PMN-MDSC(Mean ± SD) | 0.1 ± 0.1 | 0.1 ± 0.2 | 0.5 ± 0.3 | 0.6 ± 0.4 | 3.865 | 0.016 |
Table 7.
Relationship between peripheral blood lymphocyte subsets, T-reg, MDSC and pathological type
| Clinical index | Pathological type | ||||
|---|---|---|---|---|---|
| Renal clear cell carcinoma | Renal papillary cell carcinoma | Others | t value | P value | |
| Cases | n = 53 | n = 18 | n = 9 | ||
| CD3 + CD4 + (Mean ± SD) | 41.8 ± 9.6 | 42.5 ± 8.9 | 44.9 ± 10.4 | 0.784 | 0.226 |
| CD3 + CD8 + (Mean ± SD) | 28.5 ± 7.8 | 29.6 ± 8.3 | 25.8 ± 9.8 | 0.873 | 0.138 |
| CD4 + /CD8 + (Mean ± SD) | 1.7 ± 0.9 | 1.6 ± 0.7 | 2.0 ± 0.8 | 0.435 | 0.577 |
| NK(Mean ± SD) | 11.4 ± 8.1 | 11.1 ± 6.8 | 10.8 ± 7.2 | 0.764 | 0.248 |
| T-reg(Mean ± SD) | 4.3 ± 1.5 | 3.4 ± 0.8 | 4.6 ± 1.6 | 0.728 | 0.285 |
| M-MDSC(Mean ± SD) | 0.8 ± 0.9 | 2.1 ± 1.6 | 0.7 ± 1.2 | 0.916 | 0.093 |
| PMN-MDSC(Mean ± SD) | 0.4 ± 0.3 | 0.5 ± 0.6 | 0.3 ± 0.4 | 0.832 | 0.175 |
Correlation between peripheral blood lymphocyte subsets, T-reg, MDSC and clinical stage in patients with renal carcinoma
In the study of stage prediction of renal cancer patients, we selected multiple immune cell markers in peripheral blood as covariates, including CD3 + /CD4 + , CD3 + /CD8 + , CD3 + /CD4 + /CD3 + /CD8 + , T-reg, PMN-MDSC and M-MDSC. At the same time, the stage of patients with renal cell carcinoma was taken as the dependent variable, in which 0 represents stage I/II and 1 represents stage III/IV. After Logistic regression analysis, we found that the low expression of CD3 + /CD4 + lymphocytes in peripheral blood and the high expression of T-reg, PMN-MDSC and M-MDSC were associated with the clinical stage of renal cancer patients. See Table 8.
Table 8.
Correlation between peripheral blood lymphocyte subsets, T-reg, MDSC and clinical stage in patients with renal carcinoma
| Clinical index | OR value | 95%CI lower | 95%CI upper | P value |
|---|---|---|---|---|
| CD3 + CD4 + | 0.874 | 0.723 | 1.025 | 0.018 |
| CD3 + CD8 + | 1.125 | 0.976 | 1.241 | 0.128 |
| CD4 + /CD8 + | 0.417 | 0.106 | 1.312 | 0.098 |
| NK | 0.896 | 0.795 | 1.023 | 0.184 |
| T-reg | 1.82 | 1.137 | 3.245 | 0.027 |
| M-MDSC | 1.946 | 1.162 | 3.985 | 0.021 |
| PMN-MDSC | 3.356 | 2.965 | 6.783 | 0.016 |
Discussion
Relationship of peripheral blood cd3 + cd4 + , cd3 + cd8 + , cd3 + cd4 + / cd3 + cd8 + , NK, T-reg, M-MDSC, PMN-MDSC between patients with renal cancer and healthy controls
This study revealed a statistically significant decrease in the number of CD3 + /CD4 + T lymphocytes and CD3 + /CD8 + T cells in the peripheral blood of renal cancer patients compared with healthy individuals before surgery. This finding shows that the immune system function of renal cancer patients is inhibited under tumor burden, which is consistent with relevant domestic research reports [16, 17]. The possible mechanism is that renal cancer, as an immunogenic malignancy, can promote the generation of suppressor cell populations, including bone marrow-derived suppressor cells (MDSC) and regulatory T cells (T-reg). In the immunology study of renal cell carcinoma, it has been observed that tumor cells themselves can inhibit the immune response of the host to a certain extent: by constructing an immunosuppressive microenvironment, they can promote the growth and metastasis of tumor cells [17, 18]. In addition, tumor cells can also secrete a large number of immunosuppressive cytokines, resulting in a decrease in the number of CD3 + /CD4 + T cells and CD3 + /CD8 + T cells. With the development of tumor, the immune function of the body declines further, which provides favorable conditions for the development and metastasis of tumor [19, 20]. Flow cytometry analysis showed that the expression level of natural killer (NK) cells in the peripheral blood of renal cancer patients was significantly lower than that of healthy controls. NK cells are a component of the innate immune system, and the decrease in the proportion of NK cells in cancer patients reflects the weakening of cellular immune function. Therefore, through the in-depth analysis of peripheral blood lymphocytes in patients with renal cancer, we can evaluate the immune status of the body and its course of disease changes, which provides a monitoring method for the immune enhancement therapy of patients with malignant tumors, and is helpful to judge the prognosis of the disease [20, 21].
In this experiment, PMN-MDSC and M-MDSC cell counts in the peripheral blood of renal cancer patients were significantly higher than those of healthy controls. MDSCs have received increasing attention in clinical practice. Many studies have confirmed that increased MDSC levels are positively associated with poor prognosis and disease stage in patients with breast cancer, hepatocellular carcinoma, thyroid cancer, and non-small cell lung cancer (NSCLC) [22–24]. Studies have shown that in patients with metastatic renal cell carcinoma, sunitinib can significantly reduce the accumulation of MDSC in peripheral blood and reverse the inhibitory state of T cells, which provides theoretical support for sunitinib combined with immunotherapy in the treatment of renal tumors. These findings suggest that circulating MDSCs may be a potential biomarker of tumor development and guide the development of individualized and effective immunomodulatory therapies [22, 24, 25].
Relationship between peripheral blood cd3 + cd4 + , cd3 + cd8 + , cd3 + cd4 + /cd3 + cd8 + , NK, T-reg, M-MDSC, PMN-MDSC levels and clinical stage in patients with renal cancer
In this study, it was observed that there was a significant difference in the level of regulatory T cells (T-reg) in different clinical stages of renal cell carcinoma, which was higher in stage II than in stage I, and higher in stage IV than in stage III. This trend may indicate that the number of T-reg increases with a higher tumor stage. These increased T-reg can effectively inhibit the function of effector T cells produced by the host immune system in response to tumor-specific antigens, thus enabling tumors to evade the surveillance and attack of the immune system. This finding suggests that T-reg may be closely associated with the occurrence and progression of renal cell carcinoma. In addition, the study also revealed that the levels of PMN-MDSC and M-MDSC in peripheral blood mononuclear cells of patients with different stages of renal cancer changed with different stages of renal cancer, and the proportion of PMN-MDSC and M-MDSC in advanced patients was significantly higher than that in early patients. Several studies have shown that an increase in the number of MDSCs in human peripheral blood is associated with tumor size and its clinical stage. An analysis of 64 patients with colorectal cancer found that the proportion and absolute count of MDSC in peripheral blood increased significantly compared with healthy controls, and this increase was closely related to the clinical stage of cancer [26, 27]. Specifically, the proportion of circulating MDSC was significantly higher in patients with advanced (stage III/IV) tumors compared to early (stage I/II) tumors.Further analysis showed that the proportion of PMN-MDSC and M-MDSC in patients with pathological stage IV was significantly higher than that in patients with pathological stage III, suggesting that the change of MDSC was closely related to tumor progression. With the increase of the malignant degree of tumor, the proportion of the MDSC subpopulation also increased.These data suggest that the frequency of MDSC can reflect the tumor burden of cancer patients, and the expression of MDSC in patients with high clinical stage is significantly higher than that in patients with low clinical stage, which has a certain auxiliary value for disease assessment and clinical staging [24, 25, 27].
The levels of bone marrow-derived suppressor cells (MDSC) and regulatory T cells (T-reg) in peripheral blood were significantly increased, while the combined count of CD4 + and CD8 + T cells and the ratio of CD4 + /CD8 + showed a downward trend. These changes together lead to the weakening of the body’s anti-tumor immune response, which provides favorable conditions for the further progress and metastasis of tumors [28, 29]. Based on the above results, it can be concluded that the increased levels of MDSC and T-reg cells, as well as the decreased levels of CD3 + T cells and CD4 + /CD8 + ratio in peripheral blood are closely related to the invasiveness and metastatic potential of renal cell carcinoma. Therefore, these indicators may be used as early warning biomarkers for the prediction of advanced renal cell carcinoma [30].
The differences in immune cell subsets, particularly lymphocyte subpopulations and myeloid-derived suppressor cells (MDSCs), have significant implications for patient outcomes and treatment strategies in renal cell carcinoma (RCC) [31]. Lower levels of CD3 + /CD4 + T cells, which are critical for orchestrating the immune response by activating other immune cells, suggest a compromised immune response in RCC patients, potentially leading to poorer prognosis and reduced efficacy of immunotherapies [32]. Similarly, decreased levels of CD3 + /CD8 + T cells, which are cytotoxic T cells responsible for directly killing tumor cells, indicate a weakened antitumor immune response [33]. Reduced CD4 + /CD8 + ratios further suggest an imbalance in T-cell populations, which may contribute to immune suppression and tumor progression [34]. Additionally, higher levels of regulatory T cells (T-reg) and MDSCs, both of which are known to suppress immune responses, indicate increased immune suppression in RCC patients. These findings highlight the need for targeted therapies that enhance immune function, such as checkpoint inhibitors or adoptive cell transfer, to improve patient outcomes and treatment efficacy.
In our study, we focused on detecting MDSCs (myeloid-derived suppressor cells), T-reg cells (regulatory T cells), and T cells (CD3 + CD4 + and CD3 + CD8 +) in peripheral blood samples. These specific markers were chosen based on their established roles in immune regulation and their relevance to the tumor microenvironment in renal carcinoma. MDSCs contribute to immune suppression and promote tumor growth, while T-reg cells inhibit the function of effector T cells, promoting tumor progression. CD4 + and CD8 + T cells are essential components of the adaptive immune response, with CD4 + T cells assisting in immune cell activation and CD8 + T cells directly targeting tumor cells. By quantifying these markers, we aimed to evaluate the overall immune response and identify potential biomarkers for therapeutic intervention.
While our study acknowledges the limitations, such as the cross-sectional design, which precludes establishing causality, future research could consider longitudinal studies to better understand the temporal dynamics of immune cell populations in renal cancer patients. Additionally, exploring the mechanisms behind the observed changes in immune cell populations and their impact on the tumor microenvironment could provide insights into potential therapeutic targets. additionally, the sample size was relatively small, which may limit the generalizability of our findings. Secondly, the data were collected from a single center, which may introduce selection bias. Future research should include larger samples and data from multiple centers to enhance the validity and generalizability of the results.
In summary, the selection of these markers—MDSCs, T-reg cells, and T cells (CD3 + CD4 + and CD3 + CD8 +)—was driven by their established roles in immune regulation and their direct relevance to the pathophysiology of renal carcinoma, providing insights into the complex interplay between immune cells and tumor progression, and offering potential targets for immunotherapy.CD3 + /CD4 + CD3 + /CD8 + CD3 + /CD4 + CD3 + /CD8 +.
Conclusion
In renal cancer patients, the number of CD3 + /CD4 + T cells, CD3 + /CD8 + T cells, and CD3-CD16 + CD56 + cells was significantly reduced compared to healthy individuals, while the CD4 + /CD8 + ratio was increased. In addition, the numbers of PMN-MDSC, M-MDSC and Treg cells were significantly increased compared with those of normal people, indicating that the immune system function of patients was impaired. It was found that the expression levels of CD3 + /CD4 + , PMN-MDSC, M-MDSC and T-reg were related to the tumor size and clinical stage, that is, the larger the tumor diameter and the later the clinical stage, the higher the expression levels of these cells.
Acknowledgements
All research methods were conducted in accordance with relevant guidelines and regulations, and have been approved by Hangzhou Third People's Hospital.
Author contributions
Yan Li designed the study. Yan Li wrote the original draft. Zhiping Wu, Chen Ni collected raw data. Yueda Li, Ping Wang performed statistical and bioinformatics analyses. Yan Li supervised the study.
Funding
This study did not receive any funding in any form.
Data availability
The data could be obtained by contacting the corresponding author.
Declarations
Ethics approval and consent to participate
This study protocol was approved by the Ethics Committee of Hangzhou Third People's Hospital (NO.202210213HZ).
Consent for publication
Informed consent was obtained from all patients.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Guan X, Liu Z, Zhang J, et al. Myeloid-derived suppressor cell accumulation in renal cell carcinoma is correlated with CCL2, IL-17 and IL-18 expression in blood and tumors. Adv Clin Exp Med. 2018;27(7):947–53. [DOI] [PubMed] [Google Scholar]
- 2.Wu K, Zheng X, Yao Z, et al. Accumulation of CD45RO+CD8+ T cells is a diagnostic and prognostic biomarker for clear cell renal cell carcinoma. Aging (Albany NY). 2021;13(10):14304–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Hillebrand RM, Vogt A, Strassburg CP, et al. Immune check point CD40-CD40L activates dendritic and effector cells against human renal carcinoma cells. Anticancer Res. 2019;39(9):4643–52. [DOI] [PubMed] [Google Scholar]
- 4.Kato T, Tomiyama E, Koh Y, et al. A potential mechanism of anticancer immune response coincident with immune-related adverse events in patients with renal cell carcinoma. Anticancer Res. 2020;40(9):4875–83. [DOI] [PubMed] [Google Scholar]
- 5.Lu X, Lu X. Enhancing immune checkpoint blockade therapy of genitourinary malignancies by co-targeting PMN-MDSCs. Biochim Biophys Acta Rev Cancer. 2022;1877(3): 188702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Yuan H, Cai P, Li Q, et al. Axitinib augments antitumor activity in renal cell carcinoma via STAT3-dependent reversal of myeloid-derived suppressor cell accumulation. Biomed Pharmacother. 2014;68(6):751–6. [DOI] [PubMed] [Google Scholar]
- 7.Waidhauser J, Gantner AK, Schifano P, et al. Influence of cryoablation versus operation on circulating lymphocyte subsets in patients with early-stage renal cell carcinoma. BMC Cancer. 2024;24(1):825. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Li J, Zhang H, Wu J, et al. Granzymes expression patterns predict immunotherapy response and identify the heterogeneity of CD8+ T cell subsets. Cancer Biomark. 2023;38(1):77–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Juliá EP, Mandó P, Rizzo MM, et al. Peripheral changes in immune cell populations and soluble mediators after anti-PD-1 therapy in non-small cell lung cancer and renal cell carcinoma patients. Cancer Immunol Immunother. 2019;68(10):1585–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Zelba H, Bedke J, Hennenlotter J, et al. PD-1 and LAG-3 dominate checkpoint receptor-mediated t-cell inhibition in renal cell carcinoma. Cancer Immunol Res. 2019;7(11):1891–9. [DOI] [PubMed] [Google Scholar]
- 11.Ko JS, Rayman P, Ireland J, et al. Direct and differential suppression of myeloid-derived suppressor cell subsets by sunitinib is compartmentally constrained. Cancer Res. 2010;70(9):3526–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Bai D, Feng H, Yang J, et al. Landscape of immune cell infiltration in clear cell renal cell carcinoma to aid immunotherapy. Cancer Sci. 2021;112(6):2126–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Okada SL, Simmons RM, Franke-Welch S, et al. Conditioned media from the renal cell carcinoma cell line 786.O drives human blood monocytes to a monocytic myeloid-derived suppressor cell phenotype. Cell Immunol. 2018;323:49–58. [DOI] [PubMed] [Google Scholar]
- 14.Najjar YG, Rayman P, Jia X, et al. Myeloid-derived suppressor cell subset accumulation in renal cell carcinoma parenchyma is associated with intratumoral expression of IL1β, IL8, CXCL5, and Mip-1α. Clin Cancer Res. 2017;23(9):2346–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ko JS, Zea AH, Rini BI, et al. Sunitinib mediates reversal of myeloid-derived suppressor cell accumulation in renal cell carcinoma patients. Clin Cancer Res. 2009;15(6):2148–57. [DOI] [PubMed] [Google Scholar]
- 16.Orillion A, Hashimoto A, Damayanti N, et al. Entinostat neutralizes myeloid-derived suppressor cells and enhances the antitumor effect of PD-1 inhibition in murine models of lung and renal cell carcinoma. Clin Cancer Res. 2017;23(17):5187–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Rahma OE, Hodi FS. The intersection between tumor angiogenesis and immune suppression. Clin Cancer Res. 2019;25(18):5449–57. [DOI] [PubMed] [Google Scholar]
- 18.Vence L, Bucktrout SL, Fernandez CI, et al. Characterization and comparison of GITR expression in solid tumors. Clin Cancer Res. 2019;25(21):6501–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Hajiran A, Chakiryan N, Aydin AM, et al. Reconnaissance of tumor immune microenvironment spatial heterogeneity in metastatic renal cell carcinoma and correlation with immunotherapy response. Clin Exp Immunol. 2021;204(1):96–106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.De Biasi S, Guida A, Lo TD, et al. Redistribution of CD8+ T cell subsets in metastatic renal cell carcinoma patients treated with anti-PD-1 therapy. Cytometry A. 2022;101(7):597–605. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Yue Y, Cai X, Lu C, et al. Unraveling the prognostic significance and molecular characteristics of tumor-infiltrating B lymphocytes in clear cell renal cell carcinoma through a comprehensive bioinformatics analysis. Front Immunol. 2023;14:1238312. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ford JW, Gonzalez-Cotto M, MacFarlane AT, et al. Tumor-infiltrating myeloid cells co-express TREM1 and TREM2 and elevated TREM-1 associates with disease progression in renal cell carcinoma. Front Oncol. 2021;11: 662723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Peng Z, Hu Y, Ren J, et al. Circulating Th22 cells, as well as Th17 cells, are elevated in patients with renal cell carcinoma. Int J Med Sci. 2021;18(1):99–108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Fernández A, Mesa C, Marigo I, et al. Inhibition of tumor-induced myeloid-derived suppressor cell function by a nanoparticulated adjuvant. J Immunol. 2011;186(1):264–74. [DOI] [PubMed] [Google Scholar]
- 25.Chow J, Khan A, Gaudieri M, et al. Tumor and immune remodeling following radiotherapy in human renal cell carcinoma. J Immunother Cancer. 2023;11:4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ager CR, Zhang M, Chaimowitz M, et al. KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with tumor progression and immunotherapy response. J Immunother Cancer. 2023;11:9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Lechner MG, Megiel C, Russell SM, et al. Functional characterization of human Cd33+ and Cd11b+ myeloid-derived suppressor cell subsets induced from peripheral blood mononuclear cells co-cultured with a diverse set of human tumor cell lines. J Transl Med. 2011;9:90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Wang Q, Hu J, Kang W, et al. Tumor microenvironment immune subtypes for classification of novel clear cell renal cell carcinoma profiles with prognostic and therapeutic implications. Medicine (Baltimore). 2021;100(11): e24949. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Tsimafeyeu I, Volkova M, Olshanskaia A, et al. Expression of receptor tyrosine kinases on peripheral blood mononuclear cells and tumor-infiltrating lymphocytes in patients with renal cell carcinoma and healthy donors. Oncology. 2020;98(4):252–8. [DOI] [PubMed] [Google Scholar]
- 30.Zhou Y, Jiang D, Chu X, et al. Prognostic value of peripheral blood T lymphocyte subsets in clear cell renal cell carcinoma. Transl Androl Urol. 2021;10(1):326–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Díaz-Montero CM, Rini BI, Finke JH. The immunology of renal cell carcinoma. Nat Rev Nephrol. 2020;16(12):721–35. [DOI] [PubMed] [Google Scholar]
- 32.Lyu C, Stadlbauer B, Wang L, Buchner A, Pohla H. Identification of a novel combination treatment strategy in clear cell renal cell carcinoma stem cells with shikonin and ipilimumab. Front Immunol. 2023;9(14):1186388. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ivanova OK, Sharapova TN, Romanova EA, Soshnikova NV, Sashchenko LP, Yashin DV. CD3+ CD8+ NKG2D+ T lymphocytes induce apoptosis and necroptosis in HLA-negative cells via FasL-Fas interaction. J Cell Biochem. 2017;118(10):3359–66. [DOI] [PubMed] [Google Scholar]
- 34.Todo T, Ito H, Ino Y, Ohtsu H, Ota Y, Shibahara J, Tanaka M. Intratumoral oncolytic herpes virus G47∆ for residual or recurrent glioblastoma: a phase 2 trial. Nat Med. 2022;28(8):1630–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
The data could be obtained by contacting the corresponding author.