Introduction
Glioma accounts for 80% of primary malignant brain tumors in adults.1 Patients with low-grade glioma (WHO Grade I and II) have a median survival time of 11.6 years,2 while high-grade glioma (WHO Grade III and IV) exhibit poorer prognosis.3 Histopathological examination is the standard of practice for definitive diagnosis of glioma.4 However, these procedures are invasive with a risk of hemorrhage or neurological damage, and are biased in sampling or interpreting. The conventional way can further bring underestimation of significant intratumor heterogeneity in biopsies, introducing bias in both retrospective and prospective studies.5 What’s more, the invasive histopathological samples unable to offer immune-microenvironment data in patient-derived organoids (PDO) or xenografts (PDX) models, which limits their clinical relevance.6 The effective serum biomarkers for clinical diagnosis, disease staging, treatment response monitoring, and prognosis for glioma were urgent but remain unexplored.7
The highly immunosuppressive microenvironment has been identified as the essential intracranial mechanism for glioma, especially for high-grade glioma.8–10 The intracranial microenvironment lacks effective anti-tumor immunity due to the infiltration of myeloid-derived suppressor cells (MDSC) and regulatory T cells (Treg), as well as the upregulation of immune checkpoint molecules, and secretion of immunosuppressive cytokines, including TGF-β and IL-10.11,12 However, whether the systemic immune status can be influenced by the specific glioma remains unknown. Emerging evidence suggests that glioma-derived extracellular vesicles (EVs) carrying miR-21 and miR-29a can modulate distant immune cell function to activate cancer invasion.13,14 Glioma-derived peripheral cytokines, such as IL-1β, IL-6, IL-8, and TNFα, are vital mediators contributing to chronic inflammation and cancer progression.15 Thus, both systemic and intracranial immune status can drive the malignancy of glioma. The hematological parameters are more accessible, cost-effective, and can be measured using standardized testing methods compared to tumor tissue. Previous studies have shown that several hematological parameters, such as the neutrophil-to-lymphocyte ratio, have diagnostic capability for glioma grades.16–18 However, the systemic immune-inflammatory indices should include the complements, and immunoglobin as well.19–21 Therefore, we investigated the prognostic and diagnostic value of the novel systemic immune-inflammation indices (nSII) in glioma.
In this multi-institutional cohort study, we included 1282 glioma patients from Northeast, Southeast, and Eastern regions of China and collected peripheral blood at their initial hospitalization, comprehensively evaluating their preoperative peripheral immune status. We reported the association of peripheral immune status with prognosis, tumor grade, and glioma subtype, potentially benefiting future glioma diagnostic and prognostic processes.
Materials and Methods
Study Cohort Identification
A total of 2342 patients were diagnosed with glioma from three hospitals (Sun Yat-sen University Cancer Center, Xuanwu Hospital Capital Medical University, and the First Bethune Hospital of Jilin University) between October 13, 2006, and April 6, 2022 were included. Inclusion criteria were as follows: 1) glioma grading and classification histologically verified according to 2016 WHO criteria [11]; 2) age ≥ 18 years; 3) no prior cancer-specific pretreatment, such as chemotherapy or radiotherapy; 4) no current infectious disease, hyperpyrexia, hematological disease, diabetes mellitus, serious heart disease, hypertension, metabolic syndrome, severe renal or hepatic dysfunction, other cancer, autoimmune disease, inflammatory disease, or medication usage related to an inflammatory condition; 5) primary malignant glioma; 6) the duration of follow-up >3 months; 7) available data of lymphocyte subsets, cytokines, immune proteins, and complements measured at the patient’s first hospitalization; 8) previous history of steroids application before the blood samples were collected; 9) previous history of acute or chronic inflammatory disease. In total, 112 patients were excluded due to young age. In total, 301 patients were excluded due to preoperative chemotherapy or radiotherapy. In total, 477 patients were excluded based on inclusion criteria. In total, 170 more patients with secondary glioma were excluded. Consequently, 1282 patients were enrolled. Informed consent from all patients was obtained before participating in this study.
Data Collection and Analysis of Immune and Inflammatory Factors in Peripheral Blood
Age, biological sex, survival state and follow-up time, data on peripheral blood immune and inflammatory factors, IDH1 mutation status, 1p/19q co-deletion status, MGMT methylation status, WHO histopathological subtype, and grade, and whether they received postoperative chemotherapy/radiotherapy were collected.
The detailed measurement and analysis processes of immune and inflammatory factors in peripheral blood are presented in the Supplementary Materials.
Follow-Up Evaluation and Clinical Outcome
The follow-up period was from October 13, 2006 to April 6, 2022. Patients were followed up at intervals of 1 to 3 months until death. The endpoint was all-cause mortality. Time zero was set at the time of resection of the primary tumor. The patient’s status and date of death were obtained from the electronic medical record and telephone calls to the patients or their relatives.
Statistical Analysis
The Kolmogorov–Smirnov test was used to test whether the data was a normal distribution. Mean ± standard, median (interquartile range, IQR), and counts (percentages) were used to present normally distributed, non-normally distributed, and categorical variables, respectively. After the log transformation of non-normally distributed data to normally distributed data, the association of immune and inflammatory factors with tumor grades and subtypes was evaluated using ordinal logistic regression and ANOVA. Independent-sample T-test was used to pinpoint the specific subtypes or grades for which the factors significantly differ. The Pearson correlation coefficient test judged the correlation between variables. A two-tailed P-value ≤ 0.05 was considered significant. Because batch-independent hypothesis tests were used for the above analysis, the false discovery rate (FDR) was calculated by the Benjamini–Hochberg procedure (BH), with an adjusted P-value ≤ 0.05 considered statistically significant. The diagnostic value of factors was evaluated using the area under the curve (AUC) obtained from the receiver operating characteristic curve (ROC).
The Cox-proportional Hazards Regression was used to study the relationship between the variables and mortality. Only when a variable reaches a P-value ≤ 0.1 in the univariate analysis will it be used in the multivariate analysis. Kaplan-Meier (KM) survival curves were plotted for variables significantly associated with prognosis (P-value ≤ 0.05). To construct a predictive model, an innovative nomogram specific to primary glioma was developed by the rms R package (v.6.2–0), based on the clinical characteristics, immune factors, and inflammatory factors. Consistency indexes (C-index, CI) were calculated by randomly performing 10-fold cross-validation 100 times to assess model prediction efficiency in each input setting. All statistical analyses were calculated using the IBM SPSS 26.0 program and R Studio 4.3.3 (Figure 1).
Results
Baseline Characteristics
A total of 1282 patients from 3 hospitals located in Northeast, Southeast, and Eastern China were included in Table 1. In total, 733 patients were male (733/1282, 57.18%). In total, 762 patients (762/1282, 59.3%) were diagnosed with grade IV glioma. Glioblastoma (GBM) was identified in 679 patients (679/1066, 63.70%), while anaplastic oligodendroglioma (AO) was observed in 52 patients (52/1066, 4.88%). The IDH1 mutation, 1p/19q codeletion, and MGMT promoter methylation were known for 1237, 1238, and 1238 patients, respectively. A total of 698 patients (698/1237, 56.43%) had IDH1 mutant, 285 patients (285/1238, 23.02%) had 1p/19q codeletion, and 595 patients (595/1238, 48.06%) had MGMT methylation. Additionally, 921 patients (921/1209, 76.18%) received chemotherapy within one month after primary glioma resection, while 912 patients (912/1204, 75.75%) received postoperative radiotherapy. The Kolmogorov–Smirnov test showed that data of the immune and inflammatory factors were non-normal distributed and were described as median (IQR) Table 1.
Percentage of Helper T Lymphocyte, B Lymphocyte, and NK Cell Correlated with Grades and Subtypes of Glioma
The percentages of NK cells (OR = 1.04, CI = 1.02–1.06, Adjusted P-value < 0.001) and C4 levels (OR = 1.72, CI = 1.704.23, Adjusted P-value < 0.001), both important components of innate immunity, were positively correlated with glioma grades Table 2 and Supplementary Table 1. ANOVA analysis revealed a significant difference in the percentage of NK cells among different subtypes (Adjusted P-value = 0.002) Table 3 and Supplementary Table 2.
Enlarge this image.Table 2 Correlations Between Preoperative Immune and Inflammatory Factors in Peripheral Blood and Glioma Grades (I–IV)
Enlarge this image.Table 3 Correlations Between Preoperative Immune and Inflammatory Factors in Peripheral Blood and Glioma Subtypes
Subgroup comparisons across different grades and subtypes revealed that the percentage of NK cells was significantly higher in grade IV (Median (IQR), 17.30 (12.41–22.68)) and GBM subtype (Median (IQR), 17.30 (12.70–22.39)) compared to less malignant grades (Grade I–III) and subtypes (Astrocytoma, Oligodendrocytoma, Anaplastic Astrocytoma (AA), and AO) (Independent-Samples T-Test, Adjusted P-value < 0.05) Figures 2 and 3, Supplementary Figure 1, and Supplementary Figure 2. Key components of adaptive immunity, including percentage of T lymphocytes (OR = 0.97, CI = 0.95–0.98, Adjusted P-value < 0.001), Helper T (Th) lymphocytes (OR = 0.97, CI = 0.96–0.98, Adjusted P-value < 0.001), and B lymphocytes (OR = 0.98, CI = 0.95–0.99, Adjusted P-value = 0.03), showed a negative correlation with glioma grades Table 2 and Supplementary Table 1. Specifically, the overall level of percentage of T lymphocytes was significantly lower in grade IV patients (Median (IQR), 68.71 (63.58–74.48)) compared to grade II (Median (IQR), 70.78 (68.97–73.55)) and grade III (Median (IQR), 70.36 (66.99–74.36)) patients (Independent-Samples T-Test, Adjusted P-value < 0.05) Figure 2. Subgroup analysis revealed that the overall level of percentage of Th lymphocytes was significantly higher in grade II compared to advanced glioma (Grade III and IV) (Independent-Samples T-Test, Adjusted P-value < 0.05) Figure 2. Less malignant subtypes, including Astrocytoma (Median (IQR), 38.54 (34.53–43.85)) and Oligodendrocytoma (Median (IQR), 39.21 (36.63–41.27)), had higher overall levels of percentage of Th lymphocytes compared to GBM (Median (IQR), 34.20 (29.80–32.90)) and AA (Median (IQR), 35.69 (32.16–39.14)) (Independent-Samples T-Test, Adjusted P-value < 0.05) Figure 3, Table 3 and Supplementary Table 2. The overall level of percentage of B lymphocytes was significantly higher in grade I glioma patients (Median (IQR), 14.75 (13.33–16.25)) compared to other grades (Independent-Samples T-Test, Adjusted P-value < 0.05) Figure 2. Similarly to the percentage of NK cells, the level of complement C4 was significantly higher in grade IV (Median (IQR), 0.24 (0.19–0.29)) compared to grade III (Median (IQR), 0.22 (0.18–0.28)) and grade I (Median (IQR), 0.17 (0.15–0.26)) patients, and grade II (Median (IQR), 0.23 (0.17–0.28)) compared to grade I patients (Independent-Samples T-Test, Adjusted P-value < 0.05) Figure 2. In conclusion, as glioma becomes more malignant, there appears to be a gradual activation of innate immunity, while gradual suppression of adaptive immunity.
Enlarge this image.Figure 2 Violin plots of the preoperative percentage of NK cell (A), percentage of CD3+ T lymphocyte (B), percentage of CD4+ Helper T lymphocyte (C), percentage of CD19+ B lymphocyte (D), and amount of C4 (E) in peripheral blood compared in different glioma grades; *P < 0.05; **P < 0.01; ***P < 0.001.
Enlarge this image.Figure 3 Violin plots of the preoperative percentage of NK cell (A) and percentage of CD4+ Helper T lymphocyte (B) in peripheral blood compared in different glioma subtypes. Pearson correlation coefficients among different immune and inflammatory factors (C); *P < 0.05; **P < 0.01; ***P < 0.001.
Additionally, Pearson correlation coefficients showed that the percentage of Th lymphocytes (r = −0.28, Adjusted P-value < 0.001) and B lymphocytes (r = −0.53, Adjusted P-value < 0.001) are negatively correlated with the percentage of NK cells, while the level of C4 is positively correlated with it. There was a negative correlation between the percentage of B lymphocytes or NK lymphocytes and the percentage of T lymphocytes, while the percentage of Th lymphocytes positively correlated with T lymphocytes Figure 3 and Supplementary Tables 3 and 4. This was consistent with the results of the subgroup comparison in the previous section. Though there are significant correlations among other immune and inflammatory factors, no correlation was observed between them and the grades or subtypes Figure 3 and Supplementary Tables 3 and 4.
Prognostic Value of nSII in Patients with Glioma
To identify whether nSII plays a role in the prognosis of glioma patients, we performed Cox regression analysis with age, WHO Grade, IDH1 mutation status, and 1p/19q codeletion status Supplementary Table 5. The absolute measured values of high immunoglobulin kappa light chain (AHR = 0.42, 95% CI = 0.22–0.81, P-value = 0.009), medium immunoglobulin lambda light chain (AHR = 0.40, 95% CI = 0.18–0.89, P-value = 0.02), and high immunoglobulin lambda light chain (AHR = 0.54, 95% CI = 0.29–1.00, P-value = 0.05) were independent predictors of the survival in glioma and were classified with a quartile cutoff Figure 4 and Supplementary Table 6. Compared with the lower amount group (1.01mg/L < LOW group ≤ 3.95 mg/L), patients with higher amounts (3.95 mg/L < MEDIUM and HIGH groups ≤ 21.4 mg/L) of kappa light chain had more prolonged median OS, and each group’s survival curves differed (Log Rank P-value = 0.0011) Supplementary Table 7 and Figure 4. Similar results were found in the lambda light chain group (Log Rank P-value = 0.0038) Supplementary Table 7 and Figure 4.
Enlarge this image.Figure 4 Forest plots of preoperative amounts of immunoglobulin kappa light chain (A) and immunoglobulin lambda light chain (B) in peripheral blood based on the Cox-proportional Hazards Regression model. KM survival curves of the preoperative amount of kappa light chain (C) and immunoglobulin lambda light chain in peripheral blood (D).
To explore which factor in nSII could be a prognostic biomarker for GBM or grade IV patients, Cox regression analysis was carried out with chemotherapy status (received or not), radiotherapy status (received or not), age, and IDH1 mutation status Supplementary Table 8. The multivariate regression model revealed that the percentage of B lymphocytes was the specific independent predictor of survival of patients with grade IV glioma when chemotherapy status, age, and IDH1 status were adjusted (AHR = 1.029, 95% CI = 1.003–1.056, P-value = 0.027) Figure 5 and Supplementary Table 9. The percentages of B lymphocytes of grade IV patients were classified with a quartile cutoff. Grade IV patients with higher B lymphocyte percentage (7.63% < MEDIUM and HIGH groups ≤ 48.55%) had shorter median OS (Log-Rank P-value = 0.031 < 0.05) Figure 5 and Supplementary Table 9.
Enlarge this image.Figure 5 Forest plots of the preoperative percentage of CD19+ B lymphocytes in peripheral blood of IV-grade glioma patients based on the Cox-proportional Hazards Regression model (A). KM survival curves of the preoperative percentage of CD19+ B lymphocytes in peripheral blood of IV grade glioma patients (B).
We established multiple Cox regression-based nomogram scale plots to calculate 1-, 3- and 5-year-survival probabilities for rapid assessment of primary survival probability Supplementary Figure 3. C-index was used to evaluate the predicted accuracy of our plots. The C-index indicated that the regression model integrated multi-feature data (including clinical characteristics, immune factors, and inflammatory factors) was significantly better than models constructed by any single feature Supplementary Figure 4.
Evaluation of Diagnostic Efficiency of nSII in Patients with Glioma
To assess the diagnostic value of specific nSII factors among different grades and subtypes of glioma, we evaluated the percentage of NK cells, T lymphocytes, Th lymphocytes, and B lymphocytes, as well as the level of C4 Supplementary Figure 5 and Supplementary Table 10.
We found that when distinguishing between grade II and grade III glioma, we can reach a high diagnostic efficacy with 0.704 AUC (95% CI, 0.639–0.769) using only the percentage of Th lymphocytes. Furthermore, in distinguishing between Grade II and Grade IV glioma, AUC values for the percentages of Th lymphocytes, NK cells, and CD3+ T lymphocytes were notably high, with respective values of 0.711 (95% CI, 0.694–0.787), 0.724 (95% CI, 0.671–0.776), and 0.741 (95% CI, 0.694–0.787). Additionally, the combined AUC value for these indicators was 0.845 (95% CI, 0.794–0.897). When distinguishing between grade I and grade IV glioma, we can achieve 0.662 AUC (95% CI, 0.583–0.741), 0.753 (95% CI, 0.687–0.819), and 0.801 (95% CI, 0.719–0.883) using the percentage of NK cells, B lymphocytes, or C4 level, respectively. Diagnostic efficacy was significantly improved (AUC: 0.879, 95% CI, 0.806–0.952) when combining all parameters. Other ROC curves are presented in Supplementary Figure 5.
Besides, the percentage of NK cells and Th lymphocytes exhibited good diagnostic performance in distinguishing between Astrocytoma and GBM or between Oligodendrocytoma and GBM. For diagnosing Astrocytoma and GBM, the AUC values for the percentage of NK cells, Th lymphocytes, and combined model were 0.727 (95% CI, 0.674–0.781), 0.748 (95% CI, 0.698–0.798), and 0.773 (95% CI, 0.731–0.815), respectively. For differentiating between Oligodendrocytoma and GBM, the AUC values were 0.737 (95% CI, 0.674–0.800) for the percentage of NK cells, 0.780 (95% CI, 0.726–0.833) for Th lymphocytes, and 0.753 (95% CI, 0.682–0.824) for their combined model.
Discussion
Intracranial immunosuppressive tumor microenvironments were well investigated recently,9 but the knowledge of systemic immune interactions remains limited in glioma.22,23 Furthermore, the effectiveness in treating glioma remains suboptimal due to the lack of biomarkers.1,24 Hence, it is imperative to elucidate the comprehensive immune status of glioma patients and unravel the relationship between the peripheral immune system and tumor onset and prognosis. In this multi-institutional study, we examined data collected from 1282 patients since 2006, assessing the prognostic and diagnostic significance of nSII parameters, and providing insights into the association between the peripheral immune system and glioma progression.
Our study, for the first time, revealed a positive correlation between the higher percentage of NK cells in peripheral blood and the glioma grades. NK cells play a pivotal role in anti-tumor immune response.25 Recent research has demonstrated that elevated RNA expression of NK cell-related genes was associated with the malignancy of glioma.26 This showed the potential involvement of NK cell activation in glioma progression. Additionally, components of adaptive immunity also exhibit correlations with the malignancy of glioma.27 Our observations indicated a negative correlation between the percentage of T lymphocytes and the glioma grades. This is consistent with previous findings of T lymphocyte exhaustion in GBM,28 but conflicts with another finding of significant reduction in T lymphocyte levels of newly diagnosed GBM patients.29 Our data also showed that the percentage of CD4+ Th lymphocytes was negatively associated with glioma malignancy, suggesting that CD4+ Th lymphocytes can represent the most suppressed subset of T lymphocytes.
CD4+ Th lymphocytes were a primary source of immune and inflammatory cytokines and can be categorized into Th1 and Th2 subtypes.30,31 Through IL-4 and INF-γ, Th1, and Th2 could negatively regulate mutual proliferation.32 Interestingly, we found that as glioma grades increase, the median levels of IL-4 decrease, while IL-2 increases (from grade II to IV). Additionally, elevated IL-2 secretion from Th1 lymphocytes may influence the increase in NK cells. Thus, we supposed that with increasing glioma grade, decreased Th2 cells weaken the inhibitory effect of IL-4 on Th1 lymphocytes, leading to increased IL-2 secretion and increased peripheral blood NK cells. However, we did not directly measure Th cell subsets, and peripheral blood IL-2 and IL-4 levels did not exhibit statistically significant differences among different glioma grades. Lastly, the percentage of CD4+ Th lymphocytes in peripheral blood is significantly lower in grade II glioma compared to Grade III and IV as well as less malignant subtypes (Oligodendrocytoma and Astrocytoma) from GBM and AA. This finding could assist in preoperatively defining histopathological characteristics without invasive examinations.
There remains controversy around the role of B lymphocytes in glioma development. Generally, B lymphocytes function as anti-tumor cells.30,33 However, emerging studies reported that higher infiltration of B lymphocytes in the GBM microenvironment was associated with poorer survival, possibly related to CD19+ B regulatory (Breg) cells.34 We observed a poorer prognosis for grade IV glioma patients with a higher proportion of CD19+ B lymphocytes in peripheral blood, consistent with previous studies.22,35 CD19+ Bregs can suppress CD8+ T lymphocytes and induce the production of Tregs,36 which promotes the formation of an immune-suppressive microenvironment. These results suggest the pro-tumor functions of B lymphocytes in glioma patients.
Complement C4 level was also significantly correlated with the glioma grades in our cohort. However, Gousias et al reported an inverse correlation between C4 level in peripheral blood and glioma grade. C4 played an important role in complement activation.37 The complement activation was widely involved in tumor progression.38 Van der Vlis T. et al reported that the expression of complement molecules can promote tumor progression, which echoes our study.39 Lastly, we found that higher amounts of peripheral immunoglobulin kappa and lambda light chains were associated with better prognosis. Though the mechanism remains unclear, monitoring peripheral blood light chains could improve patient management in treatment decisions.
Though our study provided robust evidence for defining nSII in glioma diagnosis, 2 limitations should be taken into careful consideration regarding further application. 1. Sample amount. Our cohort contained relatively few lower-grade patients compared to high-grade cases. This imbalance may cause an underestimate of unique immune signatures characteristic of lower-grade glioma (LGGs) and a reduction in the accuracy of grade-discriminatory cutoff values for immune markers. Then, the current findings may be most applicable to high-grade glioma diagnosis until validated in larger LGG cohorts. 2. Sample source. Although our cohort consists of 3 large centers across South to North China, samples from institutions with different patient demographics around the world may lead to a more objective and universally applicable finding. To enhance generalizability of nSII, our further studies could enroll a larger population, especially LGG patients.
Conclusions
Overall, our findings were clinically important in the following ways: 1) Compared to invasive examinations, non-invasive peripheral blood examination is cheaper and more accessible, and to a certain extent, can reflect the immune status of patients. We found that specific immune and inflammatory factors were correlated with glioma grades and subtypes. Diverse peripheral blood factors, such as light chain immunoglobulin, may serve as independent prognostic indicators. Preoperative examination of these factors could monitor the immune status of patients and assist in formulating treatment plans. 2) Identifying various immune and inflammatory factors in glioma patients can assist and guide anti-tumor immunotherapy. It was of great potential to utilize NK cells in the peripheral blood for anti-tumor immunotherapy. 3) Our study presented the peripheral immune landscape of glioma patients, offering valuable evidence for the interplay between the glioma immune-suppressive microenvironment and peripheral immunity. To advance our study, we will confirm the diagnostic and prognostic value of nSII in a larger-scale cohort, including pre- and post-treatment glioma patients. In the meanwhile, we would explore the underlying mechanism that how the light chain immunoglobulins influence glioma progression and response to immunotherapy in glioma.
Data Sharing Statement
The data generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Ethical Statement
The current study was approved by the Ethics Committee of the Sun Yat-sen University Cancer Center (SL-B2023-529) on October 12, 2023, registered and assigned on ClinicalTrials.gov (NCT05635435) on December 2, 2022. All patient data was anonymized during processing. This study was performed following the Declaration of Helsinki.
Funding
This work was supported by the following: the National Natural Science Foundation of China (grant no. 82373386) to Yong-Gao Mou; the Key Science and Technology Project of Guangdong Province, China (grant no. 2023B0303020002) to Yong-Gao Mou; the Basic and Applied Basic Research Foundation of Guangdong Province (grant no. 2021A1515111198) and the National Natural Science Foundation of China (grant no. 82303310) to Sheng Zhong; and the Beijing New-star Plan of Science and Technology (grant no. Z201100006820148) to Ye Cheng.
Disclosure
The authors report no conflicts of interest in this work.
1. Schaff LR, Mellinghoff IK. Glioblastoma and Other Primary Brain Malignancies in Adults: a Review. JAMA. 2023;329(7):574–587. doi:10.1001/jama.2023.0023
2. Ohgaki H, Kleihues P. Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J Neuropathol Exp Neurol. 2005;64(6):479–489. doi:10.1093/jnen/64.6.479
3. Wen PY, Weller M, Lee EQ, et al. Glioblastoma in adults: a Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions. Neuro Oncol. 2020;22(8):1073–1113. doi:10.1093/neuonc/noaa106
4. Ahmed R, Oborski MJ, Hwang M, Lieberman FS, Mountz JM. Malignant gliomas: current perspectives in diagnosis, treatment, and early response assessment using advanced quantitative imaging methods. Cancer Manage Res. 2014;6:149–170. doi:10.2147/CMAR.S54726
5. Gerlinger MRA, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–892. doi:10.1056/NEJMoa1113205
6. Liu LY, Wu W, Cai C, Zhang H, Shen H, Han Y. Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduct Target Ther. 2023;8(1):160. doi:10.1038/s41392-023-01419-2
7. Kros JM, Mustafa DM, Dekker LJM, Sillevis Smitt PAE, Luider TM, Zheng -P-P. Circulating glioma biomarkers. Neuro Oncol. 2015;17(3):343–360. doi:10.1093/neuonc/nou207
8. Tomaszewski W, Sanchez-Perez L, Gajewski TF, Sampson JH. Brain Tumor Microenvironment and Host State: implications for Immunotherapy. Clin Cancer Res. 2019;25(14):4202–4210. doi:10.1158/1078-0432.CCR-18-1627
9. Lin H, Liu C, Hu A, Zhang D, Yang H, Mao Y. Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives. J Hematol Oncol. 2024;17(1):31. doi:10.1186/s13045-024-01544-7
10. Quail DF, Joyce JA. The Microenvironmental Landscape of Brain Tumors. Cancer Cell. 2017;31(3):326–341. doi:10.1016/j.ccell.2017.02.009
11. Sh LK, Zhang B, Ou X, et al. Myeloid-derived suppressor cells as immunosuppressive regulators and therapeutic targets in cancer. Signal Transduct Target Ther. 2021;6(1):362.
12. Se GMM, Ryan KJ, Chongsathidkiet P, Lorrey SJ, Wilkinson DS, Fecci PE. Immune suppression in gliomas. J Neurooncol. 2021;151(1):3–12. doi:10.1007/s11060-020-03483-y
13. Ms AER, Nieland L, Wei Z, et al. Glioblastoma-Associated Microglia Reprogramming Is Mediated by Functional Transfer of Extracellular miR-21. Cell Rep. 2019;28(12):3105–3119.e3107. doi:10.1016/j.celrep.2019.08.036
14. Zhao YHW, Kim TM, Jung Y, et al. MicroRNA-29a activates a multi-component growth and invasion program in glioblastoma. J Exp Clin Cancer Res. 2019;38(1). doi:10.1186/s13046-019-1026-1
15. Mg GMR, Mantovani A. Cancer Inflammation and Cytokines. Cold Spring Harb Perspect Biol. 2018;10(8):1.
16. Bambury RM, Teo MY, Power DG, et al. The association of pre-treatment neutrophil to lymphocyte ratio with overall survival in patients with glioblastoma multiforme. J Neurooncol. 2013;114(1):149–154. doi:10.1007/s11060-013-1164-9
17. Cupp MA, Cariolou M, Tzoulaki I, Aune D, Evangelou E, Berlanga-Taylor AJ. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 2020;18(1):360. doi:10.1186/s12916-020-01817-1
18. Ruofei Liang NC, Mao L, Wang X, Mao Q, Liu Y, Liu Y. Significance of systemic immune-inflammation index in the differential diagnosis of high- and low-grade gliomas. Clin Neurol Neurosurg. 2018;164:50–52. doi:10.1016/j.clineuro.2017.11.011
19. Ricklin D, Lambris JD. Complement in immune and inflammatory disorders: pathophysiological mechanisms. J Immunol. 2013;190(8):3831–3838. doi:10.4049/jimmunol.1203487
20. Das M, Karnam A, Stephen-Victor E, et al. Intravenous immunoglobulin mediates anti-inflammatory effects in peripheral blood mononuclear cells by inducing autophagy. Cell Death Dis. 2020;11(1):50. doi:10.1038/s41419-020-2249-y
21. Zhang JJZY, Chen Q, Qn C, et al. A Novel Prognostic Marker and Therapeutic Target Associated with Glioma Progression in a Tumor Immune Microenvironment. J Inflamm Res. 2023;Volume 16(16):895–916. doi:10.2147/JIR.S398775
22. Marinari E, Allard M, Gustave R, et al. Inflammation and lymphocyte infiltration are associated with shorter survival in patients with high-grade glioma. Oncoimmunology. 2020;9(1):1779990. doi:10.1080/2162402X.2020.1779990
23. Kmiecik J, Poli A, Brons NH, et al. Elevated CD3+ and CD8+ tumor-infiltrating immune cells correlate with prolonged survival in glioblastoma patients despite integrated immunosuppressive mechanisms in the tumor microenvironment and at the systemic level. J Neuroimmunol. 2013;264(1–2):71–83. doi:10.1016/j.jneuroim.2013.08.013
24. Tom MC, Cahill DP, Buckner JC, Dietrich J, Parsons MW, Yu JS. Management for Different Glioma Subtypes: are All Low-Grade Gliomas Created Equal? Am Soc Clin Oncol Educ Book. 2019;39(39):133–145. doi:10.1200/EDBK_238353
25. Coenon L, Geindreau M, Ghiringhelli F, Villalba M, Bruchard M. Natural Killer cells at the frontline in the fight against cancer. Cell Death Dis. 2024;15(8):614. doi:10.1038/s41419-024-06976-0
26. Li C, Liu F, Sun L, Liu Z, Zeng Y. Natural killer cell-related gene signature predicts malignancy of glioma and the survival of patients. BMC Cancer. 2022;22(1):230. doi:10.1186/s12885-022-09230-y
27. von Roemeling CA, Wang Y, Qie Y, et al. Therapeutic modulation of phagocytosis in glioblastoma can activate both innate and adaptive antitumour immunity. Nat Commun. 2020;11(1):1508. doi:10.1038/s41467-020-15129-8
28. Woroniecka K, Chongsathidkiet P, Rhodin K, et al. T-Cell Exhaustion Signatures Vary with Tumor Type and Are Severe in Glioblastoma. Clin Cancer Res. 2018;24(17):4175–4186. doi:10.1158/1078-0432.CCR-17-1846
29. Zhang L, Yu JS. Triptolide reverses helper T cell inhibition and down-regulates IFN-γ induced PD-L1 expression in glioma cell lines. J Neurooncol. 2019;143(3):429–436. doi:10.1007/s11060-019-03193-0
30. Schwartz M, Zhang Y, Rosenblatt JD. B cell regulation of the anti-tumor response and role in carcinogenesis. J Immunother Cancer. 2016;4(1):40. doi:10.1186/s40425-016-0145-x
31. Berger A. Th1 and Th2 responses: what are they? BMJ. 2000;321(7258):424. doi:10.1136/bmj.321.7258.424
32. Ivanova EA, Orekhov AN. T Helper Lymphocyte Subsets and Plasticity in Autoimmunity and Cancer: an Overview. Biomed Res Int. 2015;2015:327470. doi:10.1155/2015/327470
33. Sarvaria A, Madrigal JA, Saudemont A. B cell regulation in cancer and anti-tumor immunity. Cell Mol Immunol. 2017;14(8):662–674. doi:10.1038/cmi.2017.35
34. Lee-Chang C, Rashidi A, Miska J, et al. Myeloid-Derived Suppressive Cells Promote B cell-Mediated Immunosuppression via Transfer of PD-L1 in Glioblastoma. Cancer Immunol Res. 2019;7(12):1928–1943. doi:10.1158/2326-6066.CIR-19-0240
35. Zhang Y, Wang W, Liu Q, et al. CD19(+)CD73(+) B cells infiltration indicates poor prognosis and unfavorable responses to immunotherapy in gastric cancer. Int Immunopharmacol. 2024;141:113002. doi:10.1016/j.intimp.2024.113002
36. Mohib K, Cherukuri A, Zhou Y, Ding Q, Watkins SC, Rothstein DM. Antigen-dependent interactions between regulatory B cells and T cells at the T:B border inhibit subsequent T cell interactions with DCs. Am J Transplant. 2020;20(1):52–63. doi:10.1111/ajt.15546
37. Vyse TJ, Tsao BP. Complement C4, the Major Histocompatibility Complex, and Autoimmunity. Arthritis Rheumatol. 2022;74(8):1318–1320. doi:10.1002/art.42119
38. Zhang R, Liu Q, Li T, Liao Q, Zhao Y. Role of the complement system in the tumor microenvironment. Cancer Cell Int. 2019;19(1):300. doi:10.1186/s12935-019-1027-3
39. van der Vlis TAM B, Kros JM, Mustafa DAM, et al. The complement system in glioblastoma multiforme. Acta Neuropathol Commun. 2018;6(1):91. doi:10.1186/s40478-018-0591-4
Jie Lu,1,* Zhi-Yun Zhang,2,* Sheng Zhong,1,* Davy Deng,3,* Wen-Zhuo Yang,1,* Su-Wen Wu,1 Ye Cheng,4 Yang Bai,2 Yong-Gao Mou1
1Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 2Department of Plastic and Reconstructive Surgery, The First Hospital of Jilin University, Changchun, People’s Republic of China; 3Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA; 4Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yong-Gao Mou; Yang Bai, Email [email protected]; [email protected]
© 2025. This work is licensed under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Zhang, Z Y; Zhong, S; Deng, D; Yang, W Z; Wu, S W; Cheng, Y; Bai, Y; Mou, Y G