Deficiency of SMARCB1 drives an immunosuppressive microenvironment in meningioma

Abstract

Background

Meningiomas are the most common primary intracranial tumor in adults and systemic therapy is urgently needed for high-grade fatal tumors and those cannot be completely removed by surgery. Multiomics studies have established a molecular classification system in addition to the grading system by the World Health Organization, and SWI/SNF-related BAF chromatin remodeling complex subunit B1 (SMARCB1) mutation was enriched in the immunogenic subgroup. Meningiomas are myeloid-dominant tumors with abundant and unevenly distributed CD163⁺ macrophages, a feature linked to intratumoral heterogeneity. However, the biological drivers of this phenomenon remain unknown.

Methods

A study cohort consisting of 113 patients was established to examine the association between serum immune profile and relapse-free survival. The second study cohort containing 35 patients across different WHO grades and disease states was established to validate and identify immune cell infiltration in the tumor microenvironment. Spatial distribution of immune cells was accessed by immunohistochemistry staining and multiplex immunofluorescence staining. Single-cell RNA sequencing (RNA-seq), bulk RNA-seq and whole exon seq data were analyzed to identify genomic signatures that represent the immunogenic subgroup of meningiomas. Public databases were explored to determine a potential mechanistic link between SMARCB1 and the interleukin-17/colony stimulating factor 1 (IL-17/CSF1) axis.

Results

Serum IL-17 A and IL-5 levels favored a good prognosis of meningioma. CD163⁺ macrophages were enriched in meningiomas regardless of the WHO grade and disease status (primary or recurrent). Compared to CD25⁺/Foxp3⁺ regulatory T cells and CD15⁺/CD33⁺ myeloid-derived suppressor cells, CD163⁺ macrophages tend to be more enriched around SMARCB1-deficient tumor cells. RNA-seq revealed that a 14-gene signature, including IL-17, CSF1, and related upstream and downstream genes, accurately characterizes the immunogenic subtype of meningiomas.

Conclusion

The findings reveal that the infiltration of CD163⁺ macrophages in meningioma may be mediated by the IL-17/CSF1 axis through SMARCB1 regulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40478-025-02217-3.

Introduction

Meningiomas are the most common intracranial tumors in adults [1]. The rapid aging and easy access to neuroimaging have led to an increase in the reported incidence of meningioma [2, 3]. Although the majority (approximately 80.1%) of meningiomas are benign (World Health Organization [WHO] grade 1), the remaining high-grade types (grade 2, 18.3%; grade 3, 1.5%) are biologically aggressive, with an exceptionally low metastatic incidence ranging from 0.1% to 0.76% [4, 5]. Nowadays, surgery remains the main treatment for meningiomas, and radiotherapy is the only widely accepted adjuvant therapy [2]. However, the intricate anatomy of the skull base often prevents the complete resection of meningiomas, leading to recurrence even in benign cases [6]. Moreover, protocols for radiation dose and treatment volume have not been standardized. Furthermore, the radiosensitivity of adjacent critical structures, particularly the brainstem and optic chiasm, prevents the use of radiotherapy for meningiomas in these areas [2, 7]. Therefore, systemic treatment strategies for meningiomas are urgently needed.

Genomic analysis revealed mutations of tumor necrosis factor receptor-associated factor 7, Kruppel-like factor 4, AKT serine/threonine kinase 1, smoothened (SMO), and other genes, except for neurofibromatosis 2 (NF-2), in meningioma, paving a novel avenue for targeted therapeutics [8–11]. Nevertheless, clinical trials testing sunitinib, bevacizumab, and other molecules showed limited efficacy [2, 12, 13]. More profound studies have discovered several genomic mutations associated with the prognosis of meningiomas, such as BRCA1-associated protein-1, polybromo 1, telomerases reverse transcriptase promoter, phosphatase and tensin homolog, and SWI/SNF-related BAF chromatin remodeling complex subunit B1 (SMARCB1) [4]. Besides, studies of meningioma heterogeneity through multiomics methods have established several molecular classification systems. In brief, meningiomas were classified into four molecular subgroups, in which the immunogenic group was enriched for immune-related transcription pathways, whereas other groups were enriched in nucleotide and lipid metabolism and cell cycle pathways or designated as NF2-wild type [14–18]. Each of these molecular classification systems appeared to outperform the up-to-date WHO classification system in predicting outcomes and assisting clinical treatment. However, not all medical centers have access to expensive devices required for high-throughput technology. The interpretation of big data generated by high-throughput technology is also challenging [2]. Therefore, identifying immunohistochemistry (IHC) markers that accurately represent molecular classifications could serve as an economic and practical approach in clinical settings. Additionally, liquid biopsy is an emerging research area focused on the diagnosis and subtyping of solid tumors. Thus, this study was conducted to explore a panel of gene signatures that could be utilized to identify immunogenic meningioma.

Serum cytokines are associated with the severity of malignant tumors and are crucial in shaping the tumor microenvironment (TME) [19, 20]. The levels of colony-stimulating factor 1 (CSF1), a critical growth factor for macrophage maturation, are significantly elevated in patients with high-grade meningiomas compared with healthy controls [21, 22]. The abundance of CD163⁺ macrophages in meningiomas contributes to an immunosuppressive TME. Programmed cell death protein 1 and programmed death-ligand 1 (PD-1/PD-L1) immunotherapy has shown success in many solid tumors; however, its efficacy in normalizing meningioma TME remains limited [23, 24]. Although anti-CSF1/CSF1 receptor (CSF1R) therapies can inhibit tumor growth in myeloid-dominant cancers such as pancreatic cancer and meningiomas, the mechanisms regulating CSF1 secretion in meningiomas remain unclear [25, 26].

To comprehensively characterize the meningioma immune landscape, we integrated serum immune profiles, TME features, genomics, transcriptomics, and clinical data across WHO grades and disease states. This approach identified a 14-gene signature defining an immunogenic subgroup, yielding novel insights critical for the development of systemic therapies and refined disease management.

Materials and methods

Study cohorts

This study complied with all ethical principles and approved by Changhai Hospital. All hospitalized patients routinely signed a written waiver to articulate their willingness to contribute to research projects. To investigate the correlation between serum immune profiles and progression, 113 meningioma cases from July 2022 to December 2024 were included. To further exam TME of meningioma, 35 primary and recurrent meningioma cases spanning WHO grade 1 to 3 were included. Patient demographics, treatment, serum cytokine levels and anatomical location of meningioma were recorded from the electrical medical record. Clinical outcomes and prognosis were recorded through telephone following-up by B.J. Pathological examination was performed by board-certified pathologists (B.J. and M.X.H.) and histopathological subtypes were assigned according to the 5th WHO classification of CNS tumors [4].

Histology and scoring of tumor-infiltrated immune cells

Meningioma samples were fixed in 10% neutral formalin (CITOTEST, catalog # 80011-0099) and embedded in paraffin (CITOTEST, catalog # 80200-0008). Serial sections of 4 μm thickness were mounted onto slides for H&E (BASO, hematoxylin, catalog # BA4041; BASO, eosin, catalog # BA4022) or immunohistochemistry (IHC) staining following standard protocols. Clinical validated IHC was performed for CD4 (MXB Biotechnologies, SP35 clone, catalog # RMA-0620), CD8 (MXB Biotechnologies, SP16 clone, catalog # RMA-0514), CD68 (ZSGB-Biotechnology, KP1 clone, catalog # ZM-0060), CD163 (MXB Biotechnologies, MX081 clone, catalog # MAB-0869), CD20 (MXB Biotechnologies, L26 clone, catalog # Kit-0001), CD38 (ZSGB-Biotechnology, UMAB263 clone, catalog # ZM-0422), PD1 (MXB Biotechnologies, MX033 clone, catalog # MAB-0734), MPO (ZSGB-Biotechnology, rabbit multiclonal Ab, catalog # ZA-0197), SMARCB1 (ZSGB-Biotechnology, OTIR4G9 clone, catalog # ZA-0696), and EMA (ZSGB-Biotechnology, GP1.4 clone, catalog # ZM-0095) at the Department of pathology at Changhai Hospital using a Leica Bond Ⅲ staining system and digital images were obtained using Leica Aperio CS2 device. Scoring of tumor-infiltrated immune cells was conducted independently by M.X.H. and B.J. following a proposed standard methodology raised by the International Immuno-Oncology Biomarker Working Group [27]. The average score was calculated to represent tumor-infiltrated immune cells. When there is a 10% or greater discrepancy between the scores given by M.X.H. and B.J., the researchers will jointly reevaluate. H&E image and IHC image were merged with Aperio ImageScope (Leica, v12.3.3.5030) software for subsequent assessment. H&E images were used to define tumoral and stroma area. In brief, a proper region in “central tumor” was selected. Stroma Areas occupied by immune cells in a certain “central tumor” region were reported as % infiltrated immune cells. Three different regions were randomly selected for quantification, and an average score was used to represent final % infiltrated immune cells. Dual IHC staining was conducted using DS-0002 staining kit (ZSGB-Biotechnology) by following manufacturing instruction and monoclonal antibodies for EMA (ZSGB-Biotechnology, GP1.4 clone, catalog # ZM-0095), CD163 (ZSGB-Biotechnology, 10D6 clone, catalog # ZM-0428), and SMARCB1 (ZSGB-Biotechnology, OTIR4G9 clone, catalog # ZA-0696) were used.

Peritumor cerebral edema

Tumor size and peritumoral cerebral edema (PTCE) were visually assessed by B.J. and a professional radiologist X.T. using the most common method in clinical practice [28, 29]. In short, on T1-weight Magnetic Resonance Image (MRI), the maximum perpendicular diameters of tumor were assessed on the axial (x and y) images and coronal (y) diameters were assessed on the coronal images. PTCE was visually assessed on FLAIR (Fluid-attenuated Inversion Recovery Image) sequences or T2-weighted sequences, when FLAIR was unavailable, in the same way. The tumor volume (V_tumor) and tumor plus PTBE volume (V_{tumor + PTBE}) were estimated by using formula for a spheroid: . Edema index was further calculated to determine relationship between tumor size and edema volumes, which was defined as: EI=V _{tumor + PTBE} /V _tumor. Thus, an EI of 1 indicates no edema and an EI>1 indicates PTCE.

Immune signature scoring

Transcriptomic data from the cBioPortal dataset were analyzed to identify differentially expressed genes (DEGs) across 4 molecular subtypes: hypermetabolic, immunogenic, NF-2 wild-type, and proliferative. Using DESeq2 (v1.34.0) in R (v4.1.3), genes with a false discovery rate (FDR) < 0.01 and absolute log2 fold change > 1.5 were classified as significant DEGs. 14 candidate genes were selected as an immunogenic signature based on their known roles in immune regulation and subtype-specific expression patterns. The enrichment levels of this 14-gene immunogenic signature across meningioma subtypes were quantified using Gene Set Variation Analysis (GSVA) through the GSVA package (v1.42.1). The resulting enrichment scores were z-score normalized across the cohort. Final visualization was performed using ggplot2 (v3.5.2), with normalized GSVA scores displayed as violin plots stratified by molecular subtypes.

Bulk-RNA sequencing

Total RNA of 14 clinical samples was purified from FFPE blocks using an RNA purification kit (SHBIO, catalog # 72302-50) following the manufacturer’s instructions. A RIN number was checked to inspect RNA integrity by an Agilent 2100 Bioanalyzer /Agilent 4200 TapeStation (Agilent technologies). Qualified total RNA was further cleaned-up by RNAClean XP Kit (Beckman Coulter Inc, catalog # A63987) and RNase-Free DNase Set (QIAGEN, catalog # 79254). Targeted gene expression profiling was performed using a barcode-based panel. 200 ng of total RNA were applied to synthesize cDNA by reverse transcription using SMARTer stranded total RNA-seq Kit v2 (Takara, catalog # 634412) and cDNA product was hybridized to barcoded reporter probes for sequencing. Library quality control was performed using Qubit quantification and Agilent Bioanalyzer 2100 (Agilent technologies). Successful libraries exhibited peak distribution between 200 and 1000 bp, with main peak located around 300–400 bp. Bulk-RNA sequencing was performed by Illumina NovaSeq6000 platform. The raw reads were aligned to the human reference genome (GRCh38) using STAR (v2.7.11b), and gene expression was quantified as FPKM values with featureCounts (v2.0.7). Quality control was conducted using FastQC and MultiQC.

Whole exon sequencing

DNA was extracted from FFPE using QIAamp DNA FFPE tissue kit (QIAGEN, GmBH, Catlog # 56404, Germany) following the manufacturer’s instructions. Individual library preparations, hybridizations, and captures were performed following the protocol of SureSelectXT Target Enrichment System for Illumina Paired-End Sequencing Library (Agilent Technologies, USA). Quantity assessment of library was conducted by Qubit^® 2.0 Fluoromete and 2100 Bioanalyzer High Sensitivity DNA Assay. TruSeq PE Cluster Kit (Illumina) was used for cluster generation in an Illumina cBOT instrument following the manufacturer’s protocol (cBot™ User Guide). Libraries were loaded into each lane of flow cell. Sequencing was performed on an Illumina HiSeq X instrument (Illumina) by the manufacturer’s protocol (HiSeq^® X System User Guide). Multiplexed paired-reads runs were carried out with 125 cycles.

Fluorescence in situ hybridization

Hybridized FFPE specimens in 4 μm thickness were pretreated according to the instruction of the ZytoLight FISH-Tissue Implementation Kit (ZytoVision, Catalog # Z-2099-20, Germany). The qualitative detection of human CDKN2A gene deletion as well as classical satellite Ⅲ region of chromosome 9 was carried out subsequently by using Zyto Light SPEC CDKN2A/CEN 9 Dual Color Prob (ZytoVision, Catalog # Z-2063-200, Germany). All procedures were conducted strictly following manufacturer’s instructions.

Principal component analysis and immune signature scoring

Quantified expression data were subjected to principal component analysis (PCA) using the prcomp function in R (v4.1.3), with the top two principal components (PC1 and PC2) visualized to assess sample clustering. The 14-gene immune signature score was calculated for each sample via GSVA (v1.42.1) and scores were projected onto the PCA plot, and gradient colors showed high- or low-scores.

Immune infiltration score

The immune infiltration score quantifies the balance between pro- and anti-tumor immune components in TME through standardized integration of key cell type percentages. The score was calculated by summing z-score normalized values of CD8⁺ cytotoxic T cells (anti-tumor, + 1 weight), CD4⁺ helper T cells (protective, + 1 weight) and with CD68⁺ macrophages (contextual, + 1 weight) providing additional myeloid reference, then subtracting the z-score of CD163⁺ macrophages (immunosuppressive, − 1 weight). Each cell population’s percentage was normalized using cohort-specific means (µ) and standard deviations (σ) to enable cross-sample comparison. Positive scores indicated an immune-active phenotype (dominant CD8/CD4), while negative scores reflected immunosuppression (dominant CD163). This weighted approach highlighted samples with extreme immune deviations that may benefit from targeted immunotherapies.

K-means clustering and subtype identification

Unsupervised K-means clustering (k = 4) was applied to the PCA-transformed data (PC1 and PC2) using the stats (v4.4.1) package in R, with Euclidean distance as the metric. Subtype-specific immune scores were compared and revealed that Cluster 4 exhibited significantly higher immune activation.

Analysis of publicly available meningioma datasets

To explore gene expression patterns, we performed analysis using the cBioPortal database (https://www.cbioportal.org/). We examined transcription levels of CSF1, CD163, IL17RA, NF-κB pathway components (RELA and NFkB1), SMARCB1, and INGR1, in publicly available meningioma clinical samples. Pearson’s correlation analysis was conducted using built-in tools to assess relationships between these genes. For additional validation, we analyzed meningioma cell line data from two public resources: the Single Cell Expression Atlas (SCEA, https://www.ebi.ac.uk/gxa/sc/home) and the Cancer Cell Line Encyclopedia (CCLE, https://sites.broadinstitute.org/ccle). Processed expression data for CH-157MN and IOMM-Lee cell lines were downloaded to generate comparative visualizations of SMARCB1, CSF1, and IL-17 signaling pathway components through heatmaps and histograms. This multi-database approaches enabled cross-validation of findings between clinical samples and established cell line models, while utilizing each platform’s analytical capabilities. All analyses were performed using the processed data available from these resources to maintain consistency with their respective normalization methods.

Statistical analysis

Unpaired student’s t test was performed to compare infiltration density of immune cells in different meningioma samples.

Data availability

Our bulk-RNA sequencing data was deposited in GEO database (GSE304666) and SRA database (PRJNA1301565) for public use.

Results

Characteristics of clinical cohorts

Liquid biopsy for diagnosis and subtyping is a growing area of interest, and patient serum could effectively differentiate meningiomas from other intracranial tumors [30]. A panel of cytokines, including IL-10, IL-17A, IL-12P70, IL-8, IFN-α, IFN-γ, IL-5, IL-6, TNF-α, IL-1β, IL-4, and IL-2, are routinely examined at Changhai Hospital. To clarify whether a serum cytokine level is a potential biomarker for meningioma outcomes, a cohort consisting of 113 cases was established, which included 36 male (31.86%) and 77 female (68.14%) patients, with ages ranging from 26 to 79 years (SI Fig. 1a and b). Owing to the low incidence of high-grade meningiomas, only WHO grades 1 (n = 100, 89.38%) and 2 (n = 13, 10.62%) tumors were included (SI Fig. 1c). Meningothelial meningiomas were the most common histological subtype, accounting for 73.45%, whereas the remaining cases were assigned to other less common histological subtypes, such as fibrous, transitional, psammomatous, angiomatous, microcystic, atypical, and chordoid (SI Fig. 1d). Moreover, 46.02% of the meningiomas were located at convexity sites, 36.28% were located at the skull base, and only 17.70% were located at the parasagittal and falcine sites (SI Fig. 1e). To investigate immune cell infiltration in the TME across primary and recurrent meningiomas of different WHO grades, a second cohort of 35 cases was established, which included 28 primary (male, n = 11; female, n = 17) and 7 recurrent (male, n = 17; female, n = 4) cases (SI Fig. 2a). The average ages of male and female patients with primary and recurrent tumors across WHO grades 1, 2, and 3 tumors were 60.7, 55.3, 58.3, 55.3, 48.75, and 48.5 years, respectively (SI Fig. 2b). Regarding tumor locations, 16 were situated at convexity sites, 15 at the skull base, and 4 in parasagittal and falcine sites (SI Fig. 2c). The cohort included 10 distinct meningioma subtypes, encompassing nearly all histopathological types defined in the WHO Classification system (SI Fig. 2d).

Serum IL-17 A is a good prognostic factor of meningiomas

Serum cytokine levels were associated with tumor types, such as squamous cell carcinoma, breast cancer, and endometrial carcinoma [31–33]. The combined analysis of Genome Wide Association Study and Mendelian randomization revealed that serum cytokine levels influenced the meningioma TME, indicating that circulating cytokines might be valuable markers for the subtyping and prognosis prediction of meningiomas [19]. In this study, raw serum level data of 12 cytokines were obtained from clinical records. Approximately 30% of cases in the cohort exhibited high levels of IL-10, IL-12P70, IL-8, and IL-6, whereas increases in IL-17A, IFN-α, IFN-γ, IL-5, and TNF-α were observed far less frequently (Fig. 1a). The positive correlations observed among cytokines, such as IFN-γ/IL-17 A, IL-1β/IFN-γ/IL-12P70/ IL-4, and IL-4/IL-2, indicated a complex immunoregulatory network in meningiomas. The IFN-γ/IL-17A link hinted at residual Th1–Th17 crosstalk, possibly counterbalanced by tumor-driven immunosuppression. Strong IL-1β associations with IFN-γ and IL-12P70 might reflect myeloid cell-mediated T/NK cell recruitment, whereas paradoxical IL-4 correlations with proinflammatory cytokines (IL-12P70/IL-1β) could indicate feedback inhibition of CD163 + macrophages. The IL-2/IL-4 synergy further supported an immunosuppressive T regulatory cell-favoring milieu (Fig. 1b). Collectively, these interactions proposed that meningiomas exploit cytokine redundancy to sustain an “inflamed-but-suppressed” microenvironment where simultaneous blockade of IL-17/IL-1β and disruption of the IL-4/IL-2 axis may be required for effective immunotherapy.

Fig. 1.

Patient serum cytokine levels in meningioma. a The serum levels of a panel of 12 cytokines were measured 24–48 h before surgical resection. b Serum levels of cytokines were normalized and assigned for correlation analysis. b–e Kaplan–Meier curves showed relapse-free survival by serum cytokine levels. The elevation of serum IL-17A (c) and IL-5 (e) levels indicated good prognosis of meningioma, whereas IL-6 (d) correlated with poor outcomes

Comparative analyses of the serum cytokine levels between WHO grade 1 and 2 meningiomas demonstrated the lack of significance in all the 12 cytokine levels, which were decreased in WHO grade 2 cases, except for IFN-α (p < 0.05), indicating attenuated inflammatory responses. (SI Fig. 3a–l). To further determine whether serum cytokine levels were associated with prognosis, a telephone follow-up was conducted 6 months to 2 years after meningioma resection. The Kaplan–Meier survival analysis demonstrated distinct prognostic associations between cytokine levels and RFS. As illustrated, high IL-17A (Fig. 1c) and IL-5 (Fig. 1e) levels were correlated with improved clinical outcomes, as evidenced by higher survival probabilities over time (IL-17A, p = 0.14; IL-5, p = 0.13). Conversely, high IL-6 levels (Fig. 1d) exhibited a trend toward poor prognosis (p = 0.13). However, the serum level of other cytokines did not show any remarkable trend regarding prognosis (SI Fig. 4a–i).

CD163⁺ TAMs are the predominant immune cells infiltrating the meningioma TME

Immunotherapy is a promising therapeutic strategy for several types of solitary tumors. Even though meningioma is immunosuppressive, clinical trials have proved that PD-1 monoclonal antibodies (pembrolizumab and nivolumab) could improve the prognosis of certain meningiomas [23, 34]. Most meningiomas are located outside of the brain-blood barrier; thus, immune therapy is theoretically feasible in meningiomas [35, 36]. To determine infiltrated immune cell types in meningiomas across different WHO grades and disease states, further IHC staining showed that the intensity of CD68⁺ macrophages in WHO grade 2 tumors was higher than those in grades 1 and 3, whereas the intensity of CD163⁺ macrophages were significantly decreased across WHO grades 1, 2, and 3 (Fig. 2a–h). Conversely, the intensity of CD68⁺ macrophages in recurrent cases significantly increased compared with primary cases, whereas the infiltration of CD163⁺ macrophages slightly increased (Fig. 2a–f, i–j). CD163⁺ macrophages accounted for approximately 20% of infiltrated immune cells, whereas the percentage of CD68⁺ macrophages was less than 5%, demonstrating that CD163 + macrophages were the most prevalent immune cells, modeling an immune-suppressive environment in primary and recurrent meningioma regardless of the WHO grades.

Fig. 2.

CD163⁺ TAMs were the predominant immune cells within the tumor microenvironment of meningiomas. H&E and immunohistochemistry staining showed the infiltration of CD 68⁺ and CD163⁺ TAMs among primary and recurrent meningioma cases across WHO grades 1 (a and d), 2 (b and e), and 3 (c and f). Statistical analysis of the percentage of CD68⁺ (g and i) and CD163⁺ (h and j) TAMs. ns, not significant, ** P < 0.01

T cells are secondary main immune cell types. The CD4⁺ T cells accounted for < 5% regardless of the WHO grade and disease states, showing minimal difference among groups (SI Fig. 5a–g and i). However, CD8⁺ T cell intensity significantly increased in WHO grade 2 tumors compared with WHO grade 1 and dramatically increased in recurrent cases compared with primary ones, indicating a more active anti-tumor TME (SI Fig. 5a–f, h, and j). However, CD20⁺ B cells, CD38⁺ plasma cells, MPO⁺ neutrophils, and PD-1⁺ T cells were approximately null regardless of the WHO grade and disease states (SI Fig. 6a–h). Overall, CD163⁺ TAMs were the predominant immune cell populations infiltrating the TME in meningiomas.

Intratumor heterogeneity alters the infiltration of CD163⁺ TAMs

Multiomics is, DNA methylation, whole-exome sequencing, RNA sequencing, and mass spectrometry, yielded molecular classification systems primarily based on gene expression profiles linked to key biological processes, including NF-2 mutations, immunoregulation, lipid and nucleotide metabolism, and cell proliferation [7, 15–17, 37]. To determine DEGs that are potentially associated with immunoregulation in meningiomas, public raw data from cBioportal were re-analyzed [14]. The OncoPrint visualization summarized the mutational landscape of the top 50 most frequently altered genes across 115 samples, with 93.04% (107/115) of the cases harboring at least one genomic alteration. NF-2 (54%) most frequently mutated, whereas SMARCB1 exhibited a mutation frequency of 4.3% but demonstrated notable enrichment in the immunogenic subgroup (31.25%), which were histologically assigned as WHO grade 1 (Fig. 3a). SMARCB1 is a conservative protein coding gene regulating target gene expression through the movement of nucleosomes between enhancers and promoters [38]. SMARCB1 mutation is the core driving factor of advanced malignant tumors, and its missense mutation is associated with poor prognosis of meningiomas [39–41]. However, how SMARCB1 modulates the meningioma TME remains unclear. Thirty-eight samples were screened by IHC staining of SMARCB1 to determine intratumor heterogeneity. Only three samples had regional absence or attenuated expression of SMARCB1, in which one was assigned as WHO grade 1 and the other as grade 2. The last one was assigned as WHO grade 3 because it was combined with homozygous deletion of CDKN2A (SI Fig. 8a). However, whole exon sequencing demonstrated none of the SMARCB1 expression deficiency was caused by genomic missense mutation (data not shown). Double staining of EMA/CD163 or SMARCB1/CD163 showed the effect of SMARCB1 deficiency on the infiltration of CD163⁺ TAMs, which showed that the macrophage density was higher surrounding SMARCB1-deficienct tumor cells compared to SMARCB1-intact cells (Fig. 3b and c; SI Fig. 7a and b). However, in the tumor, except from CD163⁺ TAMs, regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) engage in a crosstalk and mutual reinforcement through cytokines like CSF1, IL-10, and TGFβ, forming an immunosuppressive triad that is a major therapeutic target for cancer immunotherapy [42, 43]. To determine whether Treg and MDSCs were involved in establishing an immunosuppressive TME in meningioma, a multiplex immunofluorescence staining was applied to dissect relative cell density of CD163⁺ TAMs, CD25⁺/Foxp3⁺ Tregs and CD15⁺/CD33⁺ MDSCs. Compared with SMARCB1-intact tumor region, the density of TAMs, Tregs and MDSCs in SMARCB1-deficient tumor region synchronously increased but percentage of CD163⁺ TAMs (9.59%) was remarkably higher than CD25⁺/Foxp3⁺ Tregs (0.04%) and CD15⁺/CD33⁺ MDSCs (3.96%) in SMARCB1-deficient tumor region (SI Fig. 8b). Due to limited sample size, statistical significance was not available in current study. Meningioma with either homozygous deletion of CDKN2A/2B or TERT promoter mutation is directly assigned as WHO grade 3, indicating poor outcomes. To elucidate immunosuppressive microenvironment under this situation, two cases presented with CDKN2A heterozygous deletion and one case presented with both SMARCB1 deficiency and CDKN2A homozygous deletion were included. Multiplex immunofluorescence staining showed that percentage of CD163⁺ TAMs in the case presented with CDKN2A homozygous deletion and SMARCB1 deficiency (12.16%) was slightly increased compared to the cases presented with SMARCB1 deficiency but not CDKN2A homozygous deletion (9.59%). In contrast, percentage of CD15⁺/CD33⁺ MDSCs was slightly decreased from 3.96% to1.98% correspondingly (SI Fig. 8b). However, CDKN2A heterozygous deletion didn’t affect immune cell infiltration. Overall, these results revealed that SMARCB1 deficiency mainly favors the recruitment of CD163⁺ TAMs, contributing to an immunosuppressive TME in meningiomas.

Fig. 3.

CD163⁺TAMs were recruited to the tumor microenvironment surrounding SMARCB1-deficient meningioma cells. a The top 50 most frequently mutated genes in 115 samples based on alteration frequency. b Representative photos showed H&E and IHC staining of SMARCB1, EMA, and CD163 of a WHO grade 2 meningioma case. c Zoom-in image of representative areas showing SMARCB1-intact and SMARCB1-deficient with different densities of infiltrated CD163⁺ TAMs

SMARCB1 deficiency dramatically elevated peritumor cerebral edema

Neuroimaging is not always specific for diagnosis and prognosis prediction for meningioma, but peritumoral cerebral edema (PTCE) might be prominent in certain histological subtypes of meningioma. To determine whether SMARCB1 deficiency and CDKN2A homozygous deletion affect meningioma PTCE, 16 cases across WHO grade 1–3 whose SMARCB1 expression have been screen by IHC were included. The clinical, pathological, and radiological characteristics of 15 patients (one patient was removed due to incomplete MRI images) are shown in Supplementary information Table 1. Two cases (13.3%) presented with SMARCB1 deficiency without CDKN2A deletion. Two cases (13.3%) showed heterozygous CDKN2A deletion without SMARCB1 deficiency. One case (6.7%) demonstrated both SMARCB1 deficiency and CDKN2A deletion. The remaining 11 cases (73.3%) retained intact SMARCB1 expression and showed no CDKN2A deletion. Additionally, all cases were confirmed to have a wild-type TERT promoter by genomic sequencing. The PTCE was found 14 cases (93.3%) regardless of lesion location and histological subtypes. Of note, PTCE was dramatically higher in cases with SMARCB1 deficiency compared to those with intact SMARCB1 expression, approaching a margin of statistical significance (p = 0.06) (SI Fig. 9a and b). The mean EI value was lower in cases with a CDKN2A heterozygous deletion (1.61) than in those without a deletion (3.12). However, the single case presenting both SMARCB1 deficiency and CDKN2A deletion showed the highest EI value (9.11) within this small, discovery-phase cohort (SI Fig. 9a and c).

Gene signature of the TME in meningiomas

In accordance with previous studies, the present study confirmed the abundance of CD163⁺ macrophages in meningioma TME, with only a paucity of T cells, B cells, and neutrophils in the tumor [44, 45]. CSF1 is the primary cytokine controlling monocyte and macrophage differentiation, proliferation, renewal, and function [21]. Targeting CSF1 or CSF1R has been proven as an effective therapeutic strategy for pancreatic cancer, which is also myeloid-prevalent [46]. Besides, the blockage of the CSF1/CSF1R axis by monoclonal antibody abrogated the proliferation of the NF2^-/-Cdkn2ab^-/- meningioma cell line MGS1 inoculated into FVB mice [22]. Therefore, the expression profiles of CSF1/CSF1R and its associated upstream and downstream genes could be potential biomarkers for the prediction and evaluation of meningioma prognosis. Although the source of CSF1 in meningioma was not elucidated, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that CSF1 expression was regulated by IL-17 signaling pathway. Matrix metallopeptidases (MMPs) are downstream genes of the IL-17 signaling pathways, which regulate tumor-associated extracellular remodeling through degrading collagens [47]. Based on the established roles in immune regulation, a panel of 14 genes (e.g., CD163, CSF1, IL-17RA, and their associated genes) was selected as a discriminatory immunogenic signature. This signature effectively distinguished immunogenic tumors from the other three molecular subtypes: NF2 wild-type, hypermetabolic, and proliferative (Fig. 4a). Immune activation scores were further calculated by integrating individual gene signatures into a combined signature, which remarkably distinguished the immunogenic subtype (Fig. 4b). To validate the effectiveness of these 14 gene signatures, 14 samples (13 meningioma samples and 1 dura mater control) across WHO grades and histological subtypes were subjected to bulk-RNA sequencing. Based on the transcription levels of these 14 genes, a combined immune activation score of individuals was calculated for PCA. The two samples harboring deficient expressions of SMARCB1 were clustered into the group with a high-immune score (Fig. 4c). Further K-means cluster analysis obtained a similar result (Fig. 4d), indicating that these 14 genes effectively identify and represent immunogenic meningiomas. However, Pearson’s analysis did not show a significant positive correlation between the immune activation score and the immune infiltration score (Fig. 4e) but demonstrated that the infiltration of CD163⁺ macrophages was significantly and positively correlated with the immune activation score (Fig. 4f), emphasizing that CD163⁺ macrophages are pivotal regulators in meningioma TME.

Fig. 4.

Gene signature of the TME in meningiomas demonstrated the enrichment of immune cells. a and b Violin plots showed the expression levels of 14 genes, including CSF1, CSF1R, CSF2RA, CSF2RB, CSF3R, CD163, IL-17RA, IL-17D, IL-10RB, IFNGR2, MMP2, MMP17, MMP21, and MMP28, successfully clustered meningioma cases into four molecular subgroups, along with a distinctive immune activation score of the immunogenic group. c The immune activation scores calculated by the transcription levels of the 14 genes were used in the principal component analysis to distinguish 14 samples (13 meningioma samples and 1 dura mater control). d K-means clustering of 14 meningioma cases by the transcription levels of the 14 genes. e Pearson correlation analysis of the geneset GSVA score and immune infiltration score. f Pearson correlation analysis of % infiltrated CD163⁺ TAMs and geneset GSVA score

Meningioma cell lines with activated IL-17 signaling pathway showed attenuated SMARCB1 expression

CSF1 is a required growth factor for M2-like macrophages. However, its source within the meningioma has not yet been determined. Single-cell RNA sequencing and bioinformatics analyses predicted that tumor cells expressing sulfotransferase family 1E member 1 could secrete CSF1; however, the molecular mechanism was elusive [48]. The KEGG pathway indicated that CSF1 expression could be modulated by IL-17 signaling. Therefore, we hypothesized that IL-17 promotes CSF1 expression in meningioma tumor cells to drive M2 polarization of macrophages, and SMARCB1 deficiency induces CSF1 secretion, leading to immune-suppressive TME. To test this hypothesis, expression patterns of key regulators were explored in cBioportal. A panel of 14 genes has been validated as an immunogenic meningioma signature, prompting further investigation of their functional associations. Pearson’s analysis revealed that CSF1 expression was significantly and positively correlated with CD163, IL-17RA, NF-κB (RELA), NFκB1, and IFNGR1 (Fig. 5a, b, d, e, and g) but negatively correlated with SMARCB1 (Fig. 5f). The expression of CD163 was also significantly correlated with IL-17RA (Fig. 5c). The immortalized human meningioma cell lines CH-157MN and IOMM-Lee were both derived from high-grade meningiomas. Transcriptomic comparison revealed similar IL-17 receptor expression levels between the two cell lines, whereas SMARCB1 expression was remarkably lower in CH-157MN compared with that in IOMM-Lee (Fig. 5h and i). Conversely, the expression levels of key components of the IL-17 signaling pathway, such as RELA and MAPKs, in CH-157MN were higher than those in IOMM-Lee cells (Fig. 5h). Notably, CSF1 expression in CH-157MN was higher than that in the IOMM-Lee (Fig. 5h). In addition, the expression of several downstream target genes, including C-X-C motif chemokine ligands, C-C motif chemokine ligands, and MMPs, in CH-157MN cells were dramatically higher than that in IOMM-Lee (Fig. 5j and k). These data suggested a possible regulation axis: IL-17 signaling promotes CSF1 secretion by tumor cells, which was enhanced by SMARCB1 deficiency, leading to the immunosuppressive microenvironment in meningioma.

Fig. 5.

Meningioma cell lines with the activated IL-17 signaling pathway showed attenuated SMARCB1 expression. a–g TCGA database was explored to determine Pearson correlation between gene expressions of CD163 and CSF1 (a), CSF1 and IL-17RA (b), CD163 and IL-17RA (c), CSF1 and NF κB(RELA) (d), CSF1 and NFκB1 (e), CSF1 and SMARCB1 (f), and CSF1 and IFNGR1 (g). h–k Single Cell Expression Atlas was accessed to compare the expression levels of SMARCB1, CSF1, and key components of the IL-17 signaling pathway, along with their downstream target genes

Discussion

In this study, multi-dimensional data, including clinical, histopathological, and transcriptomic profiles, of meningiomas spanning WHO grades and disease states were comprehensively integrated. The analyses revealed several critical insights into meningioma biology and tumor–immune interactions: (1) high levels of IL-17A, IL-5, and IL-6 correlated with meningioma progression, suggesting their potential utility as biomarkers for disease monitoring and prognosis evaluation. (2) In meningiomas, the TME was predominantly infiltrated by CD163⁺ M2-like TAMs, with a striking paucity of cytotoxic T lymphocytes, indicative of immune-evasion and immune-suppressive phenotypes. (3) Tumor cells harboring SMARCB1 deficiency exhibit a pronounced recruitment of CD163⁺ M2-like TAMs, implicating SMARCB1 deficiency contributing to shape an immunosuppressive niche. (4) A 14-gene transcriptional signature robustly distinguished an immunogenic meningioma subset, providing a molecular framework for understanding the tumor–immune crosstalk. Notably, our findings proposed a novel mechanistic link wherein IL-17 signaling may modulate CSF1 expression in meningioma cells, potentially regulated by SMARCB1. This pathway could underline the observed immune microenvironment remodeling in meningiomas and warrants further functional validation.

The use of liquid biopsy for meningioma diagnosis and subtyping is an emerging area of interest [2]. High-throughput sequencing approaches have been validated to distinguish meningiomas from radio image mimickers [30]. However, these discoveries required further validation to differentiate low-grade from high-grade meningiomas, and community hospitals cannot access expensive advanced devices. However, ordinary screening of serum biomarkers is an economic method to monitor cancer prognosis. High serum high-sensitive C-reactive protein concentration is associated with poor prognosis in meningiomas [49]. Compared with normal controls, the serum CSF1 level is significantly increased in patients with meningiomas [22]. In triple-negative breast cancer, PD-1 blockade significantly elicited higher serum levels of CSF and IL-17, compared with normal control [50]. These findings indicated that the serum immune profile served as liquid biomarkers for disease monitoring. However, whether the serum levels of cytokines differ in meningiomas across different WHO grades and disease states was not clarified. In this study, the serum levels of a panel of cytokines were recorded 24–48 h before surgery. Kaplan–Meier analysis revealed that the RFS was better with higher levels of IL-17A and IL-5, whereas higher levels of IL-6 supported a poor prognosis. In concordance, co-culture of meningioma tumor cells with M2-polorized macrophages promoted tumor cell proliferation through an IL-6-mediated signaling pathway [45]. Routine clinical profiling of serum cytokines was initiated in July 2022, leading to the short observation time window, ranging from 6 to 30 months. Additionally, WHO grade 1 case accounted for 89.38% of the cohort; therefore, approximately 1/3 of the cases were censored in the survival analysis, resulting in the nonstatistical significance. Further analysis showed a consistently lower cytokine level in WHO grade 1 meningiomas than in grade 2, with IFN-α levels reaching significance. To obtain a more solid conclusion, expanding the study cohort and extending the follow-up time are necessary in future research.

As mentioned in the 5th edition of WHO CNS tumor, the clinical significance of peritumoral cerebral edema (PTCE) in meningioma is potentially profound, which indicates specific histopathological subtype [4]. Prognostically, pronounced PTCE is a radiographic biomarker for more aggressive tumor biology, correlating with higher histopathological grades and mutational burdens [51, 52]. Substantial PTCE is strongly associated with brain invasion, severe complications, and poor clinical outcomes if not managed appropriately even after successful tumor resection [52–54]. Though genomic alteration of SMARCB1 is rare, its relatively high incidence in immunogenic subgroup of molecular classification drove us to determine the relationship between PTCE and SMARCB1. Besides, CDKN2A deletion and TERT promoter mutation independently affect meningioma grading. Herein, our data revealed that SMARCB1-deficiency had positive effect on PTCE volume compared to SMARCB1-intact cases. The one patient in study cohort with CDKN2A homozygous deletion showed a dramatical increase in PTCE volume than no deletion ones. Our electrical medical record showed that approximately all patients underwent operation once suspicious meningioma lesion was detected, but luckily, the patient presented with both SMARCB1-deficiency and CDKN2A homozygous deletion chose to be observed for 29 weeks, wherein the maximum tumor diameter grew from 35 mm to 57 mm with synchronized growth of peritumor volume. Telephone follow-up (6 to 50 months post operation) showed no recurrence after surgery. Though we found out that SMARCB1-deficiency and/or CDKN2A homozygous deletion may contribute to rapid meningioma growth and formation of larger volume PTCE, more data is needed to support this claim.

Immune cell infiltration and cytokine secretion in TME are primary drivers inducing edema in primary tumors, brain metastases, and animal models [55–57]. Our next aim was to determine whether SMARCB1 and CDKN2A status modulate TME of meningioma. TAMs were the predominant immune cells infiltrating meningiomas; while CD4⁺ T cells, CD8⁺ T cells, CD20⁺ B cells, CD38⁺ B cells, neutrophils, and PD-1⁺ lymphocytes were scarce in the TME regardless of the WHO grades and disease states. The infiltration of CD163⁺ TAMs gradually declined along with the increase in the WHO grade, reaching significance when comparing WHO grade 3 malignant meningiomas to WHO grade 1, indicating that anti-cancer immune activity was suppressed in low-grade meningiomas. However, the intratumoral infiltration pattern of M2-like TAMs was uneven, which was attributed to intratumor heterogeneity, genotypes and methylation classes [17, 45]. To determine molecular mechanisms driving intratumor heterogeneity, public transcriptomic data from cBioportal were re-analyzed. Our results demonstrated that SMARCB1 deficiency was pronounced in immunogenic meningiomas. We hypothesized that regional SMARCB1 mutation promoted the infiltration of immune cells, particularly M2-like macrophages. IHC screening of SMARCB1 expression revealed a reginal or entire expression deficiency of SMARCB1 in 3 out of 38 cases (7.89%). Dual IHC staining for EMA/CD163 or SMARCB1/CD163 revealed even greater accumulation of CD163⁺ TAMs surrounding SMARCB1-deficient tumor cells than in those retaining SMARCB1 expression. These findings indicated that the decreased SMARCB1 expression favored an immune-suppressive microenvironment. Apart from CD163⁺ TAMs, regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) are major regulators that could suppress tumor immunity. To determine the exclusively predominant role of CD163⁺ TAMs in suppressive TME of meningioma, multiplex immunofluorescence was applied to reveal that CD25⁺/Foxp3⁺ Tregs and CD15⁺/CD33⁺ MDSCs may have null function due to their extremely low density. However, the underlying molecular mechanisms and immune cell crosstalk regarding remodeling suppressive TME remain elucidated.

Of note, an immunosuppressive landscape is not defined by a single cell type but by a dynamic, interconnected network of multiple hematopoietic and non-hematopoietic cells. Traditionally, TAMs are divided into anti-tumorigenic M1 and pro-tumorigenic M2 functions due to their high plasticity. However, the dynamic activation of TAMs regulated by various cytokines, metabolic cues, and spatial context within the TME [58]. Tregs are highly immunosuppressive CD4⁺ T cells constitutively expressing CD25 and Forkhead Box P3 (Foxp3), which play key roles in preventing autoimmunity and suppressing anti-tumor effects through different mechanisms, such as downregulation of CD80/86, sequestration of IL-2, and secretion of cytokines (such as IL-10, IL-35, and TGF-β) [59]. MDSCs are pathologically activated myeloid cells, broadly categorized into polymorphonuclear (PMN-MDSCs) and monocytic (M-MDSCs) subsets, with potent immunosuppressive capabilities [42]. Constant presence of pro-inflammatory factors favor expansion and accumulation of MDSCs in cancer which might be regulated by signaling pathways (e.g. mTOR, JAK, NF-κB, Notch3) or vascular endothelial growth factor (VEGF) [42, 60, 61]. Besides, accumulation of MDSCs in premetastatic niche promotes cancer metastasis but suppressive MDSCs infiltration is in paucity in meningioma [58, 62]. In tumor microenvironment, TAMs, Tregs, and MDSCs do not operate alone but engage in extensive crosstalk, through soluble factors, metabolic interference, cell-contact mechanisms, creating a self-reinforcing circuit. Elevated IL-10 in MDSCs drives macrophage M2 polarization and downregulates secretion of IL-12 by M2 TAMs [63]. Secretion of IL-10 also affect MHC Ⅱ expression, leading to reduction in antigen presentation potential of macrophages. Furthermoe, in a hypoxia condition, hypoxia induced factor 1 α (HIF1α) promotes M-MDSCs to differentiate into TAMs [64, 65]. In systemic lupus erythematosus, MDSCs interact with TAMs, inhibiting expression of CD40 and secretion of IL-27, leading to high infiltration of TAMs in TME [66]. In the colitis-associated cancer transition, IL-6-induces exosomal miR-93-5p from G-MDSCs to promote differentiation of M-MDSCs into M2 TAMs. This process is driven by chronic IL-6 stimulation via the IL-6/JAK/STAT3 signaling pathway [67]. In colon cancer mouse model, secretion of IL-10 and TGF-ꞵ by MDSCs after stimulation of IFN-γ promoted maturation of Tregs [68]. M2 TAMs also secrete IL-10 and TGF-ꞵ to expand Tregs while impairing cytotoxic T cells mediated by C-C motif chemokine ligand 22 (CCL22) [69]. An immune reconstitution model revealed that a crosstalk between MDSCs and Tregs mediated by CD40/CD40L interactions is essential for Treg expansion [70]. Regulatory B (Breg) cells which can be converted from naïve B cells, supporting immunological tolerance through production of IL-10, IL-35, and TGF- β [71]. Breg cells can induce expansion of MDSCs through TGF-β and drive MDSCs to produce reactive oxygen species (ROS) and nitric oxide (NO) [72]. Co-culture of Breg cells with T cells showed that blockade of TGF-β but not IL-10 prevented differentiation of Foxp3⁺ Tregs [73]. TME also recruits neutrophils into tumors to become tumor-associated neutrophils (TANs), which are subcategorized into anti-tumor N1 TANs and pro-tumor N2 TANs. Hypoxia, high level of ROS, and high hydrogen reprogram TANs to perform as N2 TANs [74]. Physical interaction between TANs and TAMs enhances production of oncostatin M and IL-11 to promote intrahepatic cholangiocarcinoma cell growth via STAT3 signaling [75]. In mouse model of hepatocellular carcinoma, TANs were recruited by C-X-C motif chemokine ligand (CXCL5) secreted by hepatocellular carcinoma cells into TME and subsequently increased migration of TAMs and Tregs through CCL2 and CCL17 [76]. In myeloid-cell-enriched liver tumor, CCL4⁺ TANs favored recruitment of more TAMs while PD-L1⁺ TANs suppress cytotoxic T cell, confirming the pro-tumor phenotypes of TANs [77]. The TME contains a diverse array of immunosuppressive cells that work in concert to inhibit effective immune responses. Except for immunosuppressive cells mentioned above (including TAM, MDSC, Treg, Breg, and TAN), dendritic cells, cancer-associated fibroblasts, and other mesenchymal stromal cells also play significant roles in remodeling tumor TME [42, 58, 59, 69, 72, 78]. Therapeutic strategies aim to block recruitment, differentiation, and communication of immunosuppressive cells to active anti-tumor function. Future studies should employ sophisticated techniques, such as single-cell RNA-seq or multiplexed spatial imaging, to elucidate complexity of immune landscape, and more accurately delineate their roles in meningioma progression and therapeutic responses.

Angiogenesis is a fundamental biological process in hypoxia conditions, which induced expression of hypoxia-inducible factor 1α (HIF1α) to regulate expression of VEGF, playing a critical role under either physiological or pathological conditions [79, 80]. Therapeutic strategy targeting angiogenesis was established since 1971 and targeting VEGF has been become a cornerstone for cancer therapy [80, 81]. In tumor microenvironment, M2 TAMs and MDSCs promote tumor development by secreting VEGF to support tumor angiogenesis and MDSCs also express CXCR2 to promote the formation of pre-metastatic niche [58, 69]. Reversely, VEGF suppress anti-tumor responses through enhancing recruitment of Tregs, polarizing monocytes into M2 TAMs, and impairing natural killer cells [69, 82]. To promote angiogenesis and metastasis, M2 TAMs secrete matrix metalloproteinases (e.g. MMP2 and MMP9) and MMP9 has been well-documented in angiogenesis of meningioma [83, 84]. Meningioma is TAM-enriched and VEGF level was reported to be upregulated in meningioma but prognostic role of angiogenesis in meningioma was controversial which was attributed to lack of conformity in neo-angiogenesis study. High level VEGF might be a negative predictor of poor outcomes [84]. Nevertheless, VEGF blockage is beneficial in treatment of recurrent and refractory meningiomas [13, 85]. Blockage of VEGF is potential strategy to indirectly inhibit pro-tumor capability of M2 TAMs, which are prominent immune cells in meningioma TME. A phase Ⅱ trials demonstrated anti-tumor activity of bevacizumab (a monoclonal antibody against VEFG-A) in recurrent meningioma with a PFS-6 rate of 77% for WHO grade 2 cases and a PFS-6 rate of 46% for WHO grade 3 cases [13]. A retrospective study with median 7.5-year follow-up showed that patients treated with bevacizumab reached a mPFS of 12 month and 1-year overall survival of 64.6% compared to sunitinib (a multikinase inhibitor targeting VEGF) treated patients whose mPFS of 7 month and 1-year overall survival of 52.6% [86]. One patient suffering from multiple recurrent meningiomas was sensitive to sunitinib and genomic analysis discovered GNAS mutation and 1p/22q co-deletion [87]. Due to the immuno-regulatory role of VEGF and potential beneficial molecular alterations, our further research will exam VEGF effect from at least two angles: (1) apply high-throughput technologies to thoroughly inquire transcriptomic and genomics signature of SMARCB1-deficient meningioma. (2) establish organoid model and mouse model of meningioma to determine crosstalk between immunosuppressive cells with/without intervention of VEGF blockages (e.g. bevacizumab, sunitinib, cabozantinib).

The anti-VEGF agent can reverse immunosuppressive TME, making the tumor more susceptible to the reinvigorated immune attack. PD-1 is highly expressed on Tregs, so a combination of VEGF inhibitor and anti-PD-1 immunotherapy may boost the anti-tumor response of effector T lymphocytes. Conditional deletion of PD-1 in tumor-infiltrated Tregs not only impaired proliferation of Tregs but also suppressed tumor progression [88]. In controversial, treatment with anti-PD1 increased Treg cell activation and protected from apoptosis to maintain cell proliferation in hyperprogressive disease and triple-negative breast cancer models [89, 90]. A single arm, open-label phase 2 trial (NCT03279692) demonstrated promising efficacy of pembrolizumab on treating recurrent and progressive high grade meningioma, achieving a PFS-6 rate of 0.48 and a medium PFS of 7.6 months [34]. In a single-arm, single-center, open-label, phase 2 trial, PD-1 inhibitor sintilimab failed to improve PFS-6 in high grade and low grade recurrent/progressive meningiomas [91]. However, the evaluation of combination effect of VEGF inhibitor and PD-1/PD-L1 blockage has not been reported in meningioma. Clinical trials that evaluated combination efficacy of bevacizumab plus anti-PD-1/PD-L1 in lung cancer, renal cell carcinoma, hepatocellular carcinoma demonstrated promising benefits [92–94]. A meta-analysis including 3 phase 3 randomized control trials (RCT) revealed that combination of PD-1/PD-L1 inhibitors, bevacizumab, and chemotherapy significantly improved PFS (HR: 0.76 [0.66, 0.87], P < 0.001) compared to PD-1/PD-L1 inhibitors plus chemotherapy in small cell lung cancer [95]. Future studies are necessary to yield promising and interesting insights into meningioma treatment and the underlying biological facets within meningioma TEM remain elucidated. Furthermore, adoptive transfer of chimeric antigen receptor (CAR) T cells has demonstrated notable success in hematologic malignancies, yet immunosuppressive microenvironment limits efficiency and safety of CAR-T cells [96]. Finaly, M2 TAM-related biomarkers (e.g., CD206 and CD163) are being investigated for their prognostic and predictive utility in immunotherapies which may pave an alternative therapeutic avenue for meningioma [69].

Given the relatively low incidence of SMARCB1 mutation in meningiomas, the development of a rapid and accessible method is valuable. CSF1 is required to regulate macrophage differentiation. However, whether the polarization of macrophages in meningiomas is regulated by IL-17 signaling is unclear. The KEGG pathway database showed that CSF1 expression might be regulated by IL-17 signaling. A 14-gene genomic signature effectively distinguished immunogenic meningiomas from other three molecule groups. Analyses of the transcription levels of these 14 genes in our cohort confirmed their efficiency to identify samples harboring SMARCB1 deficiency, which showed high immune activation scores. These findings raised a potential candidate regulating CSF1 expression. Interestingly, the progression of an immune-suppressive microenvironment in pancreatic adenocarcinoma is mediated by NF-κB and the downstream product CXCL1, which is a downstream target of the IL-17 signaling pathway [97]. Here, we hypothesized that SMARCB1 regulates CSF1 expression mediated by IL-17 signaling in meningioma cells. Gene expression levels of CSF1, IL-17RA, CD163, and NF-κB(RELA) showed significant pairwise positive correlation. However, CSF1 expression was negatively correlated with SMARCB1. The transcription profiles were further compared in two meningioma cell lines: CH157MN and IOMM-Lee. SMARCB1 expression was higher in IOMM-Lee cells than in CH-157MN cells, whereas downstream genes (e.g., CSF1 and MMPs) were more highly expressed in CH-157MN cells. Even though these data were not collected from in vivo or in vitro experiments, to some extent, they confirm that SMARCB1 deficiency promotes an immunosuppressive microenvironment in meningiomas via the IL-17/CSF1 signaling axis. The current findings provide a rationale for future studies to dissect the biological role of SMARCB1 in macrophage polarization and the formation of an immunosuppressive tumor microenvironment. As a next step, these key differentially expressed genes and potential signaling pathways will be validated using larger cohort, as well as through in vivo and in vitro models.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

All authors have made substantial contributions to the conceptualization and performance to the study. B.J., H.H., Y.H.L., and X.T. conceived and designed the study under supervision of M.X.H., analyzed survival data and RNA-seq data with supervision from G.H.H. J.J.X., X.Q.W, and L.Z. performed histology and immunohistochemistry staining. B.J., H.H., and Y.H.L. completed the manuscript and revised by all other authors. M.X.H. and J.X.C. conceived, designed, supervised, and acquired funding for the study.

Funding

This study was supported by the Project of Shanghai Science and Technology Committee of China (No. 18411963000 to M.X.H.) and the National Natural Science Foundation of China (No. 82272715 to J.X.C.).

Data availability

Our bulk-RNA sequencing data was deposited in GEO database (GSE304666) and SRA database (PRJNA1301565) for public use.

Declarations

Ethics approval and consent to participate

All hospitalized patients admitted into Changhai hospital routinely signed a written waiver to articulate their willingness to contribute to research projects.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.