CD36 as a potential prognostic biomarker and modulator of the tumor microenvironment in glioma

Introduction

The annual incidence of glioma is about 4 to 6 per 100,000 per year, which accounts for approximately 80% of all malignant brain and other central nervous system tumors (1,2). In recent years, there have been significant advancements in both the diagnosis and the treatment of glioma, but the prognosis for glioma patients still remains poor, particularly for glioblastoma (GBM) patients, who have a median overall survival (OS) of only about 14–16 months (3). The short survival period of GBM patients is primarily attributed to the tumor recurrence and therapeutic resistance, which largely stems from its high molecular heterogeneity and immunosuppressive tumor microenvironment (TME) (4). Novel therapeutic strategies are urgently needed because of the poor individual prognosis of glioma patients following currently approved anti-tumor therapies (5).

Cluster of differentiation 36 (CD36) is a transmembrane glycoprotein and expresses on tumor cells, microvascular endothelial cells, stromal cells, and immune cells, which exhibits variable expression levels depending on cell type and tumor stage (6,7). As a multi-ligand receptor, CD36 mediates diverse biological processes through interactions with different ligands. It facilitates cellular uptake of fatty acids and participates in lipid metabolism to promote cancer progression (8,9). CD36 in tumor-associated immune cells mediates immunosuppression to foster tumor proliferation and progression (10,11). Notably, CD36 is significantly upregulated in various malignant epithelial tumors and hematological malignancies, promoting tumor progression and therapy resistance (7,12,13). Despite these insights, the specific role of CD36 in glioma remains an area yet to be fully understood.

In this study, we explored the role of CD36 in glioma progression and its prognostic significance. We discovered that the expression level of CD36 mRNA was linked to poor clinical outcome in glioma patients, and CD36 could enhance the proliferation and migration of glioma cell lines (U87, U251, and LN229). Additionally, the present study demonstrated that CD36 affects the immune environment of glioma by promoting the infiltration of macrophages and neutrophils into TME. These findings offer new insights into the relationship between CD36 and glioma, highlighting its potential role in the processes of glioma. We present this article in accordance with the TRIPOD and MDAR reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1974/rc).

Methods

Acquisition and integration of dataset

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. A total of 1,157 normal brain tissues with available RNA-sequence (RNA-seq) data from Genotype-Tissue Expression (GTEx) database were extracted. There were 698 glioma samples from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) with RNA-seq datasets, and corresponding clinical information was downloaded from the University of California, Santa Cruz (UCSC) Xena database (http://xena.ucsc.edu/). In addition, 693 gene expression data and matching clinical information of glioma samples were obtained from the Chinese Glioma Genome Atlas (CGGA) database (https://www.cgga.org.cn/). The RNA-seq data were normalized by using transcripts per million (TPM) or fragments per kilobase of transcript per million mapped reads (FPKM) methods, and samples lacking complete clinical information were excluded. We focused on examining the differences in CD36 expression between cancerous and normal tissues, as well as its variation among glioma patients with different clinical characteristics in both TCGA and CGGA datasets.

Immunohistochemical (IHC) assay

Briefly, the glioma tissue specimens were deparaffinized and dehydrated, followed by antigen retrieval, hydrogen peroxide treatment, and blocking of non-specific antigens. The sections were then incubated with monoclonal rabbit anti-CD36 (1:800, 18836-1-AP, Proteintech, Wuhan, China) overnight at 4 ℃. Finally, the samples were incubated with a secondary antibody and visualized using a diaminobenzidine (DAB) staining kit for signal detection.

Kaplan-Meier survival analysis

All patients were divided into high and low CD36 expression based on median levels. Kaplan-Meier survival analysis was performed to examine the impact of CD36 expression on OS of patients. The log-rank test determined the statistical significance of differences among the groups. We also employed the “timeROC” R package to create time-dependent receiver operating characteristic (ROC) curves, assessing the predictive accuracy of CD36 expression for 1-, 3-, and 5-year OS.

Univariate and multivariate Cox regression analysis

Cox proportional risk regression analysis was performed for CD36 expression, age, pathological grade, IDH status and 1p/19q status. Variables with statistical significance in the univariate analysis (P<0.05) were included in a multivariate Cox regression analysis to assess the independent impact of CD36 on OS. We calculated hazard ratio (HR) and 95% confidence interval (CI), considering P<0.05 as statistically significant.

Differentially expressed genes (DEGs) analysis

DEGs were identified between high and low CD36 expression groups in TCGA database, using the “DESeq2” R package. Up- and down-regulated DEGs with adjusted P value <0.05 and absolute log₂-fold change (FC) >1 were processed for subsequent analyses and visualized using volcano and heatmaps.

CD36-interacting molecules and functional enrichment

We developed a CD36-related gene-gene interaction network by using the GeneMANIA Database (http://www.genemania.org/) and a protein-protein interaction (PPI) network via the STRING Database (https://string-db.org/). For CD36-binding proteins, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, using the “clusterProfiler” package in R for statistical analysis.

Glioma immune cell infiltration analysis

We used the “Estimate” R package to calculate immune, stromal, and estimate scores for glioma samples from TCGA and compared these scores between high and low CD36 expression groups. We determined the composition of 22 immune cell types in the samples by “CIBERSORT” R package, focusing on those with a P<0.05. We then analyzed the relationship between CD36 expression and immune cell distribution in glioma tissues. Additionally, the “GSVA” R package was employed to assess immune cell proportions based on gene expression, further exploring the correlation between CD36 expression and immune cell infiltration in the glioma microenvironment.

Cell culture and RNA interference

U87and LN229 glioma cell lines were acquired from the Cell Resource Center, Institute of Basic Medical Sciences (China) and U251 cell line was acquired from the OriCell (China). These cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM, Gibco, Grand Island, NY, USA) containing 10% fetal bovine serum (FBS, Gibco, USA) and 1% penicillin and 1% streptomycin (10378016, Invitrogen, Carlsbad, CA, USA), maintained at 37 ℃ and 5% CO₂. Negative control (NC) and CD36 small interfering RNA (siRNA) were transfected into these cell lines using Lipofectamine^TM 3000 (L3000015, Invitrogen, USA) according to the manufacturer’s recommended protocol. The siRNA products are obtained from GENOMEDITECH (Shanghai, China). The siRNA sequences were as follows: nontargeting, siRNA sense: 5'-UUCUCCGAACGUGUCACGUTT-3', antisense: 5'-ACGUGACACGUUCGGAGAATT-3'; CD36, siRNA sense: 5'-GGCUGUGUUUGGAGGUAUUCU-3', antisense: 5'-AGAAUACCUCCAAACACAGCC-3'.

RNA extraction and quantitative real-time polymerase chain reaction (qRT-PCR)

Total RNA was extracted from U87, U251, and LN229 cells using a kit (RC112-01, Vazyme Biotech, Nanjing, China), and its concentration and purity were measured by NanoDrop (Thermo Fisher Scientific, Waltham, MA, USA). Then Evo M-MLV RT Mix Kit (AG11705, Accurate Biotechnology, Hunan, China) was used to reversely transcribed total RNA of all samples. Quantitative mRNA expression was determined following qRT-PCR using SYBR Green Premix Pro Taq HS qPCR Kit (AG11701, Accurate Biotechnology, China). The primer sequences were listed in Table S1. The Ct values of each gene were corrected by Ct reading of corresponding β-actin. The PCR reactions were performed using Light Cycler 480 (Roche, Basel, Switzerland).

Western blot assay

Total proteins were extracted using RIPA Lysis Buffer (Beyotime Biotechnology, Shanghai, China) with protease/phosphatase inhibitors (Solarbio, Beijing, China) and quantified by a bicinchoninic acid (BCA) assay (Beyotime Biotechnology, China). Proteins, alongside a molecular weight marker, were loaded on a 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel and transferred to polyvinylidene difluoride (PVDF) membranes, which were then blocked with 5% skimmed milk. The membranes were incubated with primary antibodies against CD36 (18836-1-AP, 1:1,000, Proteintech, China) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH; 10494-1-AP, 1:10,000, Proteintech, China), followed by secondary antibody (anti-rabbit IgG, SA00001-2, Proteintech, China). After washing, protein detection was done using an electrochemiluminescence (ECL) reagent and a chemiluminescence detection system (Amersham Imager 680, GE Healthcare, Chicago, USA).

Colony formation assay

Cells (1.0×10⁴ cells per well) were grown in 6-well plates at 37 ℃ with 5% CO₂ for several days until colonies formed. These colonies were then fixed with 4% paraformaldehyde and stained with crystal violet for counting and analysis.

Cell Counting Kit-8 (CCK-8) assay

Cell proliferation was determined using CCK-8 assay (HY-K0301, MedChemExpress, Monmouth Junction, NJ, USA). Cells were seeded into 96-well plates at a density of 3,000 cells per well and incubated with CCK-8 solution at various time points (0, 24, 48 and 72 hours). Cell viability was determined by measuring the absorbance at 450 nm using a microplate reader.

EdU proliferation assay

Cells were seeded in 96-well plates at 1.5×10⁴ cells/well and incubated for 24 hours. After incubation with 50 µM EdU (RiboBio, Guangzhou, China) for 2 hours, they were fixed with 4% paraformaldehyde and stained with Apollo solution and Hoechst33342. The proliferation index was determined as the ratio of EdU-positive cells to total cells, with images captured using a confocal microscope from multiple random fields.

Wound healing and migration assays

We evaluated cell migration by wound healing and transwell assays (Corning Incorporated, Corning, NY, USA). For wound healing, 1×10⁶ cells per well were seeded in 6-well plates, scratched with a sterile tip (200 µL), and observed at 0, 12, 24, and 48 hours after culturing in serum-free medium. In transwell assay, 4×10⁴ cells per well were placed in the upper chamber with serum-free medium and migrated cells to the lower chamber were fixed, stained, and counted after 24 hours.

Statistical analysis

R software (version 3.6.3/4.1.2) was used for statistical analysis and two-tailed P<0.05 was considered as significant. The “ggplot2” R package was used for visualizations. Differences between two groups were analyzed with the Wilcoxon rank-sum test, while the Kruskal-Wallis test was used for multiple group comparisons. Spearman’s correlation coefficient assessed the relationship between CD36 expression and immune cells/genes. Experimental data were presented as “mean ± standard deviation” (SD) by using the t-test for analysis. All experiments were repeated no less than 3 times. The statistical significance was described as follows: *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; and ns, not significant.

Results

High expression of CD36 is associated with worse clinicopathological features in glioma patients

The TCGA pan-cancer analysis revealed varying CD36 expression levels across cancer types (Figure 1A). Specifically, higher CD36 expression was noted in GBM and low-grade glioma (LGG) compared with the normal brain tissues (Figure 1B). Moreover, the results of IHC staining indicated that CD36 expression increased with glioma grade advancement (Figure 1C). To assess the correlation between CD36 expression and clinicopathological features in glioma, we analyzed CD36 expression in the TCGA and CGGA databases, stratifying glioma patients into various groups. Firstly, glioma patients were divided into a high age (≥60 years), and a low age (<60 years) group. Our work indicated that CD36 mRNA level was significantly higher in the high-age group (Figure 1D). Secondly, according to the World Health Organization (WHO) grade of glioma, the expression level of CD36 mRNA in WHO IV grade patients was significantly higher than WHO II or III grade. In addition, this significance was not found between WHO II grade and WHO III grade patients (Figure 1E). These results indicated that CD36 mRNA level might be a marker of high-grade glioma in patients over 60 years old. Furthermore, considering the emerging significance of isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status as glioma biomarkers, our data showed higher CD36 mRNA expression in glioma patients with IDH wildtype and 1p/19q non-codeletion than those with IDH mutation and 1p/19q codeletion (Figure 1F,1G). We also found that the CD36 expression of recurrent glioma was significantly increased in comparison with primary glioma in CGGA dataset (Figure 1H). Over all, these results underscored that CD36 upregulation is associated with glioma malignancy.

Figure 1 CD36 was increased in glioma and its high expression was associated with malignant clinicopathological features in TCGA and CGGA databases. (A) The expression of CD36 in pan-carcinoma in TCGA database and corresponding normal tissues in GTEx database. (B) The expression of CD36 in TCGA glioma was higher than normal brain tissues in GTEx database. (C) Representative immunohistochemistry staining images of CD36 in glioma (magnification 20×). (D) The expression of CD36 of glioma patients in high age group (>60 years) was higher than the low (≤60 years). (E) The expression of CD36 of glioma patients with WHO IV was higher than WHO II and WHO III. (F) The expression of CD36 of glioma patients with IDH wildtype was higher than IDH mutation. (G) The expression of CD36 of glioma patients with 1p/19q non-codeletion was higher than 1p/19q codeletion. (H) The expression of CD36 in recurrent glioma patients was higher than primary glioma patients. *, P<0.05; **, P<0.01; ***, P<0.001; ns, not significant. CGGA, Chinese Glioma Genome Atlas; GTEx, Genotype-Tissue Expression; IDH, isocitrate dehydrogenase; TCGA, The Cancer Genome Atlas; WHO, World Health Organization.

Higher CD36 mRNA expression predicts poorer prognosis in glioma patients

To confirm whether CD36 affect the clinical prognosis of glioma patients, we performed Kaplan-Meier survival analysis using data from the TCGA and CGGA databases. Consistent findings from both databases indicated that higher CD36 expression correlates with worse prognosis in glioma patients (Figure 2A). In addition, we evaluated prognostic prediction accuracy by calculated the area under the ROC curve (AUC). The results showed that the AUC values were 0.688 for 1-year OS and 0.689 for both 3- and 5-year OS in the TCGA database. Similarly, in the CGGA database, CD36 demonstrated notable predictive accuracy, with AUC values of 0.623 for 1-year OS, 0.667 for 3-year OS, and 0.669 for 5-year OS (Figure 2B). These findings suggested that upregulation of CD36 in glioma patients might be a biomarker for a less favorable prognosis.

Figure 2 CD36 affected the prognosis of glioma patients. (A) Glioma patients with low expression of CD36 had longer OS compared with high expression of CD36 in both TCGA and CGGA databases. (B) Time-dependent ROC curves based on the CD36 expression of glioma exhibit the ability to predict 1-, 3-, and 5-year OS in TCGA and CGGA databases. (C) Forest maps of the univariate and multivariate Cox regression analyses in patients with glioma from TCGA and CGGA datasets. (D) The nomogram was developed by integrating the CD36 with age, WHO grade, IDH status and 1p/19q status of glioma in CGGA database. (E) Calibration curves of 1-, 3-, and 5-year OS prediction in glioma patients in CGGA database. (F) ROC curves based on the nomogram exhibit the ability to predict 1-, 3-, and 5-year OS in patients with glioma from CGGA database. AUC, area under the curve; CGGA, Chinese Glioma Genome Atlas; CI, confidence interval; HR, hazard ratio; IDH, isocitrate dehydrogenase; OS, overall survival; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; WHO, World Health Organization.

Owing to the high expression of CD36 potentially represent poorer glioma patients’ outcomes, we further assessed whether CD36 could be an independent risk factor for glioma prognosis. We conducted univariate and multivariate Cox regression analyses, including age, WHO grade, IDH mutation status, 1p/19q codeletion status, and expression of CD36 of glioma patients. The results showed that age, WHO IV grade, IDH wildtype, 1p/19q non-codeletion, and high expression of CD36 are independent risk factors of mortality in glioma patients (Figure 2C). Next, nomogram with these independent risk factors based on CGGA database was established to predict 1-, 3- and 5-year survival probabilities of glioma patients (Figure 2D). The nomogram predicated survival probabilities closely matched the ideal reference line according to the calibration plot (Figure 2E). Furthermore, we measured nomogram risk scores for each glioma patient and tested its predictive efficacy and the nomogram had strong performance in predicting OS in glioma patients (Figure 2F). In summary, CD36 appeared to be an independent prognostic marker for patients with glioma.

CD36 promotes the ability of proliferation and migration of glioma cells in vitro

Firstly, we detected CD36 expression in U87, U251, and LN229 cell lines post-transient transfection for CD36 knockdown and this was quantified by qRT-PCR (Figure 3A) and Western blot (Figure 3B) assays. Then CCK-8 (Figure 3C), colony formation (Figure 3D) and EdU (Figure 3E) assays showed that knockdown of CD36 significantly reduced the proliferation of these cells. Moreover, wound healing (Figure 3F) and transwell migration (Figure 3G) assays confirmed that CD36 knockdown also diminished the migratory capacity of U87, U251, and LN229 cell lines. Collectively, these findings indicate that CD36 plays a crucial role in promoting the proliferation and migration of glioma cells in vitro.

Figure 3 CD36 promoted proliferation and migration of glioma cells in vitro. (A) The mRNA level of CD36 was reduced in U87, U251 and LN229 cells transfected with CD36 siRNA using qRT-PCR assay. (B) The protein level of CD36 was decreased in U87, U251 and LN229 cells transfected with CD36 siRNA by western blot analysis. (C) Knockdown CD36 of U87, U251 and LN229 cells inhibited proliferation ability using CCK-8 assay. (D) Representative images of the colony formation experiment displayed knockdown CD36 in U87 cells decreased capability of proliferation by crystal voilet staining. (E) Representative images and quantification as shown by EdU staining. CD36 knockdown with siRNA of U87, U251 and LN229 cells reduced ability of proliferation compared with control (NC). (F) Knockdown CD36 of U251 and LN229 cell lines inhibited the ability of migration compared with control (NC) by wound healing experiment. (G) CD36 siRNA-transfected U251 and LN229 cells inhibited migration capability compared with control (NC), which were analyzed using the transwell assay. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. NC, negative control; qRT-PCR, quantitative real-time polymerase chain reaction.

CD36 plays an impact on the immune microenvironment of glioma

To further investigate the role of CD36 in glioma, we screened 1,791 DEGs between high and low expression of CD36 in glioma patients based on TCGA database, including 1,308 up-regulated and 483 down-regulated genes (Figure 4A,4B). Then, GO and KEGG analyses were conducted on these DEGs. The GO analysis highlighted pathways like leukocyte migration, extracellular matrix (ECM) organization, cell chemotaxis, myeloid leukocyte migration and humoral immune response (Figure 4C). Moreover, the KEGG analysis indicated that CD36 was involved in pathways like cytokine-cytokine receptor interaction, phagosome, chemokine signaling pathway, IL-17 signaling pathway and ECM-receptor interaction (Figure 4D). To gain in-depth insights into the role of CD36, top 20 CD36-related genes (Figure 4E) and proteins (Figure 4F) were focused on subsequent functional enrichment analysis. The results emphasized pathways such as transforming growth factor β (TGF-β) signaling pathway, myeloid cell differentiation, phagocytosis, focal adhesion and ECM-receptor interaction (Figure 4G,4H). It is well known that the TME, consisting of tumor cells, immune-infiltrating cells, the ECM, adjacent blood vessels, fibroblasts, and various soluble factors (14). In view of above functional enrichment results, CD36 was mainly involved in regulating immune cell migration and differentiation, ECM, as well as cytokine and chemokine signaling pathways. Thus, we speculated that CD36 was involved in modulating the immune microenvironment of glioma.

Figure 4 Functional enrichment of CD36. (A) The volcano plot of 1,791 DEGs based on TCGA database. blue, down-regulated genes; red, up-regulated genes; grey, genes without significant differences. (B) Heatmap showed the top 10 upregulated and downregulated DEGs in the TCGA glioma tissues. (C) Enriched GO terms in CC, BP, and MF categorized for the DEGs. (D) KEGG pathways were analyzed for all the DEGs. (E) Top 20 CD36-related genes in the GeneMANIA database. (F) Top 20 CD36-related proteins in the STRING online database. (G) Bubble plot of GO enrichment analysis of CD36-related signal pathway. (H) Bubble plot of KEGG enrichment analysis of CD36-related signal pathway. Different sizes denote the number of genes, while different colors denote different significance. **, P<0.01; ***, P<0.001. BP, biological process; CC, cellular component; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

Upregulation of CD36 leads to increased immune infiltration of macrophages and neutrophils in glioma TME

Building on the established role of CD36 in regulating TME in glioma, this study further investigated whether CD36 is responsible for immune cells infiltration in glioma TME. The ESTIMATE algorithm was used to infer the infiltration of stromal and immune cells of glioma. It was showed that increased expression of CD36 was positively associated with higher immune, stromal, and ESTIMATE scores in glioma patients (Figure 5A). Specifically, the CIBERSORT algorithm was employed to evaluate the infiltration levels of different types of immune cells. It was revealed a significant increase in macrophages (M0, M1) and neutrophils in the group with high expression of CD36 (Figure 5B). Although the variation in M2-macrophages between high and low CD36 expression groups was not pronounced, the high group still exhibited a greater infiltration of M2-macrophages (Figure 5B). Further, the ssGSEA method was used to assess 29 immune components in glioma and showed a significant enrichment of macrophages and neutrophils in the high CD36 expression group (Figure 5C,5D). Additionally, a correlation analysis revealed a positive association of CD36 with the infiltration of macrophages and neutrophils (Figure 5E). In summary, these results suggested that increased CD36 expression is correlated with a higher infiltration of macrophages and neutrophils in the glioma TME. Meanwhile, macrophages, neutrophils and other myeloid cells exhibit immunosuppressive functions, implying that high expression of CD36 may be indicative of an immunosuppressive TME of glioma.

Figure 5 CD36 was associated with immune cell infiltration in TME of glioma. (A) High expression of CD36 exhibited higher stromal score, immune score, and ESTIMATE score compared with the low expression group. (B) Violin plot revealed the differential proportion of tumor-infiltrating immune cells in the glioma TME between the high and low expression of CD36 analyzed using CIBERSORT algorithm. Red bars, the group of CD36 high expression; blue bars, the group of CD36 low expression. (C) Heatmap showed the use of ssGSEA score to calculate 29 immune components between different CD36 expression groups. (D) Comparison of the immune cells between high and low expression of CD36 via ssGSEA score. (E) Correlation analysis between CD36 expression level and ssGSEA score of immune cells. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; ns, not significant. NK, natural killer; ssGSEA, single sample gene set enrichment analysis; TME, tumor microenvironment.

CD36 facilitates the migration and infiltration of macrophages and neutrophils into the TME of glioma

Our study suggested that CD36 is crucial for leukocyte migration and significantly increases myeloid immune cell infiltration in glioma TME, indicating that CD36 may enhance the migration and infiltration of peripheral myeloid immune cells into the TME. To begin with, we observed that CD36 is positively correlated with the expression of chemokines of monocytes (such as CCL2 and CCL5) and neutrophils (such as CXCL6 and CXCL8) in glioma (Figure 6A). Following this, we evaluated the mRNA expression of these chemokines in U87, U251, and LN229 cells and found a significant decrease in their levels after CD36 knockdown (Figure 6B). In the TME, infiltrating macrophages and neutrophils can be polarized into different subtypes under the influence of cytokines. Macrophages are classified into two major subgroups: M1 (anti-tumor phenotype or classically activated) and M2 (pro-tumor phenotype or alternatively activated) macrophages (15). Similarly, neutrophils can be categorized as N1 (anti-tumor phenotype) and N2 (pro-tumor phenotype) neutrophils (16). Correlation analysis further revealed that CD36 is positively associated with markers of M0 and M2 macrophages but exhibited a weak correlation with markers of M1 macrophage (Figure 6C). Similarly, CD36 is positively correlated with the expression of typical N2 neutrophil markers, while showing very weak or no correlation with N1 neutrophil markers (Figure 6D). Moreover, we also observed a significant decrease in M2-macrophage polarization factors (TGF-β1, CSF-1) and N2-neutrophil polarization factors (CSF-2, CSF-3) following CD36 knockdown in glioma cell lines (Figure 6B). These findings supported that the increased CD36 mRNA expression is closely associated with the migration and infiltration of myeloid cells and promotes the polarization of macrophages and neutrophils toward a pro-tumor phenotype.

Figure 6 The expression of CD36 in glioma was correlated with the infiltration of macrophages and neutrophils. (A) High expression of CD36 in glioma was positively associated with the recruitment of macrophages and neutrophils, but not with the infiltration of microglia. (B) The qRT-PCR experiment demonstrated changes in the chemokines and cytokines secreted by U87, U251 and LN229 cells after knockdown of CD36 in U87, U251 and LN229 cells. (C) High expression of CD36 in glioma was associated positively with classical markers of M0- and M2-macrophages and was associated weakly with classical markers of M1-macrophages. (D) High expression of CD36 in glioma was positively correlated with classical markers of N2-neutrophils and weakened with classical markers of N1-neutrophils. (E) High expression of CD36 was correlated more strongly with classical markers of BMDM-TAMs compared with classical markers of microglia-TAMs. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; ns, not significant. BMDM, bone marrow-derived monocytes; CGGA, Chinese Glioma Genome Atlas; NC, negative control; qRT-PCR, quantitative real-time polymerase chain reaction; TAM, tumor-associated macrophages; TCGA, The Cancer Genome Atlas.

Notably, macrophages in the glioma TME can be genetically classified into two major categories: embryo-derived, tissue-resident microglia and tissue-infiltrating bone marrow-derived monocytes (BMDMs) (17). Although both microglia and macrophages in the TME can be referred to as tumor-associated macrophages (TAMs), which play distinct roles in GBM (18). CX3CL1 is considered the important chemokine that directs microglial migration into the glioma TME (19). Correlation analysis showed that the association between CD36 and CX3CL1 is weak or negative (Figure 6A). Furthermore, qRT-PCR revealed that CX3CL1 expression in CD36-knockdown cells did not change compared with the control group (Figure 6B). Additionally, several studies concluded that microglia-derived TAMs and BMDM-derived TAMs possess distinct marker profiles (20,21). Correlation analysis indicated that CD36 expression is significantly positively correlated with typical BMDM-TAM markers, while its correlation with microglia-TAM markers is not significant (Figure 6E). Therefore, CD36 specifically promotes the immune infiltration of BMDM-TAMs but not microglia-TAMs. In conclusion, CD36 plays a crucial role in recruiting myeloid macrophages and neutrophils into the glioma TME, while also driving their polarization toward a pro-tumor phenotype, thereby reshaping TME.

Discussion

Glioma, accounting for a significant majority of primary brain tumor-related deaths, presents formidable challenges in treatment due to their inherent heterogeneity and the presence of the blood-brain barrier. Recently, increasingly researches highlight the potential value in discovering new biomarkers for glioma (22). In this context, CD36 previously identified as highly expressed in various cancers and linked to poorer prognosis, emerges as a point of interest. However, its prognostic significance in glioma, along with its role in tumor progression, had not been extensively explored prior to this study. Our current study revealed a positive correlation between overexpression of CD36 and the worsening prognosis in glioma. Notably, decreasing CD36 expression markedly inhibited the proliferation and migration of glioma cells. These findings suggested that CD36 may serve as a potential therapeutic target to hinder tumor progression. This study thereby sheds new light on the molecular underpinnings of glioma and opens up potential pathways for more effective treatment strategies.

The study highlighted CD36 is primary involvement in regulating immune microenvironment of glioma. While recent studies have focused on CD36 is related to lipid metabolism abnormalities in immune cells (11), its immunoregulatory function in tumor cells remains less understood. The study aimed to elucidate the potential immunomodulatory function of CD36 in glioma. The TME is considered a key factor in the development of primary and secondary brain malignancies. The brain TME exhibits highly immunosuppressive properties. Infiltrating immune cells in brain tumors regulate tumor cell invasion, progression, and immune evasion, thereby contributing to malignant phenotypes of tumor cells (23). Compared to other tumors, the glioma TME harbors relatively fewer infiltrating lymphocytes, suggesting that glioma is immunologically “cold” tumor (24). Another hallmark of glioma is the abundant infiltration of myeloid cells, primarily macrophages and neutrophils, which are important contributors to its immunosuppressive TME of glioma (25,26). During tumor development, glioma evade host immune surveillance by recruiting immune cells and subverting their native functions, effectively converting these cells into tumor accomplices. Previous research on immunotherapy for glioma has predominantly focused on T cells, while other immunosuppressive cell populations within the TME remain poorly understood. Emerging evidence suggests that targeting non-T cell populations of the TME, such as macrophages and neutrophils, may enhance the effectiveness of immunotherapy (27). Investigating how these bone marrow-derived cells migrate to the TME and undergo functional reprogramming may uncover novel biomarkers that regulate immune cell infiltration, providing important insights for developing strategies to counteract the immunosuppressive TME. The findings of this study revealed that CD36 upregulation promotes the migration of bone marrow-derived macrophages and neutrophils into the glioma TME while polarizing these cells toward immunosuppressive phenotypes, thereby contributing to the remodeling of the glioma TME.

Understanding the potential mechanisms by which CD36 affects TAMs and tumor-associated neutrophils (TANs) could provide valuable insights into targeting suppressive immune cells in glioma. Further research revealed that CD36 is primarily involved in regulating the TGF-β signaling pathway, a critical element in tumorigenesis, progression, and immunosuppression within the TME (28). There was evidence suggested that TGF-β signaling inhibited transcription factor nuclear factor-kappa B (NF-κB)-mediated anti-inflammatory responses, and acquisition of the M2 phenotype in TAMs correlated with this inhibition of NF-κB activity (29). Additionally, TGF-β induced TANs with a pro-tumorigenic phenotype, blocking it increased the attraction of neutrophils to chemokines and leaded to the recruitment and activation of TANs with an anti-tumorigenic phenotype (16). In the context of the TGF-β signaling pathway, immune infiltrating macrophages and neutrophils can transform into M2-macrophages and N2-neutrophils, respectively, both of which support tumor growth. This study hypothesized that CD36 may be involved in the regulation of macrophages and neutrophils through the TGF-β signaling pathway, contributing to the formation of an immunosuppressive TME in glioma. This hypothesis offers a new perspective on the role of CD36 in glioma, suggesting that it may not only be a marker of tumor progression but also a potential target for therapy, especially in strategies aimed at remodeling the TME to enhance the effectiveness of immunotherapeutic approaches.

The present study acknowledged certain limitations that are important for a comprehensive understanding and for guiding future studies. Firstly, the analysis of CD36 on the survival of glioma patients primarily relied on public databases. To address this limitation, we have initiated the creation of a specialized clinical database for glioma. Another limitation of our work is the absence of in vivo validation using animal models. While in vitro findings suggested that CD36 may play a role in the infiltration of macrophages and neutrophils into the glioma TME, the complexity and dynamics of the TME in a living organism necessitate further validation in an in vivo setting. Addressing these limitations through the development of a dedicated clinical database and the incorporation of in vivo studies will significantly enhance the understanding of CD36’s role in glioma progression and patient prognosis.

Conclusions

In conclusion, this study demonstrated that CD36 is not only a biomarker indicative of glioma of progression and prognosis, but also plays an important role in the recruitment and polarization of TAMs and TANs. This underscores the potential of CD36 in regulating the infiltration of immune cells within the TME. By highlighting the ability of CD36 to alter the dynamics of tumor-associated immune cells, the study provides a novel perspective on TME remodeling. This is particularly relevant in the context of therapeutic strategies, where targeting the TME can be as critical as targeting the tumor cells themselves. Furthermore, the study provides a theoretical foundation for considering CD36 as a therapeutic target in tumor treatment.

Acknowledgments

The authors would like to thank the TCGA and CGGA databases.

Footnote

Reporting Checklist: The authors have completed the TRIPOD and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1974/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1974/prf

Funding: The present study was supported by Gansu Province Health Industry Scientific Research Project of China (GSWSKY2023-04) and Cuiying Scientific and Technological Innovation Program of the Second Hospital & Clinical Medical School, Lanzhou University (CY2023 MS-A06).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1974/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Miller KD, Ostrom QT, Kruchko C, et al. Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin 2021;71:381-406. [Crossref] [PubMed]
2. Pinson H, Silversmit G, Vanhauwaert D, et al. Epidemiology and survival of adult-type diffuse glioma in Belgium during the molecular era. Neuro Oncol 2024;26:191-202. [Crossref] [PubMed]
3. Stupp R, Taillibert S, Kanner A, et al. Effect of Tumor-Treating Fields Plus Maintenance Temozolomide vs Maintenance Temozolomide Alone on Survival in Patients With Glioblastoma: A Randomized Clinical Trial. JAMA 2017;318:2306-16. [Crossref] [PubMed]
4. Bikfalvi A, da Costa CA, Avril T, et al. Challenges in glioblastoma research: focus on the tumor microenvironment. Trends Cancer 2023;9:9-27. [Crossref] [PubMed]
5. Rong L, Li N, Zhang Z. Emerging therapies for glioblastoma: current state and future directions. J Exp Clin Cancer Res 2022;41:142. [Crossref] [PubMed]
6. Wang J, Li Y. CD36 tango in cancer: signaling pathways and functions. Theranostics 2019;9:4893-908. [Crossref] [PubMed]
7. Zhou X, Su M, Lu J, et al. CD36: The Bridge between Lipids and Tumors. Molecules 2024;29:531. [Crossref] [PubMed]
8. Liu YY, Huang WL, Wang ST, et al. CD36 inhibition enhances the anti-proliferative effects of PI3K inhibitors in PTEN-loss anti-HER2 resistant breast cancer cells. Cancer Metab 2025;13:6. [Crossref] [PubMed]
9. Chen J, Yu X, Yang G, et al. Combined Blockade of Lipid Uptake and Synthesis by CD36 Inhibitor and SCD1 siRNA Is Beneficial for the Treatment of Refractory Prostate Cancer. Adv Sci (Weinh) 2025;12:e2412244. [Crossref] [PubMed]
10. Liao X, Yan S, Li J, et al. CD36 and Its Role in Regulating the Tumor Microenvironment. Curr Oncol 2022;29:8133-45. [Crossref] [PubMed]
11. Xu Z, Kuhlmann-Hogan A, Xu S, et al. Scavenger Receptor CD36 in Tumor-Associated Macrophages Promotes Cancer Progression by Dampening Type-I IFN Signaling. Cancer Res 2025;85:462-76. [Crossref] [PubMed]
12. Lambrescu IM, Gaina GF, Ceafalan LC, et al. Inside anticancer therapy resistance and metastasis. Focus on CD36. J Cancer 2024;15:1675-86. [Crossref] [PubMed]
13. Yue N, Jin Q, Li C, et al. CD36: a promising therapeutic target in hematologic tumors. Leuk Lymphoma 2024;65:1749-65. [Crossref] [PubMed]
14. Jang HJ, Park JW. Microenvironmental Drivers of Glioma Progression. Int J Mol Sci 2025;26:2108. [Crossref] [PubMed]
15. Mantovani A, Sozzani S, Locati M, et al. Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol 2002;23:549-55. [Crossref] [PubMed]
16. Fridlender ZG, Sun J, Kim S, et al. Polarization of tumor-associated neutrophil phenotype by TGF-beta: "N1" versus "N2" TAN. Cancer Cell 2009;16:183-94. [Crossref] [PubMed]
17. Zhao W, Zhang Z, Xie M, et al. Exploring tumor-associated macrophages in glioblastoma: from diversity to therapy. NPJ Precis Oncol 2025;9:126. [Crossref] [PubMed]
18. Brandenburg S, Blank A, Bungert AD, et al. Distinction of Microglia and Macrophages in Glioblastoma: Close Relatives, Different Tasks? Int J Mol Sci 2020;22:194. [Crossref] [PubMed]
19. Gutmann DH, Kettenmann H. Microglia/Brain Macrophages as Central Drivers of Brain Tumor Pathobiology. Neuron 2019;104:442-9. [Crossref] [PubMed]
20. Friebel E, Kapolou K, Unger S, et al. Single-Cell Mapping of Human Brain Cancer Reveals Tumor-Specific Instruction of Tissue-Invading Leukocytes. Cell 2020;181:1626-1642.e20. [Crossref] [PubMed]
21. Pombo Antunes AR, Scheyltjens I, Lodi F, et al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. Nat Neurosci 2021;24:595-610. [Crossref] [PubMed]
22. Śledzińska P, Bebyn MG, Furtak J, et al. Prognostic and Predictive Biomarkers in Gliomas. Int J Mol Sci 2021;22:10373. [Crossref] [PubMed]
23. Guo M, Yuan Z, Jin X, et al. Inhibition of ICAM1 diminishes stemness and enhances antitumor immunity in glioblastoma via β-catenin/PD-L1 signaling. Nat Commun 2025;16:8642. [Crossref] [PubMed]
24. Segura-Collar B, Cómitre-Mariano B, López DA, et al. The TRIB2-DNMT1 Pathway Generates an Immune-Cold Microenvironment in Glioblastoma, and Its Inhibition Promotes Immunotherapy. Cancer Immunol Res 2025;13:1022-36. [Crossref] [PubMed]
25. Akay F, Saleh M. Rational therapeutic targeting of myeloid cells in glioblastoma: challenges and perspectives. Front Immunol 2025;16:1472710. [Crossref] [PubMed]
26. Andersen BM, Faust Akl C, Wheeler MA, et al. Glial and myeloid heterogeneity in the brain tumour microenvironment. Nat Rev Cancer 2021;21:786-802. [Crossref] [PubMed]
27. Xu S, Tang L, Li X, et al. Immunotherapy for glioma: Current management and future application. Cancer Lett 2020;476:1-12. [Crossref] [PubMed]
28. Kricha A, Bouchmaa N, Ben Mkaddem S, et al. Glioblastoma-associated macrophages: A key target in overcoming glioblastoma therapeutic resistance. Cytokine Growth Factor Rev 2024;80:97-108. [Crossref] [PubMed]
29. Porta C, Rimoldi M, Raes G, et al. Tolerance and M2 (alternative) macrophage polarization are related processes orchestrated by p50 nuclear factor kappaB. Proc Natl Acad Sci U S A 2009;106:14978-83. [Crossref] [PubMed]