We retrieved clinical and transcriptomic data for glioma patients from the TCGA, CGGA, GEO, and GTEx databases. In total, we amassed 693 samples from the CGGA database, 698 samples from the TCGA database, 63 samples from the GEO database (GSE50161), and 1152 samples from the GTEx database. To be included in our analysis, samples needed to meet specific criteria: 1) Computation of the mean for any duplicated patient sequencing outcomes within the database, and 2) Each selected sample had to include comprehensive clinical information about the patient, including survival time, survival status, age, and gender, among others. Samples lacking any of these crucial clinical details were systematically excluded from the study.

In the initial phase, we conducted an analysis of CLEC7A expression in both gliomas and non-tumor brain tissues across the TCGA, GTEx, and GEO (GSE50161) databases. To delve deeper into the expression correlation between CLEC7A and molecular markers in glioma, we investigated the TCGA and CGGA databases, respectively. These pivotal molecular markers encompassed WHO grade (II, III, and IV), IDH mutation status (mutant or wildtype), 1p19q co-deletion status (codel or no-codel), and MGMT methylation status (methylated or unmethylated). The statistical analysis was executed using R software, and the presentation of results was facilitated by the “ggplot2” package.

Survival outcomes among glioma patients were evaluated employing Kaplan-Meier analysis, incorporating data from diverse sources, including the TCGA and CGGA databases, as well as clinical cases from our hospital. The glioma cohort was stratified based on CLEC7A mRNA expression levels, leading to the identification of two distinct groups: the high expression group (50% to 100% expression) and the low expression group (0% to 50% expression). Statistical analysis and visualization were executed using the “survival” and “survminer” packages, respectively. To enhance clinicians’ ability to predict glioma patient prognosis, we performed a Nomograms analysis in the CGGA and TCGA databases. The Nomograms approach serves as a robust tool for evaluating an individual’s risk by integrating multiple factors in a clinical context. We identified independent risk factors for glioma prognosis and employed them to predict the likelihood of 1, 3, and 5-year overall survival.

In this investigation, we enrolled 119 consecutive patients diagnosed with gliomas through pathological confirmation ( Table 1 ). The cohort comprised individuals admitted to Shandong Provincial Hospital, affiliated with Shandong First Medical University, from February 2008 to February 2020. All participants exhibited a preoperative Karnofsky performance score exceeding 70. The pathological sections of these selected patients underwent re-evaluation and reclassification by two pathologists, adhering to the updated WHO classification (2021) criteria. Ethical clearance for this study was obtained from the Research Ethics Committee of Shandong Provincial Hospital, affiliated with Shandong First Medical University. The research commenced after obtaining written consent from all participants or their legal representatives.

Our team has previously published a comprehensive protocol and scoring criteria for immunohistochemistry and immunofluorescence (10). In this study, the rabbit polyclonal anti-Dectin-1 antibody (Abcam, ab140039) was utilized, with dilutions of 5 µg/ml and 20 µg/ml. Two independent assessors, blinded to patient outcomes, conducted the semi-quantitative analysis.

The criteria for selecting differentially expressed genes (DEGs) in gliomas with high vs. low CLEC7AT expression involved choosing the top 500 genes with the highest coefficient of rank (COR) from both TCGA and CGGA datasets. The selected DEGs exhibited a P-value < 0.05. To explore potential underlying mechanisms, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using DAVID 6.8 (https://david.abcc.ncifcrf.gov/). The study presents the top six results, ordered by ascending P-values (P < 0.05).

The functional enrichment score for each glioma sample was computed utilizing the “GSVA” package with default settings. The gene set related to immune functions was acquired from GSEA (https://www.gsea-msigdb.org/gsea/index.jsp). The categorization of immune functions followed the guidelines outlined by GSEA. The enrichment results were visually represented through a heatmap generated with the “heatmap” package. Pearson correlation analysis was employed to assess the association between CLEC7A expression and multiple immune processes.

To scrutinize the association between CLEC7A and glioma immunity, we assessed immunity scores, stroma scores, ESTIMATE scores, and tumor purity in glioma patients exhibiting high or low CLEC7A expression in both the TCGA and CGGA databases. Employing the capabilities of CIBERSORT, our goal was to discern intricate connections between CLEC7A and the diverse landscape of immune cells infiltrating the glioma microenvironment. This comprehensive analysis played a pivotal role in unraveling potential immunomodulatory roles of CLEC7A in glioma pathogenesis. Additionally, our exploration into the correlation with immune checkpoint markers aimed to provide insights into the potential implications of CLEC7A in modulating immune responses within the context of glioma. These findings contribute significantly to a nuanced understanding of the molecular interplay in glioma, shedding light on potential avenues for therapeutic interventions. This research enhances our knowledge of the complex relationship between CLEC7A expression and glioma immunity, paving the way for further investigations and potential therapeutic strategies.

Through the analysis of single-cell data, we can acquire more intricate information regarding gene alterations. The single-cell data were sourced from the CGGA and GEO databases (GSE70630, GSE84465, GSE89567). Utilizing the Seurat R software package, we reduced cell dimensions and generated a UMAP diagram to visualize cell types. This process involved the streamlining of cell dimensions and the creation of a UMAP diagram for visualizing distinct cell types, employing the Seurat R software package. To investigate the involvement of CLEC7A in tumors, we explored expression variations across a spectrum of cell types.

Total protein was extracted following transfection of cells with siRNA. After BCA quantification, the proteins were denatured by boiling in 4x protein loading buffer, separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), and subsequently transferred to polyvinylidene difluoride membranes (0.45μm pore size, Millipore, Temecula, CA, USA). The membranes were then blocked and incubated overnight at 4°C with the following primary antibodies: CLEC7A (Abcam, ab217331, 1:500), CD163 (Abcam, ab182422, 1:1000), and GAPDH (Abclonal, A19056, 1:5000). The following day, membranes were washed three times for 10 minutes with TBST buffer and then incubated with species-appropriate horseradish peroxidase (HRP)-labeled secondary antibodies (1:5000, Cell Signaling Technology, USA) for 1 hour at room temperature. Finally, the immunoblot bands were visualized using an imaging system (Bio-Rad, Hercules, CA, USA) and quantified using ImageJ software (Version 1.46r, Wayne Raband, USA).

RAW 264.7 cells were initially cultured in DMEM supplemented with 10% FBS. Subsequently, to induce polarization towards M1 macrophages, LPS (200ng/ml) was added to the culture medium, thereby exposing RAW 264.7 cells to pro-inflammatory stimulation. After 24 hours of incubation, IL-4 (10ng/ml) was further added to the medium to promote the transition of M1 macrophages towards the M2 phenotype. Finally, Western blot analysis was conducted to validate the phenotypic changes in the cells. M2 macrophages were transfected with non-silencing scrambled control siRNA (si-NC) and small interfering RNA (siRNA). The siRNAs were designed and synthesized by Hesheng Biotechnology Co., Ltd. (Beijing, China).

To assess whether the absence of CLEC7A impacts the chemotactic capability of macrophages, we conducted a transwell assay. In each well of the upper chamber, 200µl of suspension containing 5×10⁵ RAW 264.7 cells was added. Following polarization of M2 macrophages and siRNA transfection, GL261 mouse glioma cells were seeded into the lower chamber at a density of 1×10⁶ cells per well. The coculture of M2 macrophages and GL261 cells was maintained for 48 hours. The upper chamber was rinsed twice with PBS at 24, 48 hours, respectively. Subsequently, cells from the upper layer were removed using a wet swab. Cells that migrated to the bottom surface were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet.

A multiple group comparison was performed using Tukey’s test. For the comparison of continuous variables between two groups, the Wilcoxon rank-sum test was applied. To assess the linear relationship between two continuous variables, Pearson correlation analysis was conducted. All statistical analyses were performed using R software (version 4.1.3) and GraphPad prism 8. The prognostic value was estimated by univariate and multivariate Cox models using SPSS statistical software (version 25.0). All statistical tests were two sided, and a p-value < 0.05 was considered statistically significant in all analyses.

To explore the involvement of CLEC7A in gliomas, we conducted an analysis of CLEC7A expression levels in both non-tumor brain tissue and glioma samples utilizing data from TCGA, GTEx, and GEO (GEO50161) dataset. The analysis unveiled a notable enrichment of CLEC7A expression in glioma samples ( Figures 1A, B ). Considering the molecular pathological heterogeneity of gliomas, we systematically scrutinized the RNA-seq data of glioma samples in accordance with WHO guidelines, performing a comparative expression analysis on samples from various groups. Within the TCGA database, CLEC7A demonstrated elevated expression in samples of high-grade gliomas, MGMT un-methylated status, IDH wildtype status, and those 1p/19q no-codel status, ( Figures 1C-F ). These findings were corroborated in the CGGA database ( Figures 1G-J ). Furthermore, we conducted immunohistochemical staining to validate the results from the database, utilizing clinical specimens we collected. Illustrative images derived from immunohistochemical staining are depicted in ( Figures 1K, L ). The findings disclosed a heightened expression of CLEC7A in glioma tissues, and its expression escalated with the tumor grade, aligning with the results in the database.

Our investigation delved deeper into the prognostic significance of CLEC7A in glioma. The findings revealed a noteworthy association between elevated CLEC7A expression and considerably poorer OS, as validated by Kaplan-Meier survival analysis utilizing both the TCGA and CGGA database ( Figures 2A, B ). Furthermore, to corroborate these results, we performed validation on patients from our hospital. The result established a significant correlation, demonstrating that high expression of CLEC7A was associated with reduced OS among glioma patients ( Figure 2C ).

Subsequently, we conducted both univariate and multivariate Cox regression analyses using data from the CCGA and TCGA database, revealing CLEC7A expression as an independent risk factor for gliomas ( Table 2 ). This observation was further substantiated through validation in patients from our hospital ( Table 2 ). In the Nomogram prediction model, each patient-specific characteristic corresponds to a different score. Patient 1, 3, and 5-year survival rates were predicted by summing the scores obtained for each glioma patient ( Figures 2D, F ). The agreement between actual and predicted survival rates was substantial, as evidenced by the calibration curves ( Figures 2E, G ).

Transcriptional subtyping has gained widespread acceptance as a pivotal method for molecular classification in gliomas (11). In our examination of the molecular expression profile of CLEC7A, we conducted an analysis of its distribution across distinct molecular subtypes of glioma within the TCGA database. The results highlighted a substantial upregulation of CLEC7A in the mesenchymal subtype compared to the other three subtypes, evident in both the TCGA and CGGA datasets ( Figures 3A, C ). To robustly validate this observation, we utilized a receiver operating characteristic curve analysis for CLEC7A expression and its correlation with the mesenchymal subtype across gliomas of all grades. Notably, the area under the curve (AUC) for CLEC7A expression reached 92.6% and 83.5% for the TCGA and CGGA datasets, respectively ( Figures 3B, D ). These results strongly indicate an overexpression of CLEC7A in mesenchymal subtype gliomas, suggesting a potential oncogenic role in the progression of gliomas. Considering that mesenchymal glioblastomas are recognized for their particular aggressiveness, these findings substantiate our previous identification of the association of CLEC7A with high malignancy in brain tumors.

GO and KEGG enrichment analyses provided valuable insights into the potential biological functions of CLEC7A based on DEG sets in both the TCGA and CGGA databases. In the TCGA database, the GO analysis revealed that CLEC7A is associated with several key biological processes (BP). Notably, it showed a significant involvement in the inflammatory response, immune response, and positive regulation of interleukin-6 production ( Figure 4A ). These findings suggest that CLEC7A may contribute to immune-related processes and inflammatory signaling pathways, which aligns with its potential role in glioma progression. Additionally, the most related cellular components (CC) to CLEC7A are located on the external side of the plasma membrane ( Figure 4B ). This information provides insights into the cellular localization of CLEC7A, which is crucial for understanding its interactions and potential functions in the context of gliomas. The molecular functions (MF) include transmembrane signaling receptor activity and protein binding ( Figure 4C ). This suggests that CLEC7A may function as a signaling receptor on the cell membrane and may be involved in protein-protein interactions, further implicating its role in cellular communication and signaling pathways. The CLEC7A-related signaling pathways identified are Osteoclast differentiation, Neutrophil extracellular trap formation, Staphylococcus aureus infection, B cell receptor signaling pathway ( Figure 4D ). These pathways are often linked to immune response and inflammatory processes, providing additional evidence supporting the involvement of CLEC7A in these critical biological functions. Similar results were obtained in the CGGA database, where the BP, CC, MF, and signaling pathways associated with CLEC7A mirrored those in the TCGA database ( Figures 4E-H ). This consistency across databases strengthens the reliability of the findings and further supports the notion that CLEC7A likely plays a crucial role in immune response and disease regulation.

To further validate the significance of CLEC7A in glioma immune reactions, we conducted GSVA to investigate its association with various immune processes. Utilizing data from the TCGA and CGGA databases, we assessed enrichment scores for immune processes. Our correlation analysis revealed a positive association between CLEC7A expression and several crucial immune processes ( Figures 4I, G ). Glioma patients with high CLEC7A expression exhibited elevated immune, stromal, ESTIMATE scores and lower tumor purity ( Figure 5A ). Furthermore, we examined the correlation between CLEC7A and established inhibitory immune checkpoints—such as PDCD10, CD274, PD-1, PD-2, CD276, LAG3, CTLA-4, IDO1, TNFRSF14, and PDCD1—in different databases ( Figure 6I ). CLEC7A demonstrated a strong positive correlation with these inhibitory immune checkpoints, which contribute to suppressing the immune response in gliomas. To summarize, our findings robustly support the involvement of CLEC7A in multiple aspects of immune responses within the glioma microenvironment.

During the course of tumor immunity, a diverse array of immune cells, comprising T cells, NK cells, macrophages, and others, are mobilized to instigate immune responses with the objective of eradicating cancerous cells (12). To further the immune cells most associated with CLEC7A in glioma tissues, we applied CIBERSORT to classify and analyze the proportion of 22 immune cells retrieved from the TCGA and CGGA databases. Examination of TCGA-derived samples uncovered a notable increase in the proportion of CD4 memory-activated T cells, M1 macrophages, and M2 macrophages within the CLEC7A high-expression cohort of glioma patients ( Figure 5B ). Similar findings were replicated in the CGGA database, demonstrating significantly elevated proportions of CD4 memory-activated T cells and M2 macrophages ( Figure 5C ). To validate the association between CLEC7A expression and macrophages and microglia in gliomas, we analyzed single-cell data from the CGGAS database and the GEO database (GSE70630, GSE84465, GSE89567). The results unequivocally demonstrate that within glioma tissues, CLEC7A is predominantly expressed on tumor-associated macrophages. ( Figures 6A-D ). Consequently, we can infer a strong correlation between tumor-associated macrophages and CLEC7A expression within the glioma immune microenvironment.

Our findings above confirm that CLEC7A is closely associated with macrophages. We integrated data from diverse databases to explore the correlation between CLEC7A and macrophage polarization markers, including AIF1, CD68, IL12A, NOS2, PTGS2, CD163, IL10, CSF1R, IDO1, VSIG4, and STAT6. The analysis shows that there is a significant similarity between the results in the TCGA and CGGA databases ( Figures 6E, F ). The results showed that CLEC7A was found to have a more significant correlation with the markers of M2 macrophages. To verify if CLEC7A is expressed on M2 macrophages, we used immunofluorescence to assess the co-localization of CLEC7A with the established M2 macrophage marker CD163. The results unequivocally revealed a substantial co-localization of CLEC7A with CD163 ( Figure 7A ). In addition, a significant increase in CLEC7A+ CD163+ cells was observed with increasing WHO grade of glioma. In conclusion, our findings strongly suggest that CLEC7A is highly expressed in M2 macrophages in glioma tissues.

To elucidate the specific impact of CLEC7A on macrophages, we conducted correlation analyses to explore the relationship between CLEC7A and genes associated with glioma chemotaxis and polarization in publicly available databases. Our investigation revealed a positive correlation between the expression of CLEC7A and monocyte chemoattractant factors (CCL2, CCL5) as well as factors implicated in M2 macrophage polarization (TGF-β1, CSF-1) in glioma samples obtained from both TCGA and CGGA databases ( Figures 6G, H ). To confirm these findings, we employed si-RNA to knockdown CLEC7A in M2 macrophages ( Figures 7C, E ). Meanwhile, in order to ascertain phenotypic changes during macrophage polarization, we utilized Western blot analysis to determine the expression of CD163 ( Figures 7B, D ). The effectiveness of the knockdown was assessed using Western blot analysis. Subsequently, a Transwell assay was conducted to demonstrate the impact of CLEC7A on the chemotaxis of M2 macrophages towards glioma cells ( Figures 7F, G ). These findings support our hypothesis that CLEC7A may play a role in the chemotaxis and polarization of macrophages in gliomas.