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. 2025 Nov 6;25:1723. doi: 10.1186/s12885-025-15195-5

Identification and validation of cuproptosis-related immune checkpoint expression for glioblastoma

Jinhui Huang 1,#, Shoufang Tong 2,#, Jin Liu 3,#, Shushan Hua 2, Jie Zhang 1, Rajneesh Mungur 4, Shuai Liu 1, Jiugeng Feng 1, Bin Tang 1, Jianping Zeng 1,✉
PMCID: PMC12590761  PMID: 41199254

Abstract

Objective

Investigating the expression levels of immune checkpoint genes CD276, CD40, TNFSF14, and TNFSF9 in glioblastoma multiforme patients about copper death and patient prognosis.

Methods

Transcriptional data of GBM patients were obtained from the TCGA database. LASSO Cox regression analysis was performed to identify four immune checkpoint genes (CD276, CD40, TNFSF14, and TNFSF9) associated with copper death in glioblastoma. Bioinformatics analysis was conducted to explore the correlation between the key copper death protein FDX1 and immune checkpoints. The Kaplan-Meier analysis was conducted to assess the prognosis between groups with high and low expression levels.

Results

CD276, CD40, and TNFSF14 were significantly upregulated in glioblastoma compared to adjacent normal tissues (P < 0.05), while TNFSF9 was significantly downregulated (P < 0.05). Patients with high expression of CD276, CD40, and TNFSF14 showed significantly poorer prognosis compared to those with low expression (P < 0.05). There was no statistical difference in prognosis between high and low expression groups of TNFSF9 (P > 0.05). Knockdown of CD40, CD276, and TNFSF14 significantly reduced cell viability in the A172 cell line compared to the control group (P < 0.05), while overexpression of TNFSF9 significantly reduced cell viability (P < 0.05). Similar results were observed in the U251 cell line (P < 0.05) after knockdown of CD40, CD276, and TNFSF14, as well as overexpression of TNFSF9.

Conclusion

The expression levels of immune checkpoint genes CD276, CD40, and TNFSF14 related to copper death are positively correlated with the prognosis of GBM patients, while the expression level of TNFSF9 is negatively correlated with the prognosis of GBM patients. This study establishes a novel link between immune checkpoint genes and cuproptosis, bridging copper-induced cell death mechanisms with immunotherapy for glioma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-025-15195-5.

Keywords: Cuproptosis, Glioblastoma, Immune checkpoints, Prognosis

Introduction

Glioblastoma multiforme (GBM) is a highly malignant brain tumor that is difficult to cure, often characterized by infiltrative growth and local invasion [1]. Despite years of clinical practice, the current treatment options for GBM are limited, and multimodal comprehensive treatments including surgery, radiation therapy, chemotherapy, and targeted therapy are employed. However, these treatment modalities have significant limitations, necessitating the development of new treatment strategies and efficacy standards to improve outcomes for GBM patients [2]. Therefore, it is necessary to identify new therapeutic targets and explore the underlying pathological mechanisms. Tumor immunotherapy has become a hot topic in current cancer treatment, with the potential to surpass traditional cancer treatment methods [3]. In the treatment of infiltrative malignant tumors such as GBM, immune cell therapy has already been introduced in modern medical practice. Treatment methods such as cytokine therapy, immune suppressants, and recombinant proteins have shown some efficacy. However, to fully utilize the effectiveness of tumor immunotherapy, a deeper understanding of immune cell function and the mechanisms involved in disease progression is essential [4].

Cuproptosis‑related immune checkpoint genes (CRICGs), Cuproptosis is a form of iron-dependent programmed cell death that occurs in response to copper overload [5]. Recent studies have identified some immune checkpoint genes (ICGs) that are involved in cuproptosis, and we define it as CRICGs [6–8]. These genes play a crucial role in regulating the immune response to cuproptosis-induced tissue damage and have been implicated in various diseases, including cancer and neurodegeneration [9, 10]. Understanding the function of CRICGs may provide insights into the mechanisms underlying these diseases and may lead to the development of novel therapeutic strategies.

This study first investigated the correlation between the expression levels of immune checkpoint genes CD40, CD276, TNFSF14, TNFSF9, and copper death. Then, it explored how immune checkpoint molecules affect the growth and invasive abilities of GBM through the copper death pathway, further influencing the prognosis of GBM patients.

Materials and methods

Filtering the expression matrix to identify CRICGs and ICGs in glioblastoma, and then Constructing an interrelation diagram between cuproptosis and immune checkpoints in glioblastoma

In this study, a total of 36 CRICGs were obtained from the Cancer Genome Atlas (TCGA) public database. mRNA expression patterns were standardized using the appropriate R package, and differentially expressed CRICGs were identified using the limma package in R, based on criteria with |logFC| >1 and a false discovery rate (FDR) < 0.05. Gene expression profiles were adjusted using the scaling method within the “limma” R package. The linkages between copper-induced cell death in glioblastoma and immune checkpoint genes were captured using the R programming language. Subsequently, a protein-protein interaction (PPI) network data file was constructed using Cytoscape software version 3.9.0.

Construction and validation of a prognostic model based on CRICGs

In this study, univariate Cox regression analysis was conducted to screen the prognostic value of the CRICGs. Subsequently, a prognostic risk model was built using Lasso-penalized Cox regression analysis via the glmnet R package. The risk scores of GBM patients were then computed. Patients with GBM were stratified into high-risk and low-risk groups based on the median risk score. The prognoses of the two groups were evaluated using Kaplan-Meier curves, and time-dependent ROC analysis was employed to assess the prognostic capability of the risk model evaluated through the survival ROC package. Lastly, a GEO dataset was utilized as a validation set to further corroborate the prognostic performance of this model.

Nomogram establishment based on risk score and clinical variables

We investigated the relationship between features based on CRICGs and clinical variables to explore whether the risk scores have independent prognostic value in GBM patients. Additionally, clinical variables and the risk scores based on CRICGs features were utilized to establish a nomogram associated with outcomes for evaluating the 1-year and 2-year survival probabilities of GBM patients.

Functional enrichment analyses and protein‑protein interaction (PPI)

In this study, we conducted GO analysis using the ClusterProfiler package and performed KEGG pathway analysis using the same method. Significantly enriched FDR and p < 0.05 were identified. Differentially expressed CRICGs were submitted to the STRING database (http://www.string-db.org/) to retrieve protein-protein interaction information. The PPI network was constructed and visualized using Cytoscape software. We utilized the MCODE plugin to select the most significant module of the PPI network based on MCODE scores > 10.

Gene Set Enrichment Analysis (GSEA)

In order to delve deeper into the molecular mechanisms that distinguish the low-risk and high-risk groups, we embarked on a GSEA analysis. Significance was attributed to P values below 0.05 and an FDR below 25%.

Internal validation of CRICGs signature

In this study, the training, testing, and overall cohort of GBM patients were stratified into high-risk and low-risk populations based on the median value of the training risk score. Kaplan-Meier (K-M) survival analyses were performed using the R package Survival to assess OS values, as well as stratified subgroups based on different clinicopathological characteristics. The independent variables of the risk model were sequentially evaluated through Cox regression analysis. Furthermore, ROC curves from the R package timeROC24 were employed to ascertain the efficacy of the prognostic model. Lastly, the predictive capability of this prognostic model was assessed.

Cell culture

The human glioblastoma cell lines A172, LN229, U251, U87MG, and the normal control cell line HA1800 were obtained from Dr. G. F. Vande Woude (Van Andel Research Institute, Grand Rapids, MI, USA). Cells were cultured as adherent monolayers in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum (FBS) and 1% penicillin-streptomycin at 37 °C in a humidified 5% CO₂ incubator. For routine maintenance, cells were monitored daily and passaged at 70–90% confluence using 0.25% trypsin-EDTA dissociation. Following trypsin neutralization with complete medium, cells were centrifuged (200 × g, 5 min), resuspended in fresh medium, and subcultured at 1:3–1:6 splitting ratios into new tissue culture-treated flasks. Culture medium was replaced every 2–3 days, and cells were routinely tested for mycoplasma contamination.

Quantitative real-time PCR

Total RNA was isolated using TRIzol reagent (Invitrogen, USA) following manufacturer’s protocol, with RNA concentration and purity (A₂₆₀/A₂₈₀ ≥ 1.8) verified using NanoDrop spectrophotometry. cDNA was synthesized from 1 µg RNA using PrimeScript RT Master Mix (Takara, China) under standard conditions: 37 °C for 15 min followed by 85 °C for 5 s inactivation. qPCR reactions were performed in 20 µL volumes containing SYBR Premix Ex Taq II (Takara), 0.4 µM primers, and 2 µL cDNA template on a QuantStudio 6 Pro system (Applied Biosystems), using the following thermal profile: initial denaturation at 95 °C for 30 s; 40 cycles of 95 °C for 5 s and 60 °C for 30 s; followed by melt curve analysis (95 °C for 15 s, 60 °C for 60 s, incremental increase to 95 °C). All samples were run in technical triplicates with no-template controls, and relative gene expression was calculated via the 2^(-ΔΔCt) method using GAPDH for normalization; primer sequences are listed in Table 1.

Table 1.

The primer sequences

Primer name Sequence(5’ to 3’)
GAPDH-F GTCTCCTCTGACTTCAACAGCG
GAPDH-R ACCACCCTGTTGCTGTAGCCAA
CD276-F CTGGCTTTCGTGTGCTGGAGAA
CD276-R GCTGTCAGAGTGTTTCAGAGGC
CD40-F CCTGTTTGCCATCCTCTTGGTG
CD40-R AGCAGTGTTGGAGCCAGGAAGA
TNFSF14-F GGTCTCTTGCTGTTGCTGATGG
TNFSF14-R TTGACCTCGTGAGACCTTCGCT

TNFSF9-F

TNFSF9-R

GCCTCTTGGACCTGCGGCAG

CGTGTCCTCTTTGTAGCTCAGG
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Western blot assay

Protein concentration in the supernatant obtained after cell lysis was quantified using a BCA protein assay kit (Beyotime Institute of Biotechnology). Equal amounts of total cellular protein lysate (50 µg per lane) were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and subsequently electrophoretically transferred onto polyvinylidene fluoride (PVDF) membranes (Millipore, Billerica, MA, USA). The membranes were then blocked for 1 h at room temperature with 5% (w/v) non-fat dry milk or 5% bovine serum albumin (BSA) dissolved in Tris-buffered saline containing 0.1% Tween-20 (TBST) to prevent non-specific binding. Following blocking, the membranes were incubated overnight at 4 °C with the appropriate primary antibodies diluted in blocking buffer (1:1000 dilution). The next day, membranes were washed three times for 10 min each with TBST to remove unbound primary antibody. Subsequently, the membranes were incubated for 1 h at room temperature with horseradish peroxidase (HRP)-conjugated secondary antibodies (anti-rabbit or anti-mouse IgG, as required; Bio-Rad), also diluted in blocking buffer (1:5000 dilution). Following incubation, membranes were washed three times for 5 min each with TBST to remove unbound secondary antibody. Target protein immunoreactivity was detected using enhanced chemiluminescence (ECL) reagent (Amersham Biosciences, Piscataway, NJ, USA) and visualized using a ChemiDoc imaging system (Bio-Rad). Protein band densitometry was performed for quantification using the Quantity One software (Bio-Rad). GAPDH served as the loading control for normalization.

To knock down or overexpress immune checkpoint genes and assess transfection efficiency

To knock down CD276, CD40, and TNFSF14, and overexpress TNFSF9, siRNA transfection was performed in U251 and A172 cell lines. For siRNA transfection, cells at 60–70% confluence were transfected with 50 nM siRNA complexed with Lipofectamine RNAiMAX transfection reagent (Invitrogen) in Opti-MEM reduced-serum medium, non-targeting scrambled siRNA and mock transfection (transfection reagent only) served as negative controls. Cells were harvested 48 h post-transfection for efficiency assessment. Transfection efficiency was quantified at both mRNA and protein levels using RT-qPCR (with primers listed in Table 1) and Western blotting (as described in Sect. 2.9), respectively, with normalization to untreated controls.

Statistics analysis

The statistical analyses for this study were carried out utilizing R software version 4.0.5. Data were presented as mean ± standard error of the mean (SEM). Shapiro–Wilk tests were used to assess data distributions for normality, and variance equality between groups was assessed using Levene’s test. For the data, two groups were compared with an unpaired t-test; multiple groups were compared using one-way or two-way repeated-measures (RM) ANOVA. Statistical significance was determined as follows: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Results

Identifying CRICGs and the relationship between the expression levels of CRICGs and the copper death key protein

The transcriptome data of glioblastoma multiforme patients were obtained from the TCGA database, and bioinformatics analysis revealed four CRICGS including CD276, CD40, TNFSF14, and TNFSF9 in glioblastoma (Fig. 1A, B and G). By comparing the expression of immune checkpoints CD276, CD40, TNFSF14, and TNFSF9 in tumor tissues and adjacent normal tissues, the results showed that the expression levels of CD276, CD40, and TNFSF14 were significantly higher in glioblastoma than in adjacent normal tissues (P < 0.05) (Fig. 1C, D and F), while TNFSF9 was significantly lower in glioblastoma than in adjacent normal tissues (P < 0.05) (Fig. 1E). We further analyzed the correlation between immune checkpoint expression levels and the copper death key protein FDX1 in glioblastoma cells. The results showed a significant positive correlation between FDX1 expression levels and CD276, CD40, and TNFSF14 (P < 0.05), while there was a significant negative correlation with TNFSF9 expression level (P < 0.05) (Fig. 1H). This indicates that CD276, CD40, and TNFSF14 are positively correlated with the cuproptosis process, whereas TNFSF9 exhibits an inverse correlation.

Fig. 1.

Fig. 1

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A and B Selecting copper death-related immune checkpoint genes through TCGA. C, D, E, and F This figure shows the differential expression of CD276, CD40, TNFSF14, and TNFSF9 checkpoint genes between normal tissues and GBM. G Graphs obtained from univariate analysis. Red color, HR > 1 represents a risk factor, indicating poor patient prognosis. Through LASSO Cox regression analysis, four significant genes were identified. H It indicates the correlation of CD276, CD40, TNFSF14, and TNFSF9 checkpoint genes with the copper death key protein FDX1

The independent prognostic value of the CRICGS signature and nomogram construction

We executed univariable and multivariable Cox analyses to testify whether this signature could be an independent prognostic indicator. Univariate analysis showed that risk score and age were significantly relevant to the survival of GBM patients (p < 0.05) (Fig. 2A). Multivariate analysis indicated that the risk score and age were still remarkably related to prognosis (p < 0.05) (Fig. 2B). These results demonstrated that CRICGS-based signature was an independent prognostic indicator for GBM patients. A nomogram was developed to predict the survival probability of GBM patients, which included sex, risk score, and age as prognostic indicators. The nomogram was able to predict the 1- and 2-year survival probability of patients with GBM through graphical representation (Fig. 2C). The accuracy of the nomogram was confirmed by the calibration curve, which indicated that the predicted survival was consistent with the actual outcome (Fig. 2D).

Fig. 2.

Fig. 2

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A Riskscore and age were found to be significantly associated with the survival of GBM patients in univariate analysis (p < 0.05). B In multivariate analysis, it was revealed that the riskscore and age remained significantly correlated with prognosis (p < 0.05). C The nomogram predicts the 1-year and 2-year survival probabilities of patients with glioblastoma. D The calibration curve validates that the predicted survival outcomes are consistent with the actual results

Association between the signature and clinical characteristics

The Chi-square test was used to evaluate the association between the CRICGs and the development and progression of GBM. The results (Fig. 3A) indicated that the high- and low-risk groups exhibited no significant differences were observed in age (p = 0.176) and gender (p = 0.427) (Fig. 3B). Subsequently, stratification analysis was performed to evaluate the prognostic significance of the signature in subgroups. The study found that the CRICGs-based signature exhibited excellent performance in predicting outcomes in specific subgroups such as age < = 60 (p = 0.005) (Fig. 3C2), and showed poor performance in female (p = 0.097) (Fig. 3C4), male (p = 0.053) (Fig. 3C3), and age > 60 (p = 0.338) (Fig. 3C1).

Fig. 3.

Fig. 3

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A Chi-square test evaluates the association between the development and progression of CRICGs and GBM. B There were no significant differences in age (p = 0.176) and gender (p = 0.427) between the high-risk group and the low-risk group. C 1-4 The signature based on CRICGs exhibited excellent performance in predicting outcomes in specific subgroups such as age < = 60 (p = 0.005), but showed poor performance in females (0.097), males (0.053), and age > 60 (p = 0.338)

Functional enrichment analyses, Protein-Protein Interaction (PPI), and GSEA

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to investigate the potential functions of the differentially expressed CRICGs. The results of biological process analyses showed that 36 CRICGs were remarkably involved in the regulation of T cell activation, regulation of leukocyte cell-cell adhesion, leukocyte cell-cell adhesion, and positive regulation of leukocyte activation. Cellular component analysis suggested that the external side of the plasma membrane, MHC class Ⅱ protein complex, and MHC protein complex. Molecular function analysis indicated that 36 CRICGs were majorly located in antigen binding, MHC class Ⅱ protein complex binding, and MHC protein complex binding (Fig. 4A). In the KEGG pathways, the results indicated that these genes were mainly involved in the cell adhesion molecules, allograft rejection, intestinal immune network for IgA production, autoimmune thyroid disease, viral myocarditis, and systemic lupus erythematosus (Fig. 4B). To further elucidate the molecular mechanisms of CRICGS-based signature, GSEA analysis was performed. The results of GSEA analysis showed that pathways such as Gap junction, Leishmaniasis, Neutrophil extracellular trap formation, Nucleotide metabolism, Porphyrin metabolism, Regulation of lipolysis in adipocytes, Viral protein interaction with cytokine and cytokine receptor, and Wnt signaling pathway were also enriched in the high-risk group. Conversely, metabolic pathways such as Focal adhesion, Renin-angiotensin system, and Proteoglycans in cancer were primarily enriched in the low-risk group. These findings suggest the involvement of CRICGs in multiple cellular processes and pathways, providing a better understanding of the underlying mechanisms of the CRICGS-based signature (Fig. 4C).

Fig. 4.

Fig. 4

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A The GO analysis hints that these 36 CRICGs are mainly located at the positions of antigen binding, MHC class II protein complex binding, and MHC protein complex binding. B In KEGG pathways, these genes are key players in cell adhesion molecules, graft rejection, intestinal IgA production, autoimmune thyroid diseases, viral myocarditis, and systemic lupus erythematosus. C The results of the GSEA analysis reveal that pathways such as Gap junction, Leishmaniasis, Neutrophil extracellular trap formation, Nucleotide metabolism, Porphyrin metabolism, Regulation of lipolysis in adipocytes, Viral protein interaction with cytokine and cytokine receptor, and the Wnt signaling pathway show enrichment in the high-risk group. Conversely, pathways including Focal adhesion, Renin-angiotensin system, and Proteoglycans in cancer are predominantly enriched in the low-risk group

The expression levels of CRICGs and the impact of expression levels on patient prognosis

Further evaluation was conducted to investigate the relationship between the expression levels of immune checkpoints CD276, CD40, TNFSF14andTNFSF9, and the prognosis (overall survival, OS) of patients in glioblastoma tissue. Patients were grouped based on the median expression levels of CD276, CD40, TNFSF14, and TNFSF9, and Kaplan-Meier curves were plotted to compare the survival periods. The results showed that patients with high expression of CD276, CD40, and TNFSF14 had significantly worse prognosis compared to those with low expression (P < 0.05) (Fig. 5A, B and D). There was no statistically significant difference in prognosis between patients with high expression of TNFSF9 and those with low expression (P > 0.05) (Fig. 5C).

Fig. 5.

Fig. 5

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A, B, and D Patients with high expression of CD276, CD40, and TNFSF14 exhibited significantly worse prognosis compared to those with low expression (P < 0.05). C There was no statistically significant difference in prognosis between patients with high expression of TNFSF9 and those with low expression (P > 0.05). (G1: High expression group of corresponding immune checkpoints, G2: Low expression group of corresponding immune checkpoints)

Analysis of differential gene expression under different expression levels of immune checkpoints

Based on the median expression levels of CD276, CD40, TNFSF14, and TNFSF9, patients were divided into high-expression and low-expression groups. Differential gene analysis and functional enrichment analysis were performed using transcriptomic data, as shown in the figure below (Fig. 6).

Fig. 6.

Fig. 6

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Differential gene analysis based on the different expression levels of immune checkpoints CD276, CD40, TNFSF14, and TNFSF9

Immunohistochemistry was performed to detect the expression of immune checkpoints in tumor and adjacent normal tissues

By obtaining human adjacent normal tissue and human glioblastoma tissue through clinical means, the expression of immune checkpoints CD276, CD40, TNFSF14, and TNFSF9 was detected using immunohistochemistry (Fig. 7A). Semi-quantitative analysis results showed that the expression levels of CD276, CD40, and TNFSF14 were significantly higher in glioblastoma compared to adjacent normal tissue (P < 0.01) (Fig. 7B, C and E), while the expression level of TNFSF9 was significantly lower in glioblastoma compared to adjacent normal tissue (P < 0.01) (Fig. 7D).

Fig. 7.

Fig. 7

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A Obtaining human adjacent normal tissues and human glioblastoma tissues through clinical means. B, C, and E The expression levels of CD276, CD40, and TNFSF14 in glioblastoma tissues are significantly higher than those in adjacent normal tissues (P < 0.01, unpaired t-test). D The expression level of TNFSF9 in glioblastoma tissues is significantly lower than that in adjacent normal tissues (P < 0.01, unpaired t-test)

The expression of immune checkpoints in different cell lines was examined using Western blot analysis

Various human glioblastoma cell lines (A172, LN229, U251, U87MG) and a normal human astrocyte cell line (HEB) were cultured in vitro. The expression of immune checkpoints in different cell lines was examined using Western blot analysis (Fig. 8A). Semi-quantitative analysis revealed that the expression levels of CD276, CD40, and TNFSF14 were significantly higher in glioblastoma cell lines compared to the control group (P < 0.05) (Fig. 8B, C and E). The expression level of TNFSF9 was significantly lower in glioblastoma cell lines (A172, LN229, U251) compared to the adjacent control group (P < 0.05) (Fig. 8D), while the expression level of TNFSF9 in the glioblastoma cell line U87MG showed no statistically significant difference compared to the control group (P > 0.05) (Fig. 8D).

Fig. 8.

Fig. 8

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A Culturing various human glioblastoma cell lines (A172, LN229, U251, U87MG) as well as normal human astrocyte cell line (HEB). B, C, and E The expression levels of CD276, CD40, and TNFSF14 in glioblastoma cell lines are significantly higher compared to the control group (P < 0.05, Dunnett-t test). D The expression levels of TNFSF9 in glioblastoma cell lines (A172, LN229, U251) are significantly lower than in the adjacent control group (P < 0.05, Dunnett-t test), while the expression levels of TNFSF9 in the glioblastoma cell line U87MG show no statistically significant difference compared to the control group (P > 0.05)

Interference with the expression of immune checkpoints

SiRNA transfection was used to knock down CD276, CD40, and TNFSF14 in U251 and A172 cell lines, while TNFSF9 was overexpressed. Transfection efficiency was assessed using RT-PCR and Western blot techniques. The results showed that after knocking down CD276, CD40, and TNFSF14, the expression levels of these proteins in the cell lines were significantly reduced compared to the control group (P < 0.05). Conversely, overexpression of TNFSF9 significantly increased its expression level in the cell lines compared to the control group (P < 0.05) (Supplementary Figure S1).

CCK-8 assay detects changes in cell viability

The changes in the activity of U251 and A172 glioblastoma cell lines after different interferences were detected using CCK-8 assay. The results revealed that in the A172 cell line, knocking down CD40, CD276, and TNFSF14 significantly reduced cell viability compared to the control group, with statistical significance (P < 0.05). Overexpression of TNFSF9 also significantly decreased cell viability compared to the control group, with statistical significance (P < 0.05). In the U251 cell line, knocking down CD40, CD276, and TNFSF14 significantly reduced cell viability compared to the control group, with statistical significance (P < 0.05). Similarly, overexpression of TNFSF9 significantly decreased cell viability compared to the control group, with statistical significance (P < 0.05) (Supplementary Figure S2).

Discussion

The copper death pathway, as a novel cell death pathway, has demonstrated significant importance in cancer therapy [11]. This pathway is formed within cells due to the combination of copper ions with molecules such as oxygen to form cytotoxic species [8, 12, 13]. Copper ions disrupt cell metabolism through interactions with various molecules, leading to cell destruction and apoptosis. A large amount of research indicates that the levels of copper in tumor tissues and serum of a series of cancers are significantly elevated, such as melanoma [14], breast cancer [15], prostate cancer [16], liver cancer [17], lung cancer [18], and gastric cancer [19]. Compared to other traditional apoptosis pathways, the copper death pathway is more widely present in various types of malignant tumors and can be applied in the treatment and intervention of tumors. Therefore, the copper death pathway is considered a novel strategy for treating [20].

Immune checkpoints are a group of immune molecules that are activated to suppress self-immune attacks to prevent the occurrence of autoimmune diseases [21]. However, in certain cases, cancer cells can exploit the immune checkpoint mechanism to evade attacks from the immune system and survive. When PD-1 binds to its ligand PD-L1, it inhibits the immune response of T cells, allowing tumor cells to escape immune surveillance, thereby promoting tumor growth and invasion [22]. Other immune checkpoints such as CTLA-4, and LAG-3, among others, can also inhibit T cell activity through different mechanisms to regulate immune responses [23]. Therefore, targeting immune checkpoints has become an increasingly popular cancer treatment modality and plays a crucial role in the development of cancer immunotherapy. PD-1 and CTLA-4 are two of the most extensively studied immune checkpoint molecules [24]. However, there are few comprehensive analyses of CRICGs and discussions on their clinical significance in GBM.

Some studies have shown that the ligands of PD-1, CTLA-4, and other immune checkpoints can affect the concentration of copper ions, thereby influencing the activity of the copper death pathway, which in turn affects the growth and death of cells [25]. In malignant tumors, the expression level of PD-L1 is significantly increased, which can inhibit the activity of T cells and can also increase the invasion ability of tumor cells by regulating CD38 and DMT1 to affect the copper death pathway [8]. Additionally, other immune checkpoint molecules such as TNFRSF18 and ITGAE have been reported to affect the growth and invasion ability of tumors through the copper death pathway [26]. This provides an important avenue for regulating the copper death pathway through targeting immune checkpoints and creates favorable conditions for further exploring the mechanism of immune checkpoint regulation of the copper death pathway. CD276 can serve as an immune checkpoint molecule by inhibiting T cell activity, thereby helping tumor cells evade immune surveillance and attack [27]. Overexpression of CD276 is closely related to the growth, infiltration, and metastasis of tumors [27, 28]. Therefore, studying inhibitors of CD276 is of significant importance for the treatment of tumors. Currently, inhibitors targeting CD276 mainly include antibodies and small molecular compounds [29]. For example, the immunoglobulin G1 gene recombinant protein (CON332) is a monoclonal antibody specific to CD276, which can prevent the binding of CD276 to B7-1 on T cells and stimulate mature T cells, thereby enhancing the immune-mediated killing effect on tumor cells [30]. Furthermore, a small molecule inhibitor 1A8 can inhibit the overexpression of CD276, thereby reducing embryonic development and inflammatory reactions [31]. CD40 is a member of the immunoglobulin family, widely expressed on the surface of various cells, with important immune regulatory and anti-tumor effects [32]. CD40 is expressed in various tumor cells, including melanoma, breast cancer, lymphoma, non-small cell lung cancer, etc [33]. CD40 agonists and CD40L inhibitors. CD40 agonists primarily activate immune cells by simulating the interaction between CD40L and CD40, showing remarkable efficacy in treating various tumors [34]. For example, it has been confirmed that CD40 agonists have shown good clinical and experimental therapeutic effects in patients and animal models with melanoma and other tumors [35, 36]. TNFSF14 is a member of the tumor necrosis factor (TNF) family, widely expressed on the surface of various human cells, including tumor cells [37]. Recent studies have shown that TNFSF14 is highly expressed in various tumor cells, such as gastrointestinal tumors, prostate cancer, and pancreatic cancer [38]. A small molecule inhibitor SGS-1 targeting TNFSF14 is undergoing preclinical research, as it can inhibit TNFSF14-induced cell apoptosis and alterations in the cell cycle, thereby slowing down tumor progression [39]. Additionally, research has also found that some monoclonal antibodies can selectively target TNFSF14 to inhibit its immune regulatory and cell apoptosis capabilities [40]. TNFSF9, by binding to its receptor 4-1BB, can enhance the activity of T cells and natural killer cells, promoting apoptosis and clearance of tumor cells [41]. Recent studies have indicated that TNFSF9 is underexpressed in various tumor cells, such as liver cancer, pancreatic cancer, and colorectal cancer [42]. The low expression of TNFSF9 is associated with the malignant growth and metastasis of tumors. There are already some studies on TNFSF9 inhibitors [43]. TNFSF9 inhibitors can inhibit the immune regulatory and anti-tumor immune response of tumor cells by blocking the interaction between TNFSF9 and 4-1BB [44].

In Fig. 3, the reduced model efficacy in females/elderly patients may reflect sex/age-specific TME differences, necessitating cohort-specific adjustments in future clinical translation.

In this study, the bioinformatics analysis results show that the expression levels of immune checkpoints CD276, CD40, TNFSF14, and TNFSF9 are closely related to the key clinical outcomes of patients, and high expression of immune checkpoints severely impacts the prognosis of patients. Simultaneously, immunohistochemical analysis of tumor tissues and adjacent normal tissues revealed that the expression levels of CD276, CD40, and TNFSF14 were significantly higher in brain glioblastoma tumor tissues than in adjacent tissues, while TNFSF9 was significantly lower in brain glioblastoma tumor tissues than in adjacent tissues (P < 0.05). This result was also confirmed in different human glioblastoma cell lines (A172, LN229, U251, U87MG) and normal human astrocyte cell line (HEB). Furthermore, in vitro cell experiments further confirmed that knocking down CD40, CD276, and TNFSF14 significantly reduced cell activity compared to the control group, with statistical significance (P < 0.05); overexpression of TNFSF9 significantly reduced cell activity compared to the control group, with statistical significance (P < 0.05). This mechanism provides a promising strategy for targeted therapy of glioblastoma. Based on the above results, our findings have potential therapeutic implications. We can draw the conclusion that these CRICGS are correlated with GBM patients. We can further treat GBM patients by activating or inhibiting these genes or related pathways. At present, no direct mechanism has been identified to confirm the impact of CRICGS on the copper-dependent cell death pathway in glioblastoma multiforme (GBM) cells. However, We have confirmed that the expression profiles of these CRICGS are significantly associated with the clinical prognosis of GBM patients. Furthermore, a strong correlation has been observed between the expression levels of CRICGS and those of FDX1, a key regulatory protein implicated in copper-mediated cell death. Based on these observations, it is reasonable to propose that CRICGS may modulate FDX1 activity—central to the copper-induced death pathway—via specific molecular mechanisms. This modulation could, in turn, influence the progression of copper-dependent cell death in GBM cells.

Due to the reliance on the public database (TCGA) for the initial discovery, the lack of an external validation cohort for the prognostic model, and the use of only in vitro models for functional validation. Our future research directions should focus on in vivo validation, patient cohort studies, combination therapies, and more in-depth mechanism research.

Conclusion

In conclusion, we have identified CRICGs that may be involved in the carcinogenesis and progression of GBM. CRICGS hold significant value in predicting the prognosis of GBM patients. Modulating the expression of CRICGS as a therapeutic approach for glioblastoma shows promising potential applications. Further research is needed to validate our findings.

Supplementary Information

Supplementary Material 1. (1.2MB, pdf)
Supplementary Material 2. (5.7MB, pdf)

Acknowledgements

We thank all the participants for their participation and clinical colleagues for their valuable assistance.

Abbreviations

GBM

Glioblastoma multiforme

CRICGs

Cuproptosis‑related immune checkpoint genes

ICGs

Immune checkpoint genes

TCGA

The Cancer Genome Atlas

FDR

False discovery rate

PPI

Protein-protein interaction

C-index

Concordance index

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

MF

Molecular function

BP

Biological process

CC

Cellular components

GSEA

Gene Set Enrichment Analysis

OS

Overall survival

Authors’ contributions

Conceptualization: Jianping Zeng, Jinhui Huang. Data curation: Jinhui Huang, Jin Liu, Shoufang Tong. Formal analysis: Jinhui Huang, Shoufang Tong, Shushan Hua. Funding acquisition: Jianping Zeng. Methodology: Shushan Hua, Jie Zhang. Project administration: Shuai Liu. Supervision: Jianping Zeng, Jiugeng Feng. Writing-original draft: Jinhui Huang. Writing-review & editing: Rajneesh Mungur, Bin Tang.

Funding

This work was supported by the Natural Science Foundation of Jiangxi Province, China (No. 20242BAB25530); the Science and Technology Planning Project of Jiangxi Province, China (No. 202510252). the Scientific Research Fund of Jiangxi Provincial Education Department (No. GJJ200110); the Youth Scientific Research Cultivation Fund of the First Affiliated Hospital of Nanchang University (No. YFYPY2020); and the Jiangxi Province Natural Science Foundation project (No.20224BAB206066).

Data availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jinhui Huang, Shoufang Tong and Jin Liu contributed equally.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (1.2MB, pdf)
Supplementary Material 2. (5.7MB, pdf)

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

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