Abstract

Background

   Glioblastoma, associated with poor prognosis and impaired immune
   function, shows potential interactions between newly identified
   disulfidptosis mechanisms and T cell exhaustion, yet these remain
   understudied.

Methods

   Key genes were identified using Lasso regression, followed by
   multivariate analysis to develop a prognostic model. Single-cell
   pseudotemporal analysis explored disulfidptosis T-cell exhaustion (Tex)
   signaling in cell differentiation. Immune infiltration was assessed via
   ssGSEA, while transwell assays and immunofluorescence examined the
   effects of disulfidptosis-Tex genes on glioma cell behavior and immune
   response.

Results

   Eleven disulfidptosis-Tex genes were found critical for glioblastoma
   survival outcomes. This gene set underpinned a model predicting patient
   prognosis. Single-cell analysis showed high disulfidptosis-Tex activity
   in endothelial cells. Memory T cell populations were linked to these
   genes. SMC4 inhibition reduced LN299 cell migration and increased
   chemotherapy sensitivity, decreasing CD4 and CD8 T cell activation.

Conclusions

   Disulfidptosis-Tex genes are pivotal in glioblastoma progression and
   immune interactions, offering new avenues for improving
   anti-glioblastoma therapies through modulation of T cell exhaustion.

   Keywords: glioblastoma, disulfidptosis, PD-L1, t cell exhaustion,
   multi-omics

1. Introduction

   Glioblastoma (GBM), a highly aggressive brain cancer, has a median
   survival time of 15 months. Current treatments’ limited efficacy,
   including surgery, radiotherapy, and chemotherapy, underscores the
   urgent need for innovative therapies and a deeper understanding of
   GBM’s molecular basis ([25]1–[26]3). Crucially, the tumor
   microenvironment and mechanisms of immune escape, such as T-cell
   exhaustion (Tex), play significant roles in glioblastoma (GBM) disease
   progression and therapy resistance ([27]4, [28]5). T cell exhaustion is
   characterized by a progressive loss of effector functions and sustained
   expression of inhibitory receptors, which impairs the immune system’s
   ability to effectively combat tumor cells. In gliomas, T cell function
   diminishes due to persistent antigen exposure and the presence of
   immunosuppressive factors like TGF-β and IL-10, alongside increased
   expression of immune checkpoints PD-L1 and CTLA-4 ([29]6–[30]10). These
   factors collectively result in an impaired immune response and enhanced
   tumor proliferation ([31]11–[32]13). Recent studies have elucidated the
   roles of metabolic dysregulation, epigenetic modifications, and chronic
   antigen exposure in driving T cell exhaustion ([33]14–[34]16).
   Furthermore, advancements in single-cell technologies have revealed the
   heterogeneity within exhausted T cell populations, identifying distinct
   subsets with varying functional states ([35]17, [36]18). Understanding
   these complex mechanisms is crucial for developing targeted
   immunotherapies aimed at reinvigorating exhausted T cells and enhancing
   anti-tumor immunity ([37]19, [38]20).

   In addition, recent studies have begun to unravel the complex genetic
   and epigenetic landscape of cancer ([39]21–[40]24), yet the role of
   emerging cellular processes, such as disulfidptosis—a novel cell death
   pathway—and the intricate dynamics of the tumor immune microenvironment
   remain largely underexplored ([41]25). As a newly characterized type of
   regulated cell death (RCD), disulfidptosis is considered to be closely
   related to the occurrence and development of tumors as ferroptosis and
   cuproptosis death, which were fully explored in the past, which is
   incited by the aberrant intracellular buildup of disulfides ([42]26),
   and this procedure cannot be mitigated by previous inhibitors of cell
   death ([43]27). Its linkage between cellular metabolism and fate and
   its significant impact on tumor immune responses is arousing great
   interest ([44]28, [45]29). It is found that under a glucose starvation
   situation, the expression of solute carrier family 7 member 11 can
   induce the abnormal accumulation of cystine and other disulfides
   ([46]30, [47]31). The formation of these disulfide bonds between actin
   cytoskeletal results in the collapse of the cytoskeleton structure and,
   eventually, cell death. Further, the treatment of glucose transporter
   (GLUT) inhibitors can trigger disulfidptosis, which indicates that the
   inducement of disulfidptosis might be a promising therapeutic strategy
   ([48]26). It is also reported that the disulfidptosis procedure not
   only establishes a linkage between cellular metabolism and cellular
   destiny but also demonstrates a conspicuous association with the immune
   response within the tumor microenvironment ([49]32, [50]33). Emerging
   research has shown that many cancer cells experience oxidative stress,
   leading to disulfide metabolism disorders that affect cancer cell
   survival and proliferation ([51]34–[52]37). Additionally, disulfide
   metabolism in cancer cells is also associated with biological behaviors
   such as drug resistance, metastasis, and immune escape ([53]38–[54]40).
   Understanding the interplay between disulfidptosis and GBM may provide
   insights into the complex biology of GBM and help identify potential
   therapeutic targets, ultimately improving the outcomes of GBM patients.

   Building upon these insights, our study investigates the intricate
   interplay between disulfidptosis and Tex within the glioblastoma
   microenvironment, aiming to uncover novel therapeutic targets and
   enhance treatment outcomes for GBM patients. By leveraging the
   relationship between disulfidptosis and Tex, we have developed a robust
   prognostic model for GBM survival and identified key targets that could
   potentiate T-cell-mediated tumor control. This integrative approach not
   only facilitates the prediction of patient outcomes but also paves the
   way for precision therapies tailored to individual molecular profiles.
   Ultimately, our research seeks to advance immunotherapeutic strategies
   in combating glioblastoma, offering promising avenues for personalized
   medicine and improved clinical efficacy.

2. Materials and methods

2.1. Immunofluorescence assay

   For LN229 cell replication, adhere the cells to glass coverslips until
   they reach 30% confluency, then fix them with 4% formaldehyde in
   phosphate-buffered saline (PBS) for 10 minutes at room temperature.
   After washing with PBS, increase membrane permeability by treating with
   0.1-0.5% Triton X-100 in PBS for 5 minutes. Block nonspecific binding
   by incubating with 5% bovine serum albumin (BSA) or serum for 30
   minutes. Next, incubate with the primary antibody, diluted in blocking
   solution, for 1 hour at room temperature or overnight at 4°C. After
   washing with PBS, a fluorescent secondary antibody was applied for 1
   hour in the dark, followed by a final PBS wash ([55]41–[56]43). Mount
   the coverslip and examine the cellular signals using a fluorescence
   microscope ([57]44, [58]45).

2.2. Apoptosis detection using flow cytometry

   Apoptosis was assessed using the Annexin V-FITC kit from BD
   Biosciences, USA. Cells were incubated with Annexin V-FITC for 15
   minutes, followed by a 5-minute incubation with propidium iodide (PI),
   both in the dark. Flow cytometric analysis was performed with BD
   Biosciences equipment, and data were analyzed using FlowJo software.

2.3. Cell invasion and migration assays

   Cell invasion was evaluated using Matrigel-coated Transwell inserts.
   LN229 cells (5 x 10^5 cells/ml) were seeded in the upper compartment
   and incubated for 36 hours at 37°C in 5% CO[2]. After incubation, cells
   adhering to the upper membrane were fixed with 4% formaldehyde, stained
   with crystal violet, washed with PBS, and examined microscopically. The
   invasion was quantified by counting cells that migrated through the
   membrane in five random fields ([59]46). A wound-healing assay was
   conducted to study the effect of disulfidptosis-Tex on cell migration.
   A scratch was made in the monolayer at 0 hours, detached cells were
   removed with PBS, and images were taken after 36 hours for analysis
   ([60]47).

2.4. Evaluation of mitochondrial membrane potential

   The mitochondrial membrane potential (Ψm) was evaluated using the ‘Ψm
   Assay JC-1 Kit’ (Solarbio, M8650, China), employing JC-1 as a
   fluorescent probe. When the membrane potential is high, JC-1
   accumulates within the mitochondrial matrix, leading to the emission of
   red fluorescence. Conversely, at reduced potentials, JC-1 forms
   monomers that emit green fluorescence.

2.5. Measurement of reactive oxygen species detection

   The Reactive Oxygen Species (ROS) levels were measured using the
   Reactive Oxygen Species Assay Kit (Solarbio, CA1410, China) with
   DCFH-DA as the fluorescent probe. ROS converts the non-fluorescent DCFH
   to fluorescent DCF, which is then analyzed to determine intracellular
   ROS concentrations.

2.6. Transcriptomic and clinical data analysis for glioblastoma

   Transcriptomic and comprehensive clinical data for the TCGA-GBM cohort
   were sourced from the GDC portal ([61]https://portal.gdc.cancer.gov/).
   The study focused on entries that provided both extensive clinical
   records and transcriptomic data ([62]48, [63]49). Additionally, the
   CGGA database was used for whole-genome expression profiles with
   corresponding clinical information for GBM ([64]50).

2.7. Single-cell transcriptomic analysis

   Using Seurat package version 4.2.0, the pre-filtered single-cell
   dataset was imported ([65]51, [66]52). Data normalization was performed
   using the ‘NormalizeData’ function. Post-normalization, genes with
   significant variation were identified by balancing average expression
   levels and dispersion metrics. The ‘FindClusters’ function, a
   graph-based clustering tool using a modularity optimization algorithm
   from shared nearest neighbors, delineated 19 distinct clusters from 33
   principal components at a resolution of 0.2. Differentially expressed
   genes (DEGs) in each cluster were determined using ‘FindAllMarkers’
   with default settings in Seurat.

2.8. Cell communication profiling assessment

   Cell Communication Profiling via single-cell analysis ligand-receptor
   interactions among various cell types were analyzed to identify unique
   signaling pathways ([67]53). The ‘CellChat’ tool quantified and
   estimated the probability of intercellular signaling interactions,
   applying default parameters with a significance threshold of P ≤ 0.05
   and adjustments for multiple testing using the Benjamini-Hochberg
   procedure ([68]54). We also assessed the expression of ten
   disulfidptosis-Tex-related genes across different glioblastoma cell
   types using the AUCell scoring method ([69]55, [70]56). Twelve cell
   types identified in the scRNA data were analyzed, categorizing cells
   with AUCell scores above 0.3 as high disulfidptosis-Tex activity and
   those with lower scores as reduced activity.

2.9. Enrichment analysis

   Disulfidptosis-Tex’s role in glioblastoma was assessed using ‘ssGSEA’
   to calculate gene enrichment scores in individual samples
   ([71]57–[72]59). Using ‘surv_cutpoint’, samples were categorized into
   low or high disulfidptosis-Tex enrichment groups. Intersection analysis
   identified cell types with significant disulfidptosis-Tex activity and
   contrasted gene enrichment between the groups. T cell exhaustion-linked
   DEGs in GBM were identified. Gene Ontology (GO) and Kyoto Encyclopedia
   of Genes and Genomes (KEGG) analyses with ‘clusterProfiler’ revealed
   pathways enriched among these DEGs ([73]60–[74]62).

2.10. Prognostic evaluation in glioblastoma

   The prognostic relevance of disulfidptosis-Tex-associated DEGs in
   glioblastoma was evaluated using univariate Cox regression to analyze
   their correlation with patient overall survival (OS) ([75]63–[76]65).
   Genes with a P-value < 0.05 were selected for further analysis
   ([77]66). The study used 268 tumor samples, split into training and
   validation cohorts in a 7:3 ratio, with 106 samples in the latter. A
   prognostic model was developed using the LASSO Cox regression method
   via the ‘glmnet’ R package ([78]67, [79]68), refining the list of
   potential genes.

2.11. Differential expression and functional analysis

   Differential expression between high- and low-risk glioblastoma groups
   was analyzed using the ‘limma’ R package ([80]69, [81]70). Gene Set
   Enrichment Analysis (GSEA) of log2 FC-ranked genes was performed with
   ‘clusterProfiler’ ([82]71–[83]73), and functional differences were
   examined using ‘GSVA’ ([84]74, [85]75). Results were visualized with
   ‘pheatmap’.

2.12. Immune pathway activity and immune cell infiltration analysis

   ssGSEA analyses were performed using the ‘gsva’ package in R to
   evaluate immune pathway activities in the study’s samples, utilizing
   established molecular markers ([86]76). As an enhancement of GSEA,
   ssGSEA calculates enrichment scores for individual gene set pairs
   across different samples ([87]77). These scores reflect the coordinated
   regulation of genes, either upregulated or downregulated, within
   specific gene sets for each sample. Gene expression data from all GBM
   samples were used to compute enrichment scores for 28 distinct immune
   cell types, derived from the TISIDB database
   ([88]http://cis.hku.hk/TISIDB/index.php) ([89]78). Variations in immune
   cell infiltration levels between low- and high-risk groups were
   visualized using the ‘ggplot2’ R package ([90]79, [91]80).

2.13. Statistical analyses

   Statistical analyses were performed using R software (version 4.1.3)
   and GraphPad Prism 8.0. A P-value of <0.05 was considered statistically
   significant ([92]81). In the graphs, the symbols *, **, and ***
   represent P-values of <0.05, <0.01, and <0.001, respectively.

3. Results

3.1. Single-cell RNA sequencing reveals disulfidptosis-Tex-associated gene
expression in glioblastoma and identifies immune cell subtypes

   Single-cell RNA sequencing has significantly enhanced our understanding
   of the cellular composition of glioblastoma. In this study, we utilized
   the single-cell RNA sequencing dataset [93]GSE173278 to explore the
   genes exhibiting elevated expression linked to disulfidptosis-Tex ([94]
   Figure 1 ). The analysis of single-cell transcriptomes revealed 19
   distinct clusters across 29,543 cells, as shown in the UMAP plot ([95]
   Figure 2A ). Cell surface markers were also used to identify 12 unique
   cell subtypes, including dendritic cells, central memory T cells, and
   macrophages ([96] Figures 2B, D ). Further investigation focused on
   disulfidptosis-Tex-associated differential expression genes (DEGs)
   expression patterns within these subtypes ([97] Figure 2C ).

Figure 1.

   [98]Figure 1
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   Workflow diagram of study.

Figure 2.

   [100]Figure 2
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   Single-cell analysis of genes associated with disulfidptosis-Tex in
   glioblastoma. (A) Distribution of glioblastoma cell subpopulations. (B)
   Annotation of the different glioblastoma cell subpopulations. (C) Gene
   expression profiles are specific to each cluster. (D) Expression levels
   of disulfidptosis-Tex genes across various cell types.

3.2. Endothelial cell enrichment of disulfidptosis-Tex in glioblastoma

   [102]Figure 3A illustrates the identification of 205 cells exhibiting
   active disulfidptosis-Tex. The UMAP representation of these cells
   revealed a significant prevalence of endothelial cells (ECs) ([103]
   Figure 3B ). Notably, ECs showed the strongest association with
   disulfidptosis-Tex, indicating a marked accumulation of this marker
   within tumor-associated ECs.

Figure 3.

   [104]Figure 3
   [105]Open in a new tab

   Pseudotime analysis identifies key disulfidptosis-Tex genes implicated
   in glioblastoma progression. (A) AUC scores for disulfidptosis-Tex
   activity. (B) UMAP-based chromatic map displaying the activity scores
   of disulfidptosis-Tex. (C) Pseudotime trajectory analysis. (D)
   Pseudotime trajectories segmented using Monocle2. (E) Expression
   patterns of differentially expressed genes (DEGs) across distinct cell
   branches.

3.3. Pseudotime analysis reveals core disulfidptosis-Tex gene driving
glioblastoma progression

   Pseudotime analysis of endothelial cells (ECs) in glioblastoma revealed
   the key role of disulfidptosis-Tex genes in tumor progression ([106]
   Figure 3C ). Five distinct transcriptional states were identified along
   the trajectory ([107] Figure 3D ). Further analysis of these genes
   showed their involvement in ‘angiogenesis regulation’ and
   ‘extracellular matrix organization’ ([108] Figure 3E ).

3.4. Key signaling pathways in cell communication interaction

   To investigate the roles of various cell populations in glioblastoma,
   we conducted an analysis of intercellular communication, which revealed
   significant interactions between glioblastoma cells and immune cells,
   including central memory T cells and macrophages ([109] Figure 4A ).
   Both outgoing and incoming signals were examined, alongside relevant
   ligand-receptor pairs across 12 distinct cell types. Key signaling
   pathways identified in this analysis included SPP1, PTN, MK, PSAP, GRN,
   and MIF ([110] Figure 4B ). Further analysis of the signaling pairs
   highlighted the PSAP-GPR37 pathway as the predominant interaction in
   endothelial cells, facilitating communication with oligodendrocytes and
   dendritic cells ([111] Figure 4C ). Signaling pathways from
   oligodendrocytes and macrophages to endothelial cells were also
   identified ([112] Figure 4D ). PSAP was found to be expressed across
   all 12 cell types ([113] Figure 4E ), while GPR37L1 was predominantly
   present in corticotroph cells, and GPR37 showed the highest expression
   in oligodendrocytes ([114] Figure 4F ).

Figure 4.

   [115]Figure 4
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   Key signaling pathways involved in cell communication interactions. (A)
   Profiling of cell communication through single-cell analysis. (B)
   Predicted incoming signaling pathways. (C) Potential outgoing signaling
   pairs. (D) Predicted incoming signaling pairs. (E) Signaling pair
   interactions. (F) Distribution of receptor expression.

3.5. Identification of differentially expressed genes in disulfidptosis-Tex
active subgroups

   In glioblastoma, endothelial cells with the highest disulfidptosis-Tex
   activity were characterized by 3,890 differentially expressed genes
   (DEGs) compared to other cell types ([117] Figure 5A ). A comparison
   between glioblastoma tissues and normal controls revealed 2,200 DEGs,
   with a heatmap showing the top five upregulated and downregulated genes
   ([118] Figure 5B ). Stratification based on disulfidptosis-Tex activity
   identified an additional 4,255 DEGs, with the top five genes in the
   high-activity group also highlighted ([119] Figure 5C ). An
   intersection analysis of these groups identified 143 key genes ([120]
   Figure 5D ). Enrichment analysis focused on Gene Ontology (GO) and KEGG
   pathways. GO analysis showed significant enrichment in processes such
   as synapse organization and urogenital system development, along with
   molecular functions like glycosaminoglycan binding and ECM structural
   components ([121] Figure 5E ). KEGG pathway analysis revealed notable
   enrichment in ECM-receptor interactions ([122] Figure 5F ).

Figure 5.

   [123]Figure 5
   [124]Open in a new tab

   Development of a prognostic signature based on disulfidptosis-Tex. (A)
   Differentially expressed genes (DEGs) in glioblastoma. (B) The top 10
   genes with the most significant differential expression between
   glioblastoma and normal control samples. (C) Top 10 genes
   differentially expressed between glioblastoma subgroups with high and
   low disulfidptosis gene enrichment. (D) Venn diagram illustrating gene
   overlap. (E) Gene Ontology (GO) enrichment analysis. (F) Kyoto
   Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.
   (G) LASSO analysis. (H) Kaplan-Meier (K-M) survival curves for
   high-risk and low-risk glioblastoma patients in the training cohort.
   (I) Kaplan-Meier (K-M) survival curves for high-risk and low-risk
   glioblastoma patients in the validation cohort. (J) Time-dependent
   Receiver Operating Characteristic (ROC) curves in the training cohort
   model. (K) Time-dependent Receiver Operating Characteristic (ROC)
   curves in the validation cohort model. (L) Correlation analysis between
   disulfidptosis and exhausted T cells.

3.6. Development of the disulfidptosis-Tex-based prognostic signature

   A LASSO-Cox regression analysis was performed to assess the impact of
   disulfidptosis-Tex genes on glioblastoma survival, resulting in a model
   consisting of 11 key genes ([125] Figure 5G ). This model successfully
   stratified patients into high- and low-risk groups, with the high-risk
   group demonstrating higher mortality rates and upregulated prognostic
   genes ([126] Figure 5H ). Kaplan-Meier survival curves further
   confirmed that the high-risk group had a worse prognosis compared to
   the low-risk group ([127] Figure 5I ). The model’s ability to predict
   patient outcomes was evaluated using ROC curves, achieving AUC values
   of 0.780 for 5-year survival in the training set ([128] Figure 5J ) and
   0.841 in the validation set ([129] Figure 5K ). Notably, SMC4 showed a
   strong negative correlation with T cell exhaustion genes ([130]
   Figure 5L ).

3.7. Disulfidptosis-Tex affects migration ability of glioblastoma cell

   We investigated the role of SMC4, a key gene in the
   disulfidptosis-Tex-related prognostic model, in the migratory behavior
   of glioblastoma cells. Wound-healing assays showed that silencing SMC4
   reduced cell migration at 36 hours compared to controls ([131]
   Figures 6A, B ). While transwell assays at 24 hours showed no
   significant differences between the SMC4-silenced and control groups
   ([132] Figure 6C ), at 48 hours, the migration of SMC4-knockdown cells
   was significantly lower than in the control group ([133] Figure 6D ).

Figure 6.

   [134]Figure 6
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   The impact of disulfidptosis-Tex on the migratory capacity of
   glioblastoma cells. (A, B) Wound-healing assay. (C, D) Transwell
   migration assay. ***p < 0.001, **p < 0.01.

3.8. Enrichment analysis unveiled intricate network influenced by
disulfidptosis-Tex in glioblastoma

   Gene Set Enrichment Analysis (GSEA) was used to explore the underlying
   mechanisms of the 11-gene disulfidptosis-Tex model. The results
   revealed significant enrichment in six key pathways, including the T
   cell receptor signaling pathway, in the high-risk subgroup ([136]
   Figure 7A ). Additionally, GSVA was performed using the same MsigDB
   pathway data, identifying five pathways with the most pronounced
   differences between high- and low-risk subgroups. A heatmap of these
   variations revealed a notable enrichment of drug metabolism processes
   in glioblastoma patients with low disulfidptosis-Tex activity ([137]
   Figure 7B ). These findings suggest that disrupting disulfidptosis-Tex
   could influence drug metabolism and sensitivity in glioblastoma cells.

Figure 7.

   [138]Figure 7
   [139]Open in a new tab

   Enrichment analysis unveiled an intricate network influenced by
   disulfidptosis-Tex in glioblastoma. (A) GSEA analysis. (B) Heatmap of
   pathway enrichment differences between high- and low-risk groups from
   GSVA analysis.

3.9. Connection between disulfidptosis-Tex and immune infiltration

   The relationship between immune cell infiltration and tumor progression
   was demonstrated by analyzing 28 immune cell types in both high- and
   low-risk subgroups, revealing a link with disulfidptosis-Tex ([140]
   Figure 8A ). Notably, activated and central memory CD8 T cells showed a
   significant negative correlation ([141] Figure 8B ). Immune cell
   infiltration differed markedly between risk groups, especially in
   regulatory T cells ([142] Figure 8C ). Additionally, strong
   correlations were found between prognostic genes and specific immune
   cells ([143] Figures 9A-I ). SMC4 was positively correlated with
   activated CD4 T cells ([144] Figure 9D ) and negatively correlated with
   CD56dim NK cells ([145] Figure 9E ). These findings suggest that
   disulfidptosis-Tex influences the infiltration of CD56dim NK cells and
   various T cell subsets, emphasizing the role of these genes in
   modulating tumor microenvironment interactions.

Figure 8.

   [146]Figure 8
   [147]Open in a new tab

   Connection between disulfidptosis-Tex and immune infiltration. (A) The
   relative proportions of immune cells across all glioblastoma samples.
   (B) Correlation matrix of immune cells. (C) The proportions of immune
   cells between high- and low-risk groups. ****p < 0.0001, ***p < 0.001,
   **p < 0.01, *p < 0.05.

Figure 9.

   [148]Figure 9
   [149]Open in a new tab

   Correlation scatter plots. The correlations between prognostic genes
   and specific immune cell types. (A) LYPLA1 and CD56dim natural killer
   cells. (B) COL4A1 and CD56dim natural killer cells. (C) SMC4 and
   activated CD4 T cells. (D) CYFIP2 and monocytes. (E) SMC4 and CD56dim
   natural killer cells. (F) CEND1 and monocytes. (G) PLOD2 and CD56dim
   natural killer cells. (H) PXDN and CD56dim natural killer cells. (I)
   COL4A1 and gamma delta T cells.

3.10. Drug sensitivity prediction and validation

   To better understand the prognostic value of the risk score signature
   in predicting patient outcomes, we examined mutations in
   glioblastoma-specific genes, focusing on the 20 most frequently mutated
   genes. TP53 mutations were the most common across both subgroups,
   followed by PTEN mutations ([150] Figures 10A, B ). We also evaluated
   whether risk scores could predict chemotherapeutic responses in
   glioblastoma patients. Clinical trials were performed testing drugs
   such as Dinaciclib, Bortezomib, and Docetaxel ([151] Figure 10C ). The
   results indicated that patients with higher risk scores exhibited
   increased sensitivity to these drugs, suggesting that they may be
   promising treatment options for high-risk glioblastoma patients.

Figure 10.

   [152]Figure 10
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   Drug sensitivity prediction. (A, B) The top 20 genes with the highest
   mutation frequency were in the high-risk group and in the low-risk
   group. (C) Differences in drug sensitivity between high- and low-risk
   groups.

3.11. Inhibiting disulfidptosis-Tex induces glioma cell sensitivity to drugs
and increases PD-L1 level

   We also evaluated drug sensitivity using Dactinomycin, Bortezomib, and
   Docetaxel. The results showed that combining si-SMC4 with these drugs
   significantly reduced the invasiveness of LN299 cell lines compared to
   single-agent treatments ([154] Figures 11A, B ), highlighting the role
   of disulfidptosis-Tex in glioma metastasis. Interestingly, all the
   drugs tested were found to upregulate PD-L1 expression in LN299 cells
   ([155] Figure 11C ). Further investigation revealed a significant
   increase in PD-L1 expression when LN299 cells were treated with the
   si-SMC4-drug combination ([156] Figure 11D ).

Figure 11.

   [157]Figure 11
   [158]Open in a new tab

   SMC4 regulates PD-L1 expression level. (A) Transwell assay of invasion
   ability. (B) Results of invasion cell amounts. (C) Cellular
   immunofluorescence experiment. (D) SMC4, in combination with the drug,
   promotes the upregulation of PD-1 in cells. ***p < 0.001, **p < 0.01.

3.12. Inhibiting disulfidptosis-Tex induces mitochondrial membrane potential
and ROS production in LN299 cells

   To evaluate mitochondrial dynamics, LN299 cells were stained with JC-1
   and divided into two groups: control and SMC4-interfered. Flow
   cytometry analysis revealed a significant reduction in mitochondrial
   membrane potential in the SMC4-interfered cells compared to controls
   ([159] Figure 12A ). Additionally, there was a marked increase in
   reactive oxygen species (ROS) accumulation in these cells ([160]
   Figure 12B ). These results suggest that SMC4 plays a regulatory role
   in maintaining mitochondrial integrity.

Figure 12.

   [161]Figure 12
   [162]Open in a new tab

   Impact of SMC4 on Mitochondrial Dysfunction and Membrane Potential in
   Glioblastoma Cells. (A) Detection of Mitochondrial membrane potential
   (B) Assessment of ROS production. **p < 0.01.

3.13. SMC4 inhibition reduces the ability of LN299 cells to activate T cells

   Our analysis showed a positive correlation between SMC4 expression in
   gliomas and CD4 T cell activation. To explore this further, we
   co-cultured SMC4 knockdown LN299 cells and control cells with T cells.
   The results indicated that LN299 cells with high SMC4 expression
   significantly increased the proportion of CD4 T cells ([163]
   Figures 13A-C ). However, SMC4 depletion led to a 13.3% reduction in
   CD4 T cell activation ([164] Figures 13D-F ) and a 9.62% decrease in
   CD8 T cell activation ([165] Figures 13G-I ). These findings suggest
   that SMC4 expression in glioma cells influences T cell cytotoxicity.

Figure 13.

   [166]Figure 13
   [167]Open in a new tab

   Statue of T cell activation. (A–C) T cell percentage. (D–F) CD4 T cell
   activation. (G–I) CD 8 T cell activation. **p < 0.01, *p < 0.05.

4. Discussion

   GBM is a highly heterogeneous and aggressively vascularized malignancy,
   which contributes to its dismal prognosis ([168]82). Within the GBM
   tumor microenvironment, immune evasion is a common phenomenon, with
   T-cell exhaustion playing a pivotal role. This exhaustion, often
   induced by persistent antigen exposure and chronic inflammatory states,
   results in diminished T cell functionality and immune escape ([169]83).
   Exhausted T cells exhibit upregulation of inhibitory receptors such as
   PD-1, and their binding to PD-L1 on tumor cells dampens T cell
   responses, a mechanism central to immune checkpoint regulation and
   tumor immune evasion ([170]84, [171]85).

   Our research indicates that targeting the disulfidptosis-T cell
   exhaustion (Tex) network can modulate PD-L1 expression in glioblastoma
   cells ([172] Figure 11C ). Disulfidptosis, a regulated form of cell
   death, can potentially affect the survival and turnover of tumor cells.
   These observations suggest combining disulfidptosis inhibition with
   anti-PD-L1 therapy may improve clinical outcomes in glioblastoma
   treatment ([173] Figure 11A ). Additionally, disulfidptosis might
   promote immune evasion by influencing tumor cell metabolic pathways and
   stress responses. For instance, it could bolster antioxidant defenses
   in tumor cells, protecting them from immune-mediated damage driven by
   reactive oxygen species (ROS) ([174]86). Moreover, disulfidptosis may
   alter the surface marker profile of tumor cells, thereby affecting
   immune recognition and clearance ([175]87). Our findings also implicate
   the disulfidptosis-Tex axis in the T cell receptor signaling pathway
   ([176] Figure 7A ), which may contribute to creating an
   immunosuppressive environment, thereby facilitating immune escape. The
   altered oxidative and metabolic states of tumor cells ([177] Figure 3E
   ) may further impair T-cell activity, promoting tumor progression and
   immune resistance.

   Interestingly, our data show dynamic shifts in the disulfidptosis-Tex
   network during the malignant transformation of glioblastoma ([178]
   Figure 3C ). Single-cell RNA sequencing (scRNA-seq) revealed
   considerable cellular heterogeneity, with endothelial cells exhibiting
   the highest levels of disulfidptosis-Tex activity, underscoring their
   critical role in tumor progression ([179]88, [180]89). Endothelial
   cells are essential for vascular processes such as angiogenesis and
   permeability ([181]90), and aberrant angiogenesis is a key driver of
   tumor growth, invasion, and recurrence ([182]91–[183]93). In addition,
   the disulfidptosis-Tex network not only enhances the invasiveness of
   glioblastoma cells but also increases their sensitivity to chemotherapy
   ([184] Figure 10C ). Although our study did not include in vivo
   validation, cell communication analyses suggest that endothelial cells
   (ECs) significantly influence the disulfidptosis-Tex interaction.
   Elevated vascular permeability can facilitate glioblastoma metastasis
   ([185]48), and our findings emphasize the intricate interactions within
   the glioblastoma tumor microenvironment. Identifying key signaling
   pathways and ligand-receptor pairs, such as PSAP-GPR37 and
   SPP1-(ITGA5+ITGB1), highlights the importance of intercellular
   communication in modulating tumor behavior. The
   disulfidptosis-Tex-endothelial cell network plays a central role in
   glioblastoma progression, indicating that targeting this axis may offer
   new therapeutic avenues by modifying the tumor microenvironment.

   Establishing an 11-gene disulfidptosis-Tex signature across independent
   cohorts reinforces its potential clinical utility. However, tumor
   resistance mechanisms often undermine therapeutic approaches’
   effectiveness ([186]94, [187]95). The analysis of the
   disulfidptosis-Tex risk model in glioblastoma may help identify patient
   subgroups that are more likely to respond to treatment. Strong
   correlations between immune cell subsets and prognostic genes suggest
   that immunotherapy could provide a promising alternative for patients
   with tumor ([188]96–[189]98), especially those with poor responses to
   conventional chemotherapy or targeted therapies. Nevertheless, further
   exploration of the immune microenvironment in GBM is essential.

   Disulfidptosis-Tex plays a pivotal role in modulating tumor cell
   metabolism by influencing enzymatic activities, inducing gene mutations
   associated with metabolic processes, and activating critical signaling
   pathways. Genes involved in apoptosis regulation, RNA dynamics
   ([190]99–[191]102), and cell cycle control, such as SMC4 ([192]103,
   [193]104), are integral to glioblastoma cell survival and
   proliferation, with our data suggesting that SMC4 significantly
   contributes to tumor cell growth and migration by regulating the
   cytoskeleton and maintaining mitochondrial integrity, as evidenced by
   increased ROS and altered mitochondrial membrane potential upon its
   inhibition. Additionally, genes like COL4A1, PLOD2, and PXDN
   ([194]105–[195]108), which participate in extracellular matrix
   remodeling, alongside immune-related genes such as CYFIP2, EMP3, and
   HLA-B ([196]109–[197]114), are instrumental in modulating the tumor
   microenvironment and facilitating immune evasion. These genetic
   interactions underscore the complexity of disulfidptosis-Tex’s role in
   glioblastoma progression. Our 11-gene disulfidptosis-Tex model holds
   promise as both a prognostic biomarker and a potential therapeutic
   target. By modulating T cell exhaustion, these genes may significantly
   influence the responsiveness of glioblastoma to immunotherapies. For
   instance, the observed upregulation of PD-L1 following the inhibition
   of disulfidptosis-Tex genes like SMC4 suggests a potential feedback
   mechanism that could be exploited to enhance the efficacy of PD-1/PD-L1
   inhibitors. Furthermore, altered immune cell infiltration, such as
   decreased activation of CD4 and CD8 T cells upon SMC4 inhibition,
   highlights the intricate balance between tumor cell death pathways and
   immune surveillance. One hypothetical pathway is that SMC4 interacts
   with signaling molecules involved in the PD-1/PD-L1 axis, thereby
   affecting immune checkpoint regulation and T cell exhaustion.
   Additionally, SMC4 may influence the expression of chemokines or
   cytokines that attract or activate T cells within the tumor
   microenvironment. Understanding these interactions provides a
   foundation for developing combination therapies that simultaneously
   target disulfidptosis pathways and bolster immune responses, thereby
   improving therapeutic outcomes for glioblastoma patients. Future
   studies should delve deeper into the roles of these genes in
   disulfidptosis-Tex and glioblastoma progression, investigating the
   precise molecular mechanisms by which SMC4 operates and its interplay
   with immune cells, to fully elucidate their mechanisms and therapeutic
   potential.

   While our study provides valuable insights into the role of
   disulfidptosis-Tex in glioblastoma progression and immune cell
   impairment, it is important to acknowledge several limitations that
   warrant further investigation. Primarily, our research relies heavily
   on multi-omics data, and the cellular communication mechanisms
   identified may not fully replicate the complexities of the actual
   glioblastoma immune microenvironment. The relatively small sample size
   within our glioblastoma cohort also limits the robustness and
   generalizability of our prognostic models, underscoring the necessity
   for validation in larger, independent cohorts. Additionally, our
   cellular experiments were exclusively conducted on glioblastoma cell
   lines, which restricts our ability to explore the effects of
   disulfidptosis on exhausted T cells directly. This narrow focus
   highlights the need for future studies to prioritize the biological
   impact of disulfidptosis on T-cell exhaustion and to incorporate
   diverse experimental models, including multiple glioblastoma cell lines
   and in vivo systems, to better mimic the tumor microenvironment.
   Furthermore, our 11-gene disulfidptosis-Tex prognostic model, while
   demonstrating strong predictive power within the training and internal
   validation cohorts, has yet to be validated externally due to the
   unavailability of independent datasets with comprehensive
   transcriptomic and clinical information.

   Future research should aim to validate this prognostic signature in
   independent and diverse patient populations to confirm its clinical
   utility and generalizability. Prospective studies are also essential to
   assess the model’s effectiveness across various clinical settings,
   thereby enhancing its applicability and reliability. Given the inherent
   complexity of glioblastoma and the intricate interactions between
   disulfidptosis and other biological processes, extensive experimental
   validation is crucial to substantiate our findings and to fully
   elucidate the mechanistic pathways involved. Addressing these
   limitations in future investigations will not only strengthen the
   validity of our current findings but also pave the way for the
   development of more effective and personalized therapeutic strategies
   for glioblastoma patients.

5. Conclusion

   Disulfidptosis-Tex genes are pivotal in regulating glioblastoma
   progression and immune cell infiltration, offering a novel strategy to
   modulate T cell exhaustion and enhance the efficacy of
   anti-glioblastoma therapies. Our research advances the understanding of
   how the disulfidptosis-Tex network contributes to glioblastoma
   progression and highlights potential therapeutic approaches targeting
   this pathway. These findings open up new possibilities for targeted
   interventions aimed at improving treatment outcomes for glioblastoma
   patients.

Acknowledgments