Caspase-4 in glioma indicates deterioration and unfavorable prognosis by affecting tumor cell proliferation and immune cell recruitment

Abstract

Gliomas are the most common malignant tumors of the central nervous system, accounting for approximately 80% of all malignant brain tumors. Accumulating evidence suggest that pyroptosis plays an essential role in the progression of cancer. Unfortunately, the effect of the pyroptosis-related factor caspase-4 (CASP4) on immunotherapy and drug therapy for tumors has not been comprehensively investigated. In this study, we systematically screened six hub genes by pooling differential pyroptosis-related genes in The Cancer Genome Atlas (TCGA) glioma data and the degree of centrality of index-related genes in the protein-protein interaction network. We performed functional and pathway enrichment analyses of the six hub genes to explore their biological functions and potential molecular mechanisms. We then investigated the importance of CASP4 using Kaplan-Meier survival analysis of glioma patients. TCGA and the Chinese Glioma Genome Atlas (CGGA) databases showed that reduced CASP4 expression leads to the potent clinical deterioration of glioma patients. Computational analysis of the effect of CASP4 on the infiltration level and recruitment of glioma immune cells revealed that CASP4 expression was closely associated with a series of tumor-suppressive immune checkpoint molecules, chemokines, and chemokine receptors. We also found that aberrant CASP4 expression correlated with chemotherapeutic drug sensitivity. Finally, analysis at the cellular and tissue levels indicated an increase in CASP4 expression in glioma, and that CASP4 inhibition significantly inhibited the proliferation of glioma cells. Thus, CASP4 is implicated as a new prognostic biomarker for gliomas with the potential to further guide immunotherapy and chemotherapy strategies for glioma patients.

Keywords: CASP4; Chemotherapy; Glioma; Immunotherapy; Pyroptosis.

Figure 1

Flow diagram of study procedure.

Figure 2

Expression and interaction network of pyroptosis-related genes in gliomas.

Figure 3

GO and KEGG enrichment analysis of six pyroptosis-related hub genes. (A) Biological processes (BP), Cellular components (CC), Molecular function (MF) in GO enrichment analysis. (B) KEGG pathway enrichment analysis.

Figure 4

Kaplan–Meier survival curves of the six pyroptosis-related hub genes.

Figure 5

Correlation between CASP4 and classical pyroptosis-related glioma genes. (A) Expression of recognized pyroptosis pathway-related genes in LGG and GBM. (B) Protein–protein interaction analysis of CASP4 and classical pyroptosis-related genes; number of nodes = 8, number of edges = 26, average node degree = 6.5, avg. local clustering coefficient = 0.94, PPI enrichment P-value < 1.0e−16. (C) Correlation between expression of CASP4 and classical pyroptosis-related genes in LGG. (D) Correlation between expression of CASP4 and classical pyroptosis genes in GBM. *P < 0.05, ^**P < 0.01, ^***P < 0.001, ^****P < 0.0001 and ns, no significant difference (P > 0.05). The significance of differences between groups of samples was evaluated by Wilcox tests (2 groups) and Kruskal–Wallis analysis (≥ 3 groups).

Figure 6

Correlation between CASP4 expression and clinical manifestations in glioma patients.

Figure 7

Distribution of tumor-infiltrating immune cells in glioma and their correlation with CASP4 expression. (A) Correlation between CASP4 and six tumor-infiltrating immune cell types in LGG and GBM. (B) Percentages of 22 immune cells in glioma patients determined using the CIBERSORT algorithm. (C) Percentage differentiation of 22 immune cells in patients with high and low CASP4 expression. (D) ESTIMATE algorithm results for relationship between high and low CASP4 expression with immune cells and immune scores (E). (F) Genes associated with the seven steps of antitumor immunity. (G) Correlation between CASP4 expression and the seven steps of anti-tumor immunity. *P < 0.05, ^**P < 0.01, ^***P < 0.001, ^****P < 0.0001 and ns, no significant difference (P > 0.05).

Figure 8

Correlations between the expression of CASP4 and immune checkpoint molecules, chemokines, and chemokine receptors. Correlations between the expression of CASP4 and immune checkpoint molecules (A), chemokines (C), and chemokine receptors (E) across human cancers. Scatter plots of CASP4, immune checkpoint molecules (B), chemokine (D), and chemokine receptor (F) expression in gliomas from TCGA dataset. (G) Protein–protein interaction network based on CASP4, 12 chemokines and eight chemokine receptors.

Figure 9

Relationship between CASP4 expression and drug sensitivity. Spearman’s correlation analysis of paclitaxel (A), sunitinib (B), dabrafenib (C), dasatinib (D), rapamycin (E), sorafenib (F), temozolomide (G), piperlongumine (H), and erlotinib (I) IC₅₀ scores and CASP4 gene expression.

Figure 10

Validation of CASP4 expression and function in glioma. (A) CASP4 expression in normal tissue and glioma tissue. Scale bar, 100 μm. (B) Quantification of CASP4 protein levels in normal tissue and tumor tissue, Data represent the mean ± SD, n = 3. (C) CASP4 expression in astrocytes HA1800 and glioma cells U251 detected using Western blot analysis; GAPDH was used as an internal control. (D) Quantification of CASP4 protein levels in HA1800 and U251 cell lines. Data represent the mean ± SD, n = 3. (E) Efficiency of CASP4 inhibition mediated by siRNA detected by Western blot; GAPDH was used as an internal control. (F) Quantification of CASP4 protein inhibition mediated by siRNA. Data represent the mean ± SD, n = 3. (G) CASP4 inhibition significantly inhibited U251 cell migration as determined by colony formation assay. (H) Quantification of the number of colonies. Data represent the mean ± SD, n = 3. (I) Important signaling pathways associated with CASP4 according to GSEA.