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. 2018 Oct 11;25(3):343–354. doi: 10.1111/cns.13072

Integrated profiling identifies caveolae‐associated protein 1 as a prognostic biomarker of malignancy in glioblastoma patients

Qing Guo 1, Ge‐Fei Guan 1, Wen Cheng 1, Cun‐Yi Zou 1, Chen Zhu 1, Peng Cheng 1,✉, An‐Hua Wu 1,✉
PMCID: PMC6488874  PMID: 30311408

Summary

Aims

Glioblastoma (GBM) is a lethal disease of the central nervous system with high mortality, and novel therapeutic targets and strategies for GBM are urgently needed. Caveolae‐associated protein 1 (CAVIN1) is an essential caveolar component‐encoding gene and has been poorly studied in glioma. To this end, in this study, we evaluated CAVIN1 expression in glioma tissue as well as the correlation between CAVIN1 expression and prognosis in glioma patients using the data collected from clinical samples or from the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Rembrandt, and Gene Expression Omnibus (GEO) data sets.

Methods

Survival analysis was performed with the Kaplan‐Meier curve and log‐rank test. The predictive role of CAVIN1 in progressive malignancy in glioma was evaluated by using a receiver operator characteristic (ROC) curve. Gene ontology (GO), Gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) methods were used to interpret the functions of CAVIN1 in GBM.

Results

CAVIN1 expression was elevated in GBM compared with that in low‐grade glioma and nontumor brain samples and was correlated with unfavorable outcomes in glioma patients. Additionally, CAVIN1 could serve as an independent predictive factor for progressive malignancy in GBM. Furthermore, CAVIN1 was associated with disrupted angiogenesis and immune response in the tumor microenvironment of GBM.

Conclusions

We identified CAVIN1 as a prognostic biomarker and potential target for developing novel therapeutic strategies against GBM.

Keywords: brain neoplasms, CAVIN1, glioma, prognosis

1. INTRODUCTION

As the most common primary malignancy in the central nervous system, glioblastoma (GBM) remains the most lethal cancer in the brain.1, 2 Despite extensive resection and a combination of chemo‐ and radiotherapy, the median survival of GBM patients is only 15 months.3, 4, 5 Caveolae are organelles of the plasma membrane in various types of human cells that play a crucial physiological role in the regulation of various cellular functions, including endocytosis, cell signaling, membrane trafficking, and lipid regulation.6, 7, 8 Caveolar coat components include two major protein families, cavins and caveolins.9 Compared with other lipid rafts, caveolae have a discrete morphology and unique signature proteins, caveolin 1 (CAV1) and caveolae‐associated protein 1 (CAVIN1, also known as polymerase I and transcript release factor, PTRF).7 As the first identified member of the cavin family, CAVIN1 was originally characterized as a cytoplasmic factor interacting with RNA polymerase I and mediating the dissociation of paused ternary Pol I transcription complexes.10 Then, its role in the formation and function of caveolae was defined.6, 11 CAVIN1 can interact with CAV1 and protect it from lysosome‐dependent degradation,6, 12 although the lack of CAV1 caused by CAVIN1 mutations could induce muscular dystrophy and lipodystrophy in humans.13 A deficiency of Cavin1 in mice causes macrophage number and phenotype changes in the lungs14 and leads to glucose intolerance and insulin resistance.15 The overexpression of CAVIN1 induces cellular senescence in human fibroblasts.16 Additionally, CAVIN1 participates in the regulation of immunity and has been suggested to protect human vascular endothelial cells from CTL‐mediated lysis.17

According to previous studies on its function in tumor biology, CAVIN1 plays diverse roles in different types of cancer. CAVIN1 has been reported to reduce the invasion of prostate cancer and tumorigenesis of colorectal cancer.7, 18, 19 CAVIN1 expression is downregulated in breast, colorectal, gastric, and non‐small‐cell lung cancer19, 20, 21, 22 and acts as a tumor suppressor in Ewing's sarcoma.23 However, CAVIN1 shows a tumor‐promoting role in pancreatic cancer by interacting with CAV1.24 Furthermore, CAVIN1 is required for cell proliferation and migration in rhabdomyosarcoma.25 The knock‐down of CAVIN1 reduces multidrug resistance in breast cancer cell line MCF‐7/ADR cells.26 In GBM, it has been shown that CAVIN1 mediated chemoresistance in U251 cell lines, and its expression was upregulated in samples from relapsed GBM patients, but the number of patient samples was limited.27, 28 A recent study identified an eight‐gene signature, including CAVIN1 that could predict the outcome of GBM patients.29 However, the function of CAVIN1 and its value as an individual prognostic marker in GBM need to be further investigated.

Therefore, this study sought to investigate the expression of CAVIN1 and its value as a prognostic marker in glioma with clinical samples and data from the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Rembrandt, and Gene Expression Omnibus (GEO) data set (GSE16011). We verified that CAVIN1 is an independent risk factor for glioma malignancy and might be a potential therapeutic target in GBM. Functional annotation of CAVIN1 in GBM was carried out by bioinformatics methods and showed that CAVIN1 serves as a regulator of disrupted angiogenesis and immune response in the microenvironment of GBM. Taken together, the results of this study suggest CAVIN1 as a possible biomarker in developing new antiangiogenesis or therapeutic immune strategies in GBM.

2. MATERIALS AND METHODS

2.1. Patient samples

Patient samples for western blot were collected at the First Hospital of China Medical University from June 2017 to February 2018 and included 12 samples (nine glioma tissues: three cases each for grades II, III, and IV, and three nontumor brain tissues from cranial injury internal decompression patients as control). The glioma samples for immunohistochemistry and survival analysis (14 cases for grade II, 27 cases for grade III, and 33 cases for grade IV) were collected at the First Hospital of China Medical University from January 2009 to June 2012. The histological diagnoses of these samples were confirmed by two neuropathologists according to the 2010 World Health Organization (WHO) classification guidelines. This study was approved by the Ethics Committee of the First Hospital of China Medical University.

2.2. Protein extraction and western blotting

Protein extraction, quantification, and immunoblotting were performed as described previously.30, 31 Detailed information is provided in the Appendix S1.

2.3. Immunohistochemistry

Immunostaining was carried out as described previously.32 Detailed information is provided in the Appendix S1. The results were evaluated with the German immunohistochemical score (GIS) as previously reported,30, 33, 34 which further divided the patients into low (GIS ≤4) and high (GIS>4) CAVIN1 expression groups, unless mentioned otherwise in the Figure legends.

2.4. Data set preparation for CAVIN1 expression and survival analysis

TCGA, CGGA, Rembrandt, and GSE16011 data sets were used for the analysis ofCAVIN1 expression. Data from TCGA, Rembrandt, and GSE16011 were obtained from GlioVis (https://gliovis.bioinfo.cnio.es/).35 The detailed procedure is provided in the Appendix S1.

2.5. The receiver operator characteristic (ROC) curve and principal components analysis (PCA)

The ROC curve was drawn, and the area under the ROC curve (AUC) of each cutoff was measured in accordance with previous reports.36, 37 PCA was carried out with R (version 3.4.2) (https://cran.r-project.org) to obtain the expression patterns of CAVIN1 in low‐grade glioma (LGG) and GBM.

2.6. Microenvironment cell populations‐counter (MCP‐counter)

Two nonimmune stromal cell (endothelial cells and fibroblasts) and eight immune cell (T cells, CD8+ T cells, NK cells, cytotoxic lymphocytes, B cell lineage, monocytic lineage cells, myeloid dendritic cells, and neutrophils) populations were evaluated by an MCP‐counter method as previously described.38

2.7. Gene ontology (GO) analysis

Patients were stratified into two groups according to the median expression value of CAVIN1 mRNA. The R package limma (https://www.bioconductor.org) was used to identify differentially expressed genes with | log2‐fold change |>1 and adjusted P value <0.05 in four cohorts. Then, the common upregulated and downregulated genes correlated with a high level of CAVIN1 expression were summarized from each data set. GO analysis was performed using DAVID 6.8 (https://david.ncifcrf.gov/tools.jsp).39

2.8. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA)

Gene set enrichment analysis (https://www.broadinstitute.org/gsea/index.jsp) was performed to explore whether the identified sets of genes showed significant differences between the two groups stratified as described above. Normalized enrichment score (NES) and false discovery rate (FDR) were used to determine statistically significant differences.40 GSVA (https://www.bioconductor.org) was applied to further verify significant differences between these two groups, according to the gene sets of defined signaling pathways.41

2.9. Statistical analysis

Microsoft Excel 2013 (Microsoft Inc., Redmond, WA, USA), SPSS 20 (SPSS Inc., Chicago, IL, USA), and GraphPad Prism 7 software (GraphPad Software Inc., La Jolla, CA, USA) were used for statistical analyses. P value <0.05 was defined as indicating statistical significance. The heat maps were made with R (version 3.4.2). Significant quantitative differences between and among groups were determined by a two‐tailed t test and one‐way ANOVA followed by the Tukey posttest, respectively. The univariate and multivariate Cox regression analyses were performed with R (version 3.4.2). A Kaplan‐Meier survival analysis was used to estimate the survival distribution, followed by the log‐rank test to evaluate the difference between stratified groups, using the median value as the cutoff. The relationships between CAVIN1 and human leukocyte antigen (HLA) molecules, immune checkpoint genes, and the 8‐gene local immune‐related risk signature were analyzed with Circos (https://mkweb.bcgsc.ca/tableviewer/visualize/). GISTIC2.0 was applied to evaluate the frequency of copy number alteration (CNA) of CAVIN1 in TCGA.42 Loci with GISTIC values ≥1 or ≤ −1 were defined as an amplification or deletion, respectively.

Additional information regarding the materials and methods are available in the Appendix S1.

3. RESULTS

3.1. The analysis of caveolae‐related genes in glioma identifies CAVIN1 as a gene with increased expression in GBM

We summarized a list of 12 caveolae‐associated genes according to previous reports.43, 44 Then, we compared the expression levels of these genes between LGG and GBM in TCGA RNAseq database, which included 470 LGG and 155 GBM samples. There were seven upregulated genes (CAVIN1, CAV1, CAV2, CD36, EHD2, PRKCDBP, and CAV3) and three downregulated genes (SDPR, MURC, and PACSIN2) in GBM (Figure 1A). Furthermore, we analyzed the data from CGGA, GSE16011, and Rembrandt. CAVIN1, CAV1, CAV2, EHD2, and PRKCDBP were the five overlapping upregulated genes in GBM in the four databases, and SDPR was the only overlapping downregulated gene in this cohort (data not shown). Additionally, we performed a univariate Cox regression analysis with the survival data from those four data sets. The result indicated that CAVIN1 was the only upregulated gene that was correlated with the unfavorable survival of GBM patients in all four of these data sets (Table S1).

Figure 1.

Figure 1

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CAVIN1 expression is elevated in glioma and clinically correlated with poor prognosis in glioma patients. A, A heat map describes the expression levels of caveolae‐associated genes in LGG and GBM according to TCGA RNAseq data. The 12 genes were clustered into three groups (upregulated; downregulated; no significant difference) (LGG, n = 470; GBM, n = 155, t test). B, Western blot analysis of CAVIN1 in the indicated nontumor and glioma samples. GAPDH served as a loading control. CAVIN1 expression was quantified and normalized to GAPDH (right) (nontumor, n = 3; grade II, n = 3; grade III, n = 3; grade IV, n = 3; ****P < 0.0001, ***P < 0.001; one‐way ANOVA). C, Representative western blot result of CAVIN1 in the indicated glioma cells and normal human astrocyte (NHA) samples with GAPDH as a loading control. CAVIN1 expression was quantified and normalized to GAPDH (right) (n = 3; ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05; one‐way ANOVA). D, Representative immunohistochemical images of CAVIN1 in grade II, grade III, and GBM samples. Scale bar, 100 μm. E, Kaplan‐Meier analyses evaluating the correlation between CAVIN1 protein expression and survival of 74 glioma patients (grade II, n = 14; grade III, n = 27; GBM, n = 33; CAVIN1 high vs low, P = 0.035; log‐rank test)

3.2. CAVIN1 is associated with unfavorable prognosis in glioma patients

We next focused on CAVIN1 because of its essential function in multidrug resistance in cancer cells and its identity as a potential outcome biomarker and bona fide glioma gene candidate.26, 45 First, we investigated whether CAVIN1 expression is associated with histopathological grades of glioma. Compared with nontumor and normal human astrocyte (NHA) samples, glioma samples showed upregulation of CAVIN1 expression, especially in GBM and glioma cell line samples (Figure 1B,C). Additionally, we further performed immunohistochemical staining of CAVIN1 on 74 glioma tumor tissues of diverse grades. Elevated expression levels of CAVIN1 could be observed in GBM samples compared with those in LGG samples (Figure 1D and Figure S1A). The data from the immunofluorescence staining also confirmed this result (Figure S1B). Similar results were obtained in the other four data sets (TCGA, Figure S1C; CGGA, Figure S1D; GSE16011, Figure S1E; Rembrandt, Figure S1F). Next, we explored the potential of CAVIN1 as a biomarker for the prediction of glioma patients’ survival. Patients with higher expression of CAVIN1 exhibited poorer prognosis than did those with lower expression of CAVIN1 (Figure 1E, P = 0.0035, and Figure S2A). To further validate these results, we examined the correlation between CAVIN1 and prognosis in glioma patients by using multiple data sets. According to TCGA data, there was a trend indicating that lower expression of CAVIN1 was correlated with better outcome in grade II glioma, although a significant difference was not achieved (Figure S2B). The analysis of TCGA data also revealed that CAVIN1 expression was correlated with poor prognosis in grade III and grade IV (GBM) patients (Figure S2C,D). These results were validated in the CGGA RNAseq, Rembrandt, GSE16011, and TCGA 4502A data sets (Figure S2E‐N). To investigate the correlation of CAVIN1 expression with representative clinical characteristics (age, gender, IDH1 status, radiotherapy, and chemotherapy) in GBM, we conducted Cox regression analyses and found that CAVIN1 was an independent prognostic factor for the overall survival of GBM patients (Table S2). We complemented this analysis with a comparison of the sensitivity and specificity of CAVIN1 as a predictive marker for 1‐year, 3‐year, and 5‐year patient survival with routine prognostic factors (age, grade, IDH1 status, and radiotherapy). The result also revealed that CAVIN1 could be used as a molecule to predict the survival of glioma patients (Figure 2A‐F and Figure S3A‐F). In addition, the CNA frequencies of amplification and deletion of CAVIN1 were identified in TCGA RNAseq database (grade II: amplification 2.21%, deletion 3.10%; grade III: amplification 5.33%, deletion 4.51%; GBM: amplification 11.43%, deletion 12.14%) and TCGA 4502A microarray database (GBM amplification 11.30%, deletion 9.59%) (Figure S4). The data from TCGA RNAseq and 4502A microarray databases showed that there was no mutation of CAVIN1 in glioma. Collectively, these results show that CAVIN1 expression is elevated in malignant glioma and highlight the potential of CAVIN1 as a prognostic marker for glioma.

Figure 2.

Figure 2

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A high level of CAVIN1 expression is associated with progressive malignancy of glioma. A‐F, The receiver operator characteristic (ROC) curve comparing the sensitivity and specificity of CAVIN1 as a diagnostic marker for patient survival in TCGA (A, one‐year survival; B, three‐year survival; C, five‐year survival) and CGGA (D, one‐year survival; E, three‐year survival; F, five‐year survival). G and H, The ROC curve comparing the sensitivity and specificity of CAVIN1 as a diagnostic marker for LGG vs GBM in TCGA (G, AUC: 0.8458, P < 0.0001) and CGGA (H, AUC: 0.8153, P < 0.0001). I and J, The ROC curve comparing the sensitivity and specificity of CAVIN1 as a marker for mesenchymal subtypes vs three other subtypes in TCGA (I, AUC: 0.7870, P < 0.0001) and CGGA (J, AUC: 0.8283, P < 0.0001). K and L, PCA based on the level of CAVIN1 expression‐stratified LGG and GBM patients in TCGA (K) and CGGA (L)

3.3. CAVIN1 is an independent predictive factor for progressive malignancy in GBM and demonstrates a subtype expression preference

We then characterized the expression preference of CAVIN1 in GBM with four previously defined subtypes of GBM.46 According to the TCGA RNAseq data set, CAVIN1 expression in the mesenchymal subtype was significantly higher than that in the proneural and neural subtypes (Figure S5A). However, there was no significant difference in CAVIN1 expression between the mesenchymal and classical subtypes. Moreover, the mesenchymal subtype exhibited a higher expression level of CAVIN1 than did the other three subtypes in the CGGA RNAseq data set (Figure S5B). The results of the ROC curve and AUC measurement confirmed the application of CAVIN1 as a molecule to distinguish between LGG and GBM tumors (Figure 2G,H). Similar AUC values were observed in mesenchymal subtypes vs the other three subtypes (Figure 2I,J). The results of PCA analysis also confirmed the value of CAVIN1 as a marker for recognizing the difference between LGG and GBM (Figure 2K,L). In addition, the data from the Rembrandt, GSE16011, and TCGA 4502A mRNA microarray data sets confirmed the results mentioned above (Figure S5C‐L). Collectively, these data suggest that CAVIN1 is an independent factor for the prediction of progressive malignancy in GBM.

3.4. Higher CAVIN1 expression with IDH1 and EGFR mutations implied poorer prognosis in GBM patients

IDH1, MGMT, and EGFR are three well‐known genes that have important impacts on the survival of GBM patients and their treatment response.47, 48, 49, 50 Based on these observations, we tested the role of CAVIN1 in the prediction of GBM patient outcome with differential IDH1 and EGFR mutation status and MGMT promoter methylation status. The samples were stratified according to the median value of CAVIN1 mRNA expression combined with IDH1 mutation, MGMT promoter methylation, or EGFR mutation status. First, the analysis revealed a higher expression level of CAVIN1 in GBM patients with wild‐type IDH1 than in those with the mutant form (Figure 3A,B). The correlation between CAVIN1 expression and survival of GBM patients with wild‐type IDH1 was also analyzed. Although a significant difference was not achieved, the result showed a tendency for lower levels of CAVIN1 expression to be correlated with better patient survival (Figure 3C, P = 0.0574; D, P = 0.0719). Second, we found that shorter patient survival was accompanied by higher expression of CAVIN1 in patients with wild‐type EGFR (Figure 3E,F and Figure S6A,B), which was consistent with the findings of a recent study.28 Lastly, the analysis combined with MGMT promoter methylation status demonstrated that there was no significant difference in CAVIN1 expression between the groups with methylated and unmethylated promoters (Figure S6C,D). In addition, the data from TCGA RNAseq did not confirm the association between CAVIN1 and survival of GBM patients, although there was a significant difference in TCGA 4502A mRNA microarray data set (Figure 3G,H and Figure S6E,F). Collectively, higher CAVIN1 expression with IDH1 and EGFR mutation implied poorer prognosis in GBM patients.

Figure 3.

Figure 3

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The CAVIN1 expression level enhances the predictive value of IDH1 or EGFR mutation status and MGMT promoter methylation status and confers different responses to radiotherapy. A and B, The comparison of CAVIN1 expression between the groups with wild‐type and mutant IDH1 in TCGA (A, IDH1 WT, n = 142; Mut, n = 8) and CGGA (B, IDH1 WT, n = 93; Mut, n = 29) (****, P < 0.0001; t test). C and D, The Kaplan‐Meier analyses of patients with wild‐type IDH1 survival data from TCGA (C) and CGGA (D) RNAseq databases according to CAVIN1 expression (log‐rank test). E and F, The Kaplan‐Meier analyses of patients with wild‐type EGFR survival data from TCGA 4502A mRNA microarray (E) and CGGA RNAseq databases (F) according to CAVIN1 expression (log‐rank test). G and H, The Kaplan‐Meier analyses of patients with MGMT methylated promoter survival data from TCGA RNAseq (G) and 4502A mRNA microarray databases (H) according to CAVIN1 expression (log‐rank test). I and J, The Kaplan‐Meier analyses of survival of glioma patients treated with radiotherapy from TCGA (I) and CGGA (J) according to CAVIN1 expression (log‐rank test)

3.5. CAVIN1 expression suggests different responses to radiotherapy

Radio‐ and chemotherapy were the two main postsurgical treatments for GBM patients.4 We conducted survival analyses to examine whether CAVIN1 could serve as a marker for the prediction of the response to radiation and chemotherapy. The samples in TCGA and CGGA were divided into high and low CAVIN1 groups based on the median value of CAVIN1 mRNA expression and treatment. The patients receiving radiation in the low CAVIN1 group had a survival advantage compared with the patients receiving radiation in the high group (Figure 3I,J and Figure S6G‐I). In terms of the survival of patients receiving chemotherapy, the result from TCGA RNAseq data set showed that a higher expression level of CAVIN1 was associated with shorter patient survival (Figure S6J). However, the results from the CGGA RNAseq and TCGA 4502A microarray data set did not confirm this observation (Figure S6K,L). Taken together, these results prove the value of CAVIN1 as a marker for the prediction of response to radiation in GBM.

3.6. Association of CAVIN1 expression with immune and stromal cell populations in the glioma microenvironment

The tumor microenvironment of glioma contains noncancerous cell types including immune and stromal cells, which contribute actively to the regulation of tumor progression and therapeutic response via extensive cross talk with glioma cells in the tumor microenvironment.51 By using the MCP‐counter method38 with TCGA and CGGA RNAseq data, we evaluated the correlations between CAVIN1 and two nontumor cell populations, the immune and stromal cells. The results showed that there was a negative correlation between CAVIN1 expression and cytotoxic lymphocytes and a positive correlation between CAVIN1 expression and fibroblasts (Figure 4A,B).

Figure 4.

Figure 4

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The correlation of CAVIN1 with immune cell and stromal cell populations in GBM. A and B, Clustering of MCP‐counter scores for the correlation of CAVIN1 with immune and nonimmune stromal cell populations in GBM according to TCGA (A) and CGGA (B) RNAseq databases

3.7. CAVIN1 is associated with dysregulated angiogenesis and immune response in the tumor microenvironment of GBM

To illustrate the functions and related signaling pathways of CAVIN1, we summarized the overlapping down‐ and upregulated genes with high CAVIN1 expression from four data sets. There were 41 downregulated and 141 upregulated genes (Figure S7 and Table S3). Based on these two groups of genes, we performed GO analysis to summarize a list of signaling pathways related to a high level of CAVIN1 expression as shown in Figure 5A,B. Moreover, GSVA further confirmed that 10 GO terms were associated with a high level of CAVIN1 (angiogenesis, immune response, inflammatory response, transforming growth factor beta receptor signaling pathway, vascular endothelial growth factor receptor signaling pathway, leukocyte migration, response to lipid, extracellular matrix assembly, regulation of cell adhesion, and regulation of intracellular signal transduction) (Figure 5C,D). Combined with the critical role of angiogenesis and immune response in the progression and recurrence of GBM, we further focused on the correlation analysis between CAVIN1 and these two phenotypes. The results from GSEA indicated an enrichment of the angiogenesis and immune response phenotype (Figure 6A,B). The data from the PCA also showed the different angiogenesis and immune response phenotypes in the low and high CAVIN1 expression groups (Figure 6C,D and Figure S8A,B). We further summarized the list of genes associated with a high expression level of CAVIN1 in angiogenesis and immune response phenotypes (Figure 6E,F and Table S4). Moreover, due to the crucial functions of HLA family members and immune checkpoint genes in the regulation of the immune response, we performed Pearson analysis to investigate the correlations among CAVIN1 and these genes. Strong correlations between CAVIN1 and molecules, including PDL1, HLA A, HLA B, HLA E, and HLA H were confirmed (Figure 6G,H and Table S5). In addition, our previous study identified a local immune‐related risk signature that consisted of eight genes (FOXO3, IL6, IL10, ZBTB16, CCL18, AIMP1, FCGR2B, and MMP9).52 We evaluated the correlation between this gene set and CAVIN1. The results obtained from both TCGA and CGGA revealed a correlation of CAVIN1 with this immune‐related gene signature and angiogenesis‐associated genes in GBM (Figure 6I,J and Figure S8C,D). Collectively, the elevated expression of CAVIN1 may be associated with dysregulated angiogenesis and immune response in the microenvironment of GBM.

Figure 5.

Figure 5

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The functional annotation of CAVIN1 in GBM. A and B, GO analysis of related signaling pathways according to overlapping upregulated (A) and downregulated genes (B) associated with a high level of CAVIN1 expression. C and D, Clustering of GSVA scores and analyses of signaling pathways associated with a high level of CAVIN1 expression in GBM according to TCGA (C) and CGGA (D) RNAseq databases

Figure 6.

Figure 6

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A high level of CAVIN1 expression is associated with the regulation of angiogenesis and immune response in GBM. A and B, GSEA analyses of TCGA (A) and CGGA (B) GBM RNAseq data revealed that a significant enrichment of the angiogenesis‐ (left panel) and immune response‐related (right panel) phenotypes in GBM patients with a high level of CAVIN1 expression. C and D, PCA of TCGA (C) and CGGA (D) GBM RNAseq data revealed that angiogenesis and immune response (GO terms: ANGIOGENESIS and IMMUNE RESPONSE) were correlated with a high level of CAVIN1 expression. E and F, Heat maps of TCGA (E) and CGGA (F) GBM RNAseq data showed the genes associated with CAVIN1 expression in angiogenesis and immune response phenotypes. G and H, The correlation analysis between CAVIN1 and representative HLA family members and immune checkpoint genes in TCGA (G) and CGGA (H) GBM RNAseq databases. I and J, The correlation analysis between CAVIN1 and the 8‐gene local immune‐related risk signature in TCGA (I) and CGGA (J) GBM RNAseq databases

4. DISCUSSION

GBM is characterized by its infiltrative growth pattern and recurrence even after aggressive resection and a combination of chemo‐ and radiotherapy. Thus, identifying potential prognostic and therapeutic targets for glioma is urgent. To date, increasing evidence shows that caveolae play a crucial role in multiple signaling and cell functions.53 However, the role of caveolae remains insufficiently characterized in the regulation of cancer‐related processes. Here, we employed bioinformatics methods to interpret mRNA expression and survival data of caveolae‐related genes from an integrated cohort of four data sets and identified CAVIN1 as the caveolae‐related gene with the greatest increase in expression in GBM. We evaluated the expression of the CAVIN1 protein in glioma tissue, and interestingly, CAVIN expression was not only significantly elevated in glioma tissues but also associated with devastating outcomes in glioma patients. This finding indicated the potential of CAVIN1 as a marker for the prediction of patient survival in glioma and was consistent with previous reports, although it was based on a larger sample size. Additionally, we proved that CAVIN1 served as an independent risk factor for progressive malignancy. A higher level of CAVNI1 expression meant reduced survival time in GBM patients who received radiotherapy. This finding suggested that CAVIN1 might act as a molecule for the response prediction to radiotherapy in GBM, and patients may benefit from therapeutic strategies combining routine radiation and inhibition of CAVIN1. Moreover, the incorporation of CAVIN1 expression with detection of the MGMT promoter, IDH1 status, or EGFR status could more accurately predict survival in GBM with MGMT promoter methylation, and GBM without IDH1 or EGFR mutation. This result also implied that targeting CAVIN1 may provide novel insight for GBM treatment, although further study is needed to investigate the related mechanisms.

Another important set of findings in this study is that CAVIN1 expression was closely related to stromal and immune cell populations, especially fibroblasts and cytotoxic lymphocytes. The bioinformatics analysis of the upregulated genes related to high CAVIN1 expression revealed a previously undefined association between CAVIN1 and regulation of angiogenesis and local immune response. Based on these observations, we hypothesized that CAVIN1 may participate in regulating local neovascularization and immune response in the local microenvironment of GBM. Due to its important role in the progression of glioma, CAVIN1 may serve as a novel candidate molecule for developing new antiangiogenic and immunologic strategies against glioma. However, future studies are still needed to deeply clarify the underlying mechanisms of how CAVIN1 regulates the local microenvironment of GBM and maintains therapeutic resistance.

In conclusion, we first profiled the expression pattern and prognostic value of caveolae component proteins in glioma, which identified an elevated expression of CAVIN1 and its potential as a marker to predict patient outcomes in GBM. Second, CAVIN1 not only was preferentially expressed in GBM patients with wild‐type IDH1 but also showed a mesenchymal subtype preference. Interestingly, we found a correlation between CAVIN1 expression and longer survival in GBM patients receiving radiotherapy. Finally, we identified an association of CAVIN1 with the regulation of local disrupted angiogenesis and immune response in GBM. Taken together, these results indicate CAVIN1’s potential as a prognostic biomarker in GBM and its possible use as a therapeutic molecule for the targeted therapy of GBM.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Supporting information

 

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ACKNOWLEDGMENTS

We thank all the members in Dr. A. H. Wu's lab for the helpful discussion about our study. This work was supported by 1. National Natural Science Foundation of China (Grant no. 30901781, 81172409 and 81472360); 2. Liaoning Science and Technology Plan Projects (Grant no. 2012225014 and 2011225034).

Guo Q, Guan G‐F, Cheng W, et al. Integrated profiling identifies caveolae‐associated protein 1 as a prognostic biomarker of malignancy in glioblastoma patients. CNS Neurosci Ther. 2019;25:343–354. 10.1111/cns.13072

Contributor Information

Peng Cheng, Email: chengpengcmu@sina.com.

An‐Hua Wu, Email: wuanhua@yahoo.com.

REFERENCES

  • 1. Ricard D, Idbaih A, Ducray F, et al. Primary brain tumours in adults. Lancet. 2012;379:1984‐1996. [DOI] [PubMed] [Google Scholar]
  • 2. Sun LH, Yang FQ, Zhang CB, et al. Overexpression of paxillin correlates with tumor progression and predicts poor survival in glioblastoma. CNS Neurosci Ther. 2017;23:69‐75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Wen PY, Kesari S. Malignant gliomas in adults. N Engl J Med. 2008;359:492‐507. [DOI] [PubMed] [Google Scholar]
  • 4. Stupp R, Hegi ME, Mason WP, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5‐year analysis of the EORTC‐NCIC trial. Lancet Oncol. 2009;10:459‐466. [DOI] [PubMed] [Google Scholar]
  • 5. Chinot OL, Wick W, Mason W, et al. Bevacizumab plus radiotherapy‐temozolomide for newly diagnosed glioblastoma. N Engl J Med. 2014;370:709‐722. [DOI] [PubMed] [Google Scholar]
  • 6. Hill MM, Bastiani M, Luetterforst R, et al. PTRF‐Cavin, a conserved cytoplasmic protein required for caveola formation and function. Cell. 2008;132:113‐124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Nassar ZD, Hill MM, Parton RG, et al. Caveola‐forming proteins caveolin‐1 and PTRF in prostate cancer. Nat Rev Urol. 2013;10:529‐536. [DOI] [PubMed] [Google Scholar]
  • 8. Lin Y, Wang P, Liu YH, et al. DT(270–326), a truncated diphtheria toxin, increases blood‐tumor barrier permeability by upregulating the expression of caveolin‐1. CNS Neurosci Ther. 2016;22:477‐487. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Kovtun O, Tillu VA, Ariotti N, et al. Cavin family proteins and the assembly of caveolae. J Cell Sci. 2015;128:1269‐1278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Jansa P, Mason SW, Hoffmann‐Rohrer U, et al. Cloning and functional characterization of PTRF, a novel protein which induces dissociation of paused ternary transcription complexes. EMBO J. 1998;17:2855‐2864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Liu L, Pilch PF. A critical role of cavin (polymerase I and transcript release factor) in caveolae formation and organization. J Biol Chem. 2008;283:4314‐4322. [DOI] [PubMed] [Google Scholar]
  • 12. Stoeber M, Schellenberger P, Siebert CA, et al. Model for the architecture of caveolae based on a flexible, net‐like assembly of Cavin1 and Caveolin discs. Proc Natl Acad Sci U S A. 2016;113:E8069–E8078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Hayashi YK, Matsuda C, Ogawa M, et al. Human PTRF mutations cause secondary deficiency of caveolins resulting in muscular dystrophy with generalized lipodystrophy. J Clin Invest. 2009;119:2623‐2633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Govender P, Romero F, Shah D, et al. Cavin1; a regulator of lung function and macrophage phenotype. PLoS One. 2013;8:e62045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Liu L, Brown D, McKee M, et al. Deletion of Cavin/PTRF causes global loss of caveolae, dyslipidemia, and glucose intolerance. Cell Metab. 2008;8:310‐317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Bai L, Deng X, Li J, et al. Regulation of cellular senescence by the essential caveolar component PTRF/Cavin‐1. Cell Res. 2011;21:1088‐1101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Thommen DS, Schuster H, Keller M, et al. Two preferentially expressed proteins protect vascular endothelial cells from an attack by peptide‐specific CTL. J Immunol. 2012;188:5283‐5292. [DOI] [PubMed] [Google Scholar]
  • 18. Nassar ZD, Moon H, Duong T, et al. PTRF/Cavin‐1 decreases prostate cancer angiogenesis and lymphangiogenesis. Oncotarget. 2013;4:1844‐1855. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Wang F, Zheng Y, Orange M, et al. PTRF suppresses the progression of colorectal cancers. Oncotarget. 2017;8:48650‐48659. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Bai L, Deng X, Li Q, et al. Down‐regulation of the cavin family proteins in breast cancer. J Cell Biochem. 2012;113:322‐328. [DOI] [PubMed] [Google Scholar]
  • 21. Gamez‐Pozo A, Sanchez‐Navarro I, Calvo E, et al. PTRF/cavin‐1 and MIF proteins are identified as non‐small cell lung cancer biomarkers by label‐free proteomics. PLoS One. 2012;7:e33752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Das K, Taguri M, Imamura H, et al. Genomic predictors of chemotherapy efficacy in advanced or recurrent gastric cancer in the GC0301/TOP002 phase III clinical trial. Cancer Lett. 2018;412:208‐215. [DOI] [PubMed] [Google Scholar]
  • 23. Huertas‐Martinez J, Court F, Rello‐Varona S, et al. DNA methylation profiling identifies PTRF/Cavin‐1 as a novel tumor suppressor in Ewing sarcoma when co‐expressed with caveolin‐1. Cancer Lett. 2017;386:196‐207. [DOI] [PubMed] [Google Scholar]
  • 24. Liu L, Xu HX, Wang WQ, et al. Cavin‐1 is essential for the tumor‐promoting effect of caveolin‐1 and enhances its prognostic potency in pancreatic cancer. Oncogene. 2014;33:2728‐2736. [DOI] [PubMed] [Google Scholar]
  • 25. Faggi F, Chiarelli N, Colombi M, et al. Cavin‐1 and Caveolin‐1 are both required to support cell proliferation, migration and anchorage‐independent cell growth in rhabdomyosarcoma. Lab Invest. 2015;95:585‐602. [DOI] [PubMed] [Google Scholar]
  • 26. Yi JS, Mun DG, Lee H, et al. PTRF/cavin‐1 is essential for multidrug resistance in cancer cells. J Proteome Res. 2013;12:605‐614. [DOI] [PubMed] [Google Scholar]
  • 27. Wang X, Liu T, Bai Y, et al. Polymerase I and transcript release factor acts as an essential modulator of glioblastoma chemoresistance. PLoS One. 2014;9:e93439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Huang K, Fang C, Yi K, et al. The role of PTRF/Cavin1 as a biomarker in both glioma and serum exosomes. Theranostics. 2018;8:1540‐1557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Wang W, Zhao Z, Wu F, et al. Bioinformatic analysis of gene expression and methylation regulation in glioblastoma. J Neurooncol. 2017. [DOI] [PubMed] [Google Scholar]
  • 30. Wang J, Cheng P, Pavlyukov MS, et al. Targeting NEK2 attenuates glioblastoma growth and radioresistance by destabilizing histone methyltransferase EZH2. J Clin Invest. 2017;127:3075‐3089. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  • 31. Liu DM, Wang HJ, Han B, et al. CAMKK2, regulated by promoter methylation, is a prognostic marker in diffuse gliomas. CNS Neurosci Ther. 2016;22:518‐524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Cheng P, Wang J, Waghmare I, et al. FOXD1‐ALDH1A3 signaling Is a determinant for the self‐renewal and tumorigenicity of mesenchymal glioma stem cells. Cancer Res. 2016;76:7219‐7230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Hutterer M, Knyazev P, Abate A, et al. Axl and growth arrest‐specific gene 6 are frequently overexpressed in human gliomas and predict poor prognosis in patients with glioblastoma multiforme. Clin Cancer Res. 2008;14:130‐138. [DOI] [PubMed] [Google Scholar]
  • 34. Remmele W, Schicketanz KH. Immunohistochemical determination of estrogen and progesterone receptor content in human breast cancer. Computer‐assisted image analysis (QIC score) vs. subjective grading (IRS). Pathol Res Pract. 1993;189:862‐866. [DOI] [PubMed] [Google Scholar]
  • 35. Bowman RL, Wang Q, Carro A, et al. GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro Oncol. 2017;19:139‐141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Yoshihara K, Shahmoradgoli M, Martinez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Robin X, Turck N, Hainard A, et al. pROC: an open‐source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Becht E, Giraldo NA, Lacroix L, et al. Estimating the population abundance of tissue‐infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17:218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44‐57. [DOI] [PubMed] [Google Scholar]
  • 40. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge‐based approach for interpreting genome‐wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545‐15550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA‐seq data. BMC Bioinformatics. 2013;14:7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Mermel CH, Schumacher SE, Hill B, et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy‐number alteration in human cancers. Genome Biol. 2011;12:R41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Hansen CG, Bright NA, Howard G, et al. SDPR induces membrane curvature and functions in the formation of caveolae. Nat Cell Biol. 2009;11:807‐814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Martinez‐Outschoorn UE, Sotgia F, Lisanti MP. Caveolae and signalling in cancer. Nat Rev Cancer. 2015;15:225‐237. [DOI] [PubMed] [Google Scholar]
  • 45. Wang Q, Hu B, Hu X, et al. Tumor evolution of glioma‐intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell. 2017;32(42–56):e46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Verhaak RG, Hoadley KA, Purdom E, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17:98‐110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Yan H, Parsons DW, Jin G, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360:765‐773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Hegi ME, Diserens AC, Gorlia T, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352:997‐1003. [DOI] [PubMed] [Google Scholar]
  • 49. Gan HK, van den Bent M, Lassman AB, et al. Antibody‐drug conjugates in glioblastoma therapy: the right drugs to the right cells. Nat Rev Clin Oncol. 2017;14:695‐707. [DOI] [PubMed] [Google Scholar]
  • 50. Wei JW, Cai JQ, Fang C, et al. Signal peptide peptidase, encoded by HM13, contributes to tumor progression by affecting EGFRvIII secretion profiles in glioblastoma. CNS Neurosci Ther. 2017;23:257‐265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Quail DF, Joyce JA. The microenvironmental landscape of brain tumors. Cancer Cell. 2017;31:326‐341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Cheng W, Ren X, Zhang C, et al. Bioinformatic profiling identifies an immune‐related risk signature for glioblastoma. Neurology. 2016;86:2226‐2234. [DOI] [PubMed] [Google Scholar]
  • 53. Parat MO, Riggins GJ. Caveolin‐1, caveolae, and glioblastoma. Neuro Oncol. 2012;14:679‐688. [DOI] [PMC free article] [PubMed] [Google Scholar]

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