Expression characteristics and prognostic significance of ITGA3 in glioma are more prominent than other integrins
Integrins produce an important effect in cancer biology, and some subunits are considered potential targets for tumor therapy(21).To screen for integrin subunits that impact the occurrence and progression of glioma, we first used public databases to compare the expression of all integrins in normal brain tissues and GBM samples. the expression levels of most integrins in GBM were significantly higher than that in normal brain tissue in the GSE4290, REMBRANDT, and GSE59612 datasets (Figure 1A−C). In these three datasets, the number of non-tumor brain tissues was greater than 15, which strongly illustrated the expression characteristics of integrins in GBM and normal brain tissues. Eleven significantly differently expressed overlapped integrin subunits in the three datasets were selected for further analysis (Figure 1D). In the TCGA cohort, we conducted univariate Cox regression analysis using the 11 integrin subunits and found that the expression level of ITGA3 was most significantly correlated with the OS of all glioma patients as well as GBM patients (Figure 1E−F). Therefore, we further investigated the role of ITGA3 in glioma.
ITGA3 predicts poor prognosis in glioma
Based on the results of the above univariate Cox regression analysis, we believed that the expression level of ITGA3 was correlated with the prognosis of glioma patients. To confirm this, we used the Kaplan–Meier method to perform survival analysis based on ITGA3 expression in the TCGA, CGGA, and GSE16011 databases. The results of the survival analysis were consistent with those of the univariate Cox analysis. In all glioma patients, the higher the expression level of ITGA3, the worse the prognosis of the patient (Figure 2A−C). In GBM and LGG, ITGA3 had the same prognostic significance (Figure 2D−I), indicating that ITGA3 could act as a prognostic indicator for glioma patients. In addition, glioma patients receiving radiotherapy or chemotherapy in the high ITGA3 expression group had a worse outcome than patients in the low expression group in the TCGA and CGGA datasets (Supplementary Figure 1A−D). These results further conformed that ITGA3 could act as a biomarker in predicting the sensitivity of glioma patients to radiotherapy and chemotherapy.
ITGA3 expression is enriched in aggressive subtypes of glioma
We have found that the expression level of ITGA3 in GBM was higher compared with non-tumor brain tissue. We further evaluated the expression of ITGA3 in different grades of gliomas in TCGA, CGGA, CGGA (mRNA-array), and GSE16011 databases. We found that the higher the grade of glioma, the higher the expression level of ITGA3 (Figure 3A−D). Integrated genomic analysis grouped GBM into proneural (PN), neural (NE), classical (CL), and mesenchymal (ME) transcriptomic subtypes(22, 23). Different transcriptomic subtypes have different prognoses for glioma, among which the NE subtype has a relatively favorable prognosis and the ME subtype has a relatively poor outcome. Because the expression level of ITGA3 was significantly related to the prognosis of glioma, we further detected the expression characteristics of ITGA3 in different transcriptomic subtypes. As shown in Figure 3E−H, the expression level of ITGA3 in the ME subtype was significantly higher than that in the other three subtypes in TCGA, CGGA, GSE16011, and REMBRANDT datasets. In addition to transcriptomic subtypes, IDH mutation and chromosome 1p19q codeletion status also played a critical role in the occurrence and progression of glioma(24, 25). In TCGA, CGGA, GSE16011, and Kamoun datasets, we discovered that the expression level of ITGA3 was not only higher in IDH wild-type gliomas compared with IDH mutant gliomas (Figure 3I−L), but also higher in 1p19q non-codeletion gliomas than that in codeleted gliomas (Figure 3M−Q). These results suggested that the expression level of ITGA3 was correlated with the malignant subtype of glioma. These results also explained the correlation between the expression level of ITGA3 and the prognosis of glioma. However, in the CGGA cohort, there was no significant difference in the expression of ITGA3 and MGMT promoter methylation status (data not shown).
To explore whether ITGA3 could be a diagnostic marker for glioma, we used the GETxPortal (https://www.gtexportal.org/home/documentationPage) to compare the expression of ITGA3 in different normal tissues of the human body. We found that the expression level of ITGA3 in the central nervous system was lower than that in most other tissues (Supplementary Figure 2A). We also compared the expression of ITGA3 in different tumor cell lines using the cancer cell line encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle). The expression level of ITGA3 in glioma cell lines was noticeably higher than that of most other tumor cell lines. These results indicated that ITGA3 was closely linked with the occurrence and progression of glioma, and ITGA3 might serve as a diagnostic marker for glioma. To further confirm the expression characteristics of ITGA3 in glioma, we tested the mRNA and protein expression level of ITGA3 in glioma samples and glioma cell lines. As shown in Figure 4A−B and Supplementary Figure 2C−G, the mRNA and protein expression levels of ITGA3 in glioma samples were evidently higher than those in normal brain tissue, and as the grade of glioma increased, ITGA3 expression was upregulated. In addition, the mRNA and protein expression levels of ITGA3 in different glioma cell lines (T98, LN229, U373, U251, and U87) were also significantly higher than that in NHA cells (Figure 4C−D). Immunohistochemical detection of ITGA3 protein expression levels in different grade glioma specimens and normal brain tissue samples was consistent with qPCR and western blotting results (Figure 4E). These results indicated that ITGA3 was preferentially expressed in aggressive subtypes of glioma, and thus may be useful as a diagnostic marker for glioma.
Relationship between ITGA3 and tumor purity of glioma
Our previous study found that the purity of glioma was closely related to the clinical and pathological characteristics of glioma, and could accurately predict prognosis(26). An important factor affecting the purity of glioma is the content of immune cells in the glioma microenvironment. ITGA3 is a transmembrane protein that can transduce and regulate signals inside and outside the cell. Therefore, we speculated that ITGA3 might be linked to the purity of glioma. The ESTIMATE score analysis suggested that the expression of ITGA3 was significantly positively correlated with ESTIMATE, immune, and stromal scores, but negatively correlated with tumor purity of gliomas in TCGA, CGGA, and GSE16011 datasets (Figure 5A−H, Supplementary Figure 3A−D). Correlation analysis between the expression of ITGA3 and the degree of enrichment of immune cells found that ITGA3 was positively correlated with the degree of enrichment of most immune cells in TCGA, CGGA, and GSE16011 cohorts (Figure 5I−J, Supplementary Figure 3E). Among the immune cells whose enrichment degree was positively correlated with the expression of ITGA3 and the correlation coefficient was greater than 0.4 in the three cohorts, there were six overlapped immune cell types in TCGA, CGGA, and GSE16011 datasets (Figure 5K). Figure 5L−M and Supplementary Figure 3F show the relationship between the enrichment of these six types of immune cells and the expression level of ITGA3 in TCGA, CGGA, and GSE16011 datasets. It had been confirmed that high immune cell infiltration resulted in a more unfavorable prognosis in glioma(27). These results indicated that ITGA3 reduced the purity of glioma by promoting the enrichment of immune cells in the glioma microenvironment, thereby affecting the prognosis of glioma.
ITGA3 is closely related to immune checkpoints in glioma
Among the six overlapped types of immune cells (Figure 5K−M and Supplementary Figure 2F), all are anti-tumor immunocytes except plasmacytoid dendritic cells, which have a tumor-promoting function. But the outcome of glioma patients with high ITGA3 expression was worse. This might be because ITGA3 could promote anti-tumor immunosuppression in glioma. Considering the important role of immune checkpoints in promoting tumor immunosuppression(28-30), we hypothesized that ITGA3 could promote anti-glioma immunosuppression by regulating the expression of immune checkpoints. To verify this hypothesis, we assessed the correlation between ITGA3 and immune checkpoints. Expression of ITGA3 was significantly positively correlated with the expression level of most immune checkpoints in TCGA, CGGA, and GSE16011 cohorts (Figure 6A−B, Supplementary Figure 4A). There were six immune checkpoints (CD276, NRP1, CD274, CD44, TNFRSF14, and CD40) with correlation coefficients greater than 0.4 that overlapped in TCGA, CGGA, and GSE16011 datasets (Figure 6C). The correlation between these immune checkpoints and ITGA3 is also shown in Figure 6D−O, and Supplementary Figure 4B−G. The correlation between ITGA3 and immune checkpoints indicated that ITGA3 could inhibit anti-glioma immunity by promoting the expression of immune checkpoints.
ITGA3-related immune signatures in glioma
Because ITGA3 could affect tumor purity and regulated the expression of immune checkpoints, it might also influence other aspects of glioma immunity. To determine the ITGA3 related immune signature in glioma, gene sets associated with the immune response (http://amigo.geneontology.org/amigo/landing) were filtered out. The optimal correlated genes to ITGA3 (Pearson’s |r| > 0.4) were determined, including 352, 401, and 403 genes in TCGA, CGGA, and GSE16011 datasets, respectively (Figure 7A−B, Supplementary Figure 5A). GO analysis was employed to identify the biofunctions of the overlapped genes positively related to ITGA3 in TCGA, CGGA, and GSE6011 (Supplementary Figure 5B). The results indicated that these genes were significantly enriched in GO biological processes related to neutrophil regulation, T cell, and leucocyte function in immune response (Figure 7C). GSEA analysis also found that innate immune response, inflammatory response, and adaptive immune response were highly enriched in ITGA3 high-expressing gliomas in TCGA, CGGA, and GSE16011 databases (Figure 8A−C). These results suggested that ITGA3 played a vital role in immune response in glioma.
ITGA3 regulates the EMT of glioma cells
To further explore the role of ITGA3 in glioma, we performed GSEA analysis based on hallmark gene sets. GSEA analysis revealed that the EMT was highly enriched in ITGA3 high-expressing gliomas in TCGA, CGGA, and GSE16011 cohorts (Figure 9A−C). To further confirm the effect of ITGA3 on the EMT of glioma cells, we assessed the migration and invasion potential of glioma cells after knocking down ITGA3. Wound healing and Transwell assays revealed that the migration and invasion ability of glioma cells significantly decreased after silencing ITGA3 (Figure 9D−F). In addition, the expression of ITGA3 was significantly positively correlated with the expression levels of the EMT markers (Vimentin, N-Cadherin, Claudin-1, SNAI1) in TCGA, CGGA, and GSE16011 datasets (Supplementary Figure 6A−L). In U87 and U251 cells, the expression levels of EMT markers (N-Cadherin, β-Catenin, Vimentin, SNAIL, Claudin-1) were significantly downregulated after silencing of ITGA3, while the expression of E-Cadherin was upregulated (Figure 9G). These results confirmed that ITGA3 could regulate the EMT of glioma cells.
ITGA3 promotes angiogenesis in glioma
In addition to the EMT, GSEA analysis also found that angiogenesis was highly enriched in ITGA3 high-expressing gliomas (Figure 10A−C). The tube formation capacity of HBMECs induced with glioma cell-conditioned medium was also significantly suppressed following knockdown of ITGA3 in glioma cells (Figure 10D). Glioma cells can produce many pro-angiogenic factors such as vascular endothelial growth factor (VEGF), angiopoietin and pleiotropic factors, pleiotrophin and transforming growth factor-β (TGF-β)(31, 32). To further explore the relationship between ITGA3 and angiogenesis within the glioma microenvironment, we evaluated the relationship between ITGA3 and the pro-angiogenic factors (VEGFA, TGFB1, PDGFA, and ANGPT2) in TCGA, CGGA, and GSE16011 datasets. Expression of ITGA3 in gliomas was significantly positively correlated with the expression levels of these pro-angiogenic factors (Supplementary Figure 7A−L). In addition, after silencing ITGA3 in glioma cells, the expression levels of VEGFA and TGFB1 were also significantly reduced (Figure 10E). The above results indicated that ITGA3 regulated angiogenesis in the glioma microenvironment by regulating the expression of pro-angiogenic factors in glioma cells.
Silencing ITGA3 inhibits autophagy in glioma cells
Many studies have regarded ITGA3 as an autophagy-related gene(33). However, whether ITGA3 can regulate autophagy in glioma cells is still unclear. We used glioma cells stably transfected with RFP-GFP-LC3 to explore the effect of ITGA3 on autophagy. After knockdown of ITGA3 in glioma cells stably transfected with RFP-GFP-LC3, the numbers of yellow puncta were significantly reduced (Figure 11A−B). In addition, in U87 and U251 cells, silencing ITGA3 significantly inhibited the expression of LC3-II (Figure 11C). These results indicated that ITGA3 could promote autophagy in glioma cells.
Given that ITGA3 played an important role in glioma, we further explored the signaling pathways through which it produces these effects. GSEA analysis found that ERK1/2 and PI3K-AKT-mTOR signaling pathways were significantly enriched in ITGA3 high-expressing gliomas in TCGA, CGGA, and GSE16011 datasets (Figure 11D−F, Supplementary Figure 8A−F). In vitro experiments also found that silencing ITGA3 inhibited the phosphorylation of ERK1/2 (Figure 11G−H).