ITGA3 Is a Reliable Biomarker and Putative Therapeutic Target for Glioma

doi:10.21203/rs.3.rs-131679/v1

Download PDF

Research

ITGA3 Is a Reliable Biomarker and Putative Therapeutic Target for Glioma

https://doi.org/10.21203/rs.3.rs-131679/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Glioma is the most prevalent and malignant primary central nervous system tumor in adults. As a member of the integrin alpha chain family of proteins, integrin subunit alpha 3 (ITGA3) has been found to play a critical role in the occurrence and progression of several cancers, including lung, ovarian, and pancreatic cancers. However, the role of ITGA3 in glioma remains unclear.

Methods: The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), REMBRANDT, GSE16011, GSE59612, and GSE4290 datasets were used to analyze relevant characteristics of ITGA3 in glioma. R language and GraphPad Prism 7.00 were employed as major tools for statistical analysis and graph manipulation.

Results: We identified that ITGA3 expression was upregulated in glioma and related to unfavorable outcomes of glioma patients. Expression of ITGA3 also tended to be enriched in aggressive subtypes of glioma. We demonstrated that expression of ITGA3 was negatively correlated with glioma purity. In gliomas with high ITGA3 expression, the anti-glioma immune response was inhibited. ITGA3 also regulated angiogenesis within the glioma microenvironment and promoted the epithelial–mesenchymal transition (EMT) and autophagy of glioma cells. GSEA analysis revealed that ITGA3 could activate ERK1/2 and PI3K/AKT/mTOR pathways.

Conclusion: Our data suggested that ITGA3 promoted the malignant progression of glioma by regulating the immunity of glioma as well as the EMT and autophagy of glioma cells, which could act as a therapeutic target for glioma.

Translational Medicine

ITGA3

prognosis

tumor purity

EMT

angiogenesis

autophagy

glioma

Gliomas, including low grade glioma (LGG) and glioblastoma (GBM), are the most frequent, invasive, and malignant primary tumors of the brain, with a grave prognosis and limited treatment methods available(1, 2). Although great efforts have been made in surgery, chemotherapy, radiotherapy, and combination therapy to improve therapeutic efficacy, the outcome of glioma remains dismal; the median survival time for GBM is less than 15 months(3). Therefore, to improve treatment effects, it is imperative to discover novel therapeutic targets and develop more effective strategies for glioma treatment.

Integrins are multifunctional heterodimeric transmembrane receptor molecules that generally consist of non-covalently combined α and β subunits. These molecules play a vital role in signal transduction and homeostasis by mediating the interaction between cells and of these cells with the extracellular matrix(4). Growing evidence indicates that integrins and integrin-related biological process play critical roles in regulating tumor stem-like properties, cancer invasion, and treatment resistance(5).

As a member of the family of integrin subunits, ITGA3 has been correlated with unfavorable prognoses in cancers such as non-small cell lung cancer(6), ovarian cancer(7), and pancreatic adenocarcinoma(8). In addition, ITGA3 has been found to regulate cancer cell migration and invasion in bladder cancer(9), colorectal cancer(10) and head and neck cancer(11). In nasopharyngeal carcinoma, ITGA3 was shown to simultaneously regulate the migration of tumor cells and angiogenesis within the tumor microenvironment(12). In ovarian cancer, the monoclonal antibody OV-Ab 30 − 7, which specifically neutralizes ITGA3, restrained tumor progression and lengthened the survival of tumor-bearing mice, indicating that ITGA3 might be a potential therapeutic target for ovarian cancer(13). Although accumulating studies have begun to focus on the role of ITGA3 in tumors, its specific role and underlying mechanisms are still unclear. In particular, very little is known about the role of ITGA3 in gliomas.

In this study, we identified ITGA3 as a prognostic factor in glioma and revealed that its expression is enriched in aggressive subtypes of glioma. We also found that ITGA3 was closely related to the immune microenvironment of glioma and could regulate EMT and autophagy of glioma cells.

Data source

The Cancer Genome Atlas (TCGA) glioma RNA sequencing data, as well as clinical and pathological information were obtained from TCGA database (https://portal.gdc.cancer.gov/). The Chinese Glioma Genome Atlas (CGGA), including CGGA (mRNAseq_325) (when not specified, CGGA refers to CGGA mRNAseq_325 data) and CGGA (mRNA-array), and REMBRANDT gene expression data and corresponding clinicopathological information were acquired from CGGA (http://www.cgga.org.cn). GSE16011, GSE59612, and GSE4290 datasets were acquired from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). Background correction and normalization of the raw GEO data was conducted using the robust multi-array analysis (RMA) method via the affy package in R. The Kamoun database and TCGA (Aglient-4502A) GBM data were obtained from GlioVis(14).

Screening integrins related to the prognosis of glioma

Univariate Cox regression analysis was used to select integrins correlated with the overall survival (OS) of glioma or GBM patients and was implemented using the survival package in R. Kaplan-Meyer survival analysis was employed to analyze the OS rate between different groups of glioma patients. Survival curves were plotted using the survival package.

Determination of tumor purity and immune cells enrichment level of glioma

The ESTIMATE R package was employed to calculate the stromal and immune scores of glioma samples based on the gene expression data(15). The immune cell enrichment scores were calculated via GSVA package in R(16). All heat maps were plotted using the pheatmap package in R. Genes related to the immune response were acquired from AmiGO 2 (http://amigo.geneontology.org/amigo).

Functional enrichment analysis

Gene set enrichment analysis (GSEA) was implemented using GSEA v4.1.0 to explore the hallmark gene sets, and Gene Ontology (GO) biological processes enriched in ITGA3 high- and low-expression gliomas. The differentially expressed genes in ITGA3 high- and low-expression gliomas were determined using the limma R package. GO analysis was implemented using the clusterProfiler package.

Patients and samples

Forty-five primary glioma samples were gathered from the First Hospital of China Medical University (CMU) from September, 2016 to September, 2018 (15 cases including grades II, III, and IV). Six non-tumor brain tissue specimens collected from partial lobectomy in epilepsy patients were used as a negative control. The qPCR and western blotting test were performed for all specimens. Immunohistochemistry staining was conducted for randomly selected samples (five cases of grades II, III, and IV, and two cases of non-tumor brain tissue). The Medical Ethics Committee of the First Hospital of CMU authorized the study, and each patient provided a written informed consent. Detailed information of the 45 glioma patients is shown in Supplementary Table 1.

Immunohistochemistry

Following fixation (10% neutral formalin) and embedding in paraffin, all samples were sliced as 4-μm-thick sections. Following deparaffinization in xylene, the sections were rehydrated in gradient ethanol. Citrate buffer (pH 6.0) was used for antigen retrieval. Hydrogen peroxide (3%) was employed to eliminate endogenous peroxidase activity (10 min) and the normal goat serum was employed to block nonspecific antigen (15 min). Anti-ITGA3 rabbit polyclonal antibody (1:100, no. ab131055, Abcam) was added at 4°C overnight. After washing with phosphate-buffered saline (PBS), sections were incubated with secondary antibody (SP-9001, ZSGB-BIO) for 15 min at 27°C. Then, 3,3′-diaminobenzidine (DAB; MaiXin, Fuzhou, China) was applied for developing. Expression of ITGA3 was assessed by three investigators using the German Immunohistochemical Score(17).

Cell cultures and reagents

U87, U251, LN229, T98, U373 and normal human astrocytes cell line (NHA) were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Human brain microvascular endothelial cells (HBMECs) were purchased from FENGHUISHENGWU (Changsha, Hu Nan, China). Dulbecco’s Modified Eagle’s medium (DMEM) with 10% fetal bovine serum and 1% penicillin/streptomycin was used to culture the cells. Knockdown small-interfering RNA (siRNA) targeting ITGA3 and the negative control RNA (si-NC) were purchased from RIBOBIO (Guangzhou, China). The siRNA transfection was implemented using Lipofectamine 3000 (Life Technologies, USA) according to the manufacturer’s instructions. The RFP-GFP-LC3 plasmid was used to monitor the autophagic flux, which was indicated by the formation of RFP-GFP-positive puncta. G418 (Gentihold, Beijing, China) was used to screen cells stably transfected with RFP-GFP-LC3.

Migration and invasion assays

The wound healing assay was used to evaluate the migration ability of glioma cells. A 200-μl pipette tip was used to inflict a wound when seeded cells in the 6-well plates reached a density of approximately 90% confluence. After washing with PBS to remove debris, the cells were cultivated in serum-free DMEM. The wounds were recorded and photographed at 0 h and 24 h. ImageJ software (Bethesda, MD, USA) was used to measure the wound area.

The Transwell invasion assay was adopted to assess the invasion potential of glioma cells. Transwell chambers (Costar; Corning Inc., Corning, NY, USA) plated with Matrigel (BD Biosciences, San Jose, CA, USA) were applied to perform the cell invasion assays. Following placement on 24-well plates, 100 μL of serum-free DMEM suspended with 10⁵ cells was added to the upper chamber; the lower chamber was injected with 800 μL of DMEM containing 20% fetal bovine serum. After culturing at 37 °C in 5% CO₂ for 20 h, the cells on the lower wall of the chamber were fixed and stained. In each chamber, the cells were counted in randomly selected five high power fields. Each experimental group was performed in triplicate.

Tube formation assay

In vitro angiogenesis evaluation was performed by Matrigel assay(18). Briefly, after coating with 100 μL of Matrigel, 100 μL cell suspension containing 10⁵ HBMECs was added to each well of a 96-well culture plate and the cells were cultured for 16 h. Branched tubules were imaged and counted using a fluorescence microscope (DP71; Olympus, Tokyo, Japan).

Real-time quantitative PCR (qPCR)

TRIzol reagent (Invitrogen) was applied to isolate total RNA from cells or tissues. RNA concentration was detected using a NanoDrop 2000. Then, total RNA was reverse transcribed to cDNA. Real-time qPCR was performed according to the instructions of the SYBR Green PCR master mix kit (Takara, Japan). Each sample was repeated three times. Primer sequences for GAPDH, and ITGA3 were as follows:

GAPDH forward: 5'-GGAGCGAGATCCCTCCAAAAT-3';

GAPDH reverse: 5'-GGCTGTTGTCATACTTCTCATGG-3';

ITGA3 forward: 5'-TGTGGCTTGGAGTGACTGTG-3';

ITGA3 reverse: 5'-TCATTGCCTCGCACGTAGC-3'.

Western blotting

Western blotting was conducted as described previously(19). We extracted total protein from cells or tumor tissues using RIPA buffer and PMSF (Beyotime, Beijing, China) in a ratio of 10:1. The concentration of total protein was determined using the bicinchoninic acid (BCA) (Beyotime) method. Equal amounts (30 µg) of protein were separated by 10% gel electrophoresis and transferred onto polyvinylidene fluoride membrane (EMD Millipore, Billerica, MA, USA). After blocking with 5% skim milk, the membranes were incubated with primary antibodies against ITGA3(1:1000, no. ab131055, Abcam), E-Cadherin, N-Cadherin, β-Catenin, Vimentin, Snail, Claudin-1 (1:1000; no. 9782T, Cell Signaling Technology), VEGFA (1:1000, no.ab1316, Abcam), TGFB1 (1:1000, no.3709S, Cell Signaling Technology), LC3 (1:1000, no.14600-1-AP, Proteintech), ERK1/2 (1:1000, no.4695S, Cell Signaling Technology), and p-ERK1/2 (1:1000, no.9101S, Cell Signaling Technology) at 4°C for 15 h. The membranes were washed with Tris-buffered saline with Tween-20 (TBST) and incubated with appropriate horseradish peroxidase-conjugated secondary antibodies (Proteintech; 1:5000) at 25 °C for 2 h. Immunoreactive proteins were photographed by Tanon 5200. GAPDH (1:1000, no.10494-1-AP, Proteintech) was used as a loading control.

Data analysis

All statistical analyses were conducted using R (version 4.0.2) or GraphPad Prism version 7.00 (GraphPad Software Inc., USA). Quantitative data are displayed as the mean ± standard deviation (SD). The Wilcoxon test was used to compare the statistical differences between two groups, while the differences between multiple groups were compared by Kruskal−Wallis H test. P-values < 0.05 were considered statistically significant. The Venn diagram was drawn using jvenn(20). *P < 0.05, **P < 0.01, ***P < 0.005, ****P < 0.001, ns: P ≥ 0.05.

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).

Although great efforts have been made to improve glioma treatment, the prognosis of glioma patients is still poor(1, 2). New effective treatment methods and reliable treatment targets are urgently needed for the treatment of glioma. In addition, new molecular markers for improving the diagnostic and prognosis prediction efficiency of glioma are also necessary.

Our study found that the expression level of ITGA3 was significantly upregulated in glioma compared with that of normal brain tissue, and tended to be enriched in aggressive subtypes of glioma. In addition, the expression of ITGA3 was also closely correlated with the prognosis of glioma patients. This indicated that ITGA3 could not only act as a diagnostic marker for glioma, but also serve as a prognostic predictor of glioma.

The tumor microenvironment, especially immune cell components, plays a crucial role in tumor progression. Our previous study confirmed that glioma purity was an independent prognostic factor for glioma(26). In this study, we revealed that ITGA3 was strongly correlated with glioma purity. Additionally, the expression level of ITGA3 in gliomas was also significantly positively correlated with the enrichment level of most immune cells. Furthermore, ITGA3 could also regulate the anti-glioma immune response through immune checkpoints and other immune-related genes. These findings suggested that expression levels of ITGA3 affected the characteristics of the glioma microenvironment and influenced the anti-glioma immune response state. In addition to immune regulation, we also found that ITGA3 could regulate angiogenesis in the glioma microenvironment. Anti-angiogenesis therapies have become an important treatment method for many cancers including glioma(32, 34–37). Furthermore, angiogenesis also has an important impact on the immune composition in the glioma microenvironment(38). Thus, ITGA3 could also affect the immune microenvironment of glioma through angiogenesis, and inhibiting ITGA3 not only prevented angiogenesis, but also regulated anti-glioma immunity.

In addition to the microenvironmental factors that affect the progression of glioma, the factors of glioma cells themselves should not be ignored. We found that ITGA3 promoted the EMT of glioma cells to improve their migration and invasion capabilities. The high invasiveness of glioma cells is an important factor that makes gliomas easy to relapse and difficult to treat. Although autophagy plays dual roles, which include anti-cancer effects as well as helping with therapy resistance in cancer chemoradiotherapy(39–41), we believe that the promotion effect of ITGA3 on autophagy of glioma cells might reduce the chemotherapy sensitivity of glioma cells. This is because that the prognosis of glioma patients with high ITGA3 expressing receiving radiotherapy or chemotherapy was significantly worse than that of the ITGA3 low expressing patients.

ITGA3 played the above roles in glioma, but its specific mechanism was still unclear. The ERK1/2 pathway participates in many biological processes, such as cell proliferation, differentiation, cell migration, survival(apoptosis and senescence)(42, 43), the EMT(44), autophagy(45), angiogenesis(45, 46), and inflammatory and immune responses(47). The PI3K/AKT/mTOR pathway play a critical role in basic intracellular functions in many neoplasms(48, 49). A common function of the PI3K/AKT/mTOR pathway is regulation of autophagy(50). The PI3K/AKT/mTOR and ERK1/2 pathways are also critical for exploring potential cancer therapeutic targets. The relationship between ITGA3 and these pathways shows the important role ITGA3 plays in glioma. It also reflects the possibility of ITGA3 as a target for glioma treatment.

In summary, our study explored the prognostic value, expression characteristics, and biological processes of ITGA3 in glioma. Our results revealed that ITG3 not only regulated the immune characteristics and angiogenesis within the glioma microenvironment, but also regulated the EMT and autophagy of glioma cells. These findings indicated that ITGA3 might be a potential therapeutic target for glioma.

TCGA: The Cancer Genome Atlas;

CGGA: Chinese Glioma Genome Atlas;

EMT: Epithelial–mesenchymal transition;

LGG: Low grade glioma;

GBM: Glioblastoma;

GEO: Gene expression omnibus;

OS: Overall survival;

GSEA: Gene set enrichment analysis;

GO: Gene ontology;

CMU: China Medical University;

PBS: Phosphate-buffered saline;

siRNA: Small-interfering RNA;

DMEM: Dulbecco’s Modified Eagle’s medium;

PN: Proneural;

NE: Neural;

CL: Classical;

ME: Mesenchymal;

WT: Wild type;

CCLE: Cancer cell line encyclopedia;

NHA: Normal human astrocytes cell line;

HBMEC: Human brain microvascular endothelial cell;

Ethics approval and consent to participate

The Medical Ethics Committee of the First Hospital of CMU authorized the study, and each patient provided a written informed consent.

Consent for publication

Not applicable

Data Availability Statement

The cohorts used in this study can be acquired through: http:// www.cgga.org.cn/ (CGGA, CGGA mRNA-array, REMBRANDT), https://portal.gdc.cancer.gov/ (TCGA), https://www.ncbi.nlm.nih.gov/geo/ (GSE16011, GSE4290, GSE59612), and http://gliovis.bioinfo.cnio.es/ (TCGA-GBM Aglient-4502A, Kamoun data).

Conflict of Interest

The authors declare that the study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding information

This work was supported by grants from the National Natural Science Foundation of China (grant number: 81172409).

Author Contributions

The project was designed by Qing Zhang and Anhua Wu, and implemented by Qing Zhang. Qing Zhang wrote the initial draft and Anhua Wu revised and edited it. All authors reviewed the final version of the manuscript and consented its publication.

Acknowledgments

The authors thank the Department of Pathology, The First Affiliated Hospital of China Medical University, Shenyang, China for technical assistance.

Perry JR, Laperriere N, O'Callaghan CJ, Brandes AA, Menten J, Phillips C, et al. Short-Course Radiation plus Temozolomide in Elderly Patients with Glioblastoma. N Engl J Med. 2017;376(11):1027-37.
Cancer Genome Atlas Research N, Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, et al. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med. 2015;372(26):2481-98.
Preusser M, de Ribaupierre S, Wohrer A, Erridge SC, Hegi M, Weller M, et al. Current concepts and management of glioblastoma. Ann Neurol. 2011;70(1):9-21.
Mezu-Ndubuisi OJ, Maheshwari A. The role of integrins in inflammation and angiogenesis. Pediatr Res. 2020.
Seguin L, Desgrosellier JS, Weis SM, Cheresh DA. Integrins and cancer: regulators of cancer stemness, metastasis, and drug resistance. Trends Cell Biol. 2015;25(4):234-40.
Li Q, Ma W, Chen S, Tian EC, Wei S, Fan RR, et al. High integrin alpha3 expression is associated with poor prognosis in patients with non-small cell lung cancer. Transl Lung Cancer Res. 2020;9(4):1361-78.
Wu A, Zhang S, Liu J, Huang Y, Deng W, Shu G, et al. Integrated Analysis of Prognostic and Immune Associated Integrin Family in Ovarian Cancer. Front Genet. 2020;11:705.
Hiroshima Y, Kasajima R, Kimura Y, Komura D, Ishikawa S, Ichikawa Y, et al. Novel targets identified by integrated cancer-stromal interactome analysis of pancreatic adenocarcinoma. Cancer Lett. 2020;469:217-27.
Wang JR, Liu B, Zhou L, Huang YX. MicroRNA-124-3p suppresses cell migration and invasion by targeting ITGA3 signaling in bladder cancer. Cancer Biomark. 2019;24(2):159-72.
Sa KD, Zhang X, Li XF, Gu ZP, Yang AG, Zhang R, et al. A miR-124/ITGA3 axis contributes to colorectal cancer metastasis by regulating anoikis susceptibility. Biochem Biophys Res Commun. 2018;501(3):758-64.
Koshizuka K, Hanazawa T, Kikkawa N, Arai T, Okato A, Kurozumi A, et al. Regulation of ITGA3 by the anti-tumor miR-199 family inhibits cancer cell migration and invasion in head and neck cancer. Cancer Sci. 2017;108(8):1681-92.
Tang XR, Wen X, He QM, Li YQ, Ren XY, Yang XJ, et al. MicroRNA-101 inhibits invasion and angiogenesis through targeting ITGA3 and its systemic delivery inhibits lung metastasis in nasopharyngeal carcinoma. Cell Death Dis. 2017;8(1):e2566.
Ke FY, Chen WY, Lin MC, Hwang YC, Kuo KT, Wu HC. Novel monoclonal antibody against integrin alpha3 shows therapeutic potential for ovarian cancer. Cancer Sci. 2020.
Bowman RL, Wang Q, Carro A, Verhaak RG, Squatrito M. GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro Oncol. 2017;19(1):139-41.
Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.
Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017;18(1):248-62.
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(8):862-6.
Wurdinger T, Tannous BA, Saydam O, Skog J, Grau S, Soutschek J, et al. miR-296 regulates growth factor receptor overexpression in angiogenic endothelial cells. Cancer Cell. 2008;14(5):382-93.
Zhang Q, Yang L, Guan G, Cheng P, Cheng W, Wu A. LOXL2 Upregulation in Gliomas Drives Tumorigenicity by Activating Autophagy to Promote TMZ Resistance and Trigger EMT. Front Oncol. 2020;10:569584.
Bardou P, Mariette J, Escudie F, Djemiel C, Klopp C. jvenn: an interactive Venn diagram viewer. BMC Bioinformatics. 2014;15:293.
Desgrosellier JS, Cheresh DA. Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer. 2010;10(1):9-22.
Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17(1):98-110.
Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH, Wu TD, et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell. 2006;9(3):157-73.
Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360(8):765-73.
Wang HY, Tang K, Liang TY, Zhang WZ, Li JY, Wang W, et al. The comparison of clinical and biological characteristics between IDH1 and IDH2 mutations in gliomas. J Exp Clin Cancer Res. 2016;35:86.
Zhang C, Cheng W, Ren X, Wang Z, Liu X, Li G, et al. Tumor Purity as an Underlying Key Factor in Glioma. Clin Cancer Res. 2017;23(20):6279-91.
Zuo S, Wei M, Wang S, Dong J, Wei J. Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma. Front Immunol. 2020;11:1218.
Blackburn SD, Shin H, Haining WN, Zou T, Workman CJ, Polley A, et al. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nat Immunol. 2009;10(1):29-37.
Topalian SL, Drake CG, Pardoll DM. Targeting the PD-1/B7-H1(PD-L1) pathway to activate anti-tumor immunity. Curr Opin Immunol. 2012;24(2):207-12.
Anderson AC, Joller N, Kuchroo VK. Lag-3, Tim-3, and TIGIT: Co-inhibitory Receptors with Specialized Functions in Immune Regulation. Immunity. 2016;44(5):989-1004.
Mentlein R, Held-Feindt J. Angiogenesis factors in gliomas: a new key to tumour therapy? Naturwissenschaften. 2003;90(9):385-94.
Plate KH, Scholz A, Dumont DJ. Tumor angiogenesis and anti-angiogenic therapy in malignant gliomas revisited. Acta Neuropathol. 2012;124(6):763-75.
Wang Z, Gao L, Guo X, Feng C, Lian W, Deng K, et al. Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma. Aging (Albany NY). 2019;11(24):12246-69.
Hosein AN, Brekken RA, Maitra A. Pancreatic cancer stroma: an update on therapeutic targeting strategies. Nat Rev Gastroenterol Hepatol. 2020;17(8):487-505.
Xie YH, Chen YX, Fang JY. Comprehensive review of targeted therapy for colorectal cancer. Signal Transduct Target Ther. 2020;5(1):22.
Huang A, Yang XR, Chung WY, Dennison AR, Zhou J. Targeted therapy for hepatocellular carcinoma. Signal Transduct Target Ther. 2020;5(1):146.
Yi M, Jiao D, Qin S, Chu Q, Wu K, Li A. Synergistic effect of immune checkpoint blockade and anti-angiogenesis in cancer treatment. Mol Cancer. 2019;18(1):60.
Wang N, Jain RK, Batchelor TT. New Directions in Anti-Angiogenic Therapy for Glioblastoma. Neurotherapeutics. 2017;14(2):321-32.
Sui X, Chen R, Wang Z, Huang Z, Kong N, Zhang M, et al. Autophagy and chemotherapy resistance: a promising therapeutic target for cancer treatment. Cell Death Dis. 2013;4:e838.
Fulda S, Kogel D. Cell death by autophagy: emerging molecular mechanisms and implications for cancer therapy. Oncogene. 2015;34(40):5105-13.
Galluzzi L, Morselli E, Vicencio JM, Kepp O, Joza N, Tajeddine N, et al. Life, death and burial: multifaceted impact of autophagy. Biochem Soc Trans. 2008;36(Pt 5):786-90.
Balmanno K, Cook SJ. Tumour cell survival signalling by the ERK1/2 pathway. Cell Death Differ. 2009;16(3):368-77.
Sun Y, Liu WZ, Liu T, Feng X, Yang N, Zhou HF. Signaling pathway of MAPK/ERK in cell proliferation, differentiation, migration, senescence and apoptosis. J Recept Signal Transduct Res. 2015;35(6):600-4.
Dudas J, Ladanyi A, Ingruber J, Steinbichler TB, Riechelmann H. Epithelial to Mesenchymal Transition: A Mechanism that Fuels Cancer Radio/Chemoresistance. Cells. 2020;9(2).
Feng F, Zhang M, Yang C, Heng X, Wu X. The dual roles of autophagy in gliomagenesis and clinical therapy strategies based on autophagic regulation mechanisms. Biomed Pharmacother. 2019;120:109441.
Mirzaei A, Mohammadi S, Ghaffari SH, Yaghmaie M, Vaezi M, Alimoghaddam K, et al. Osteopontin b and c Splice isoforms in Leukemias and Solid Tumors: Angiogenesis Alongside Chemoresistance. Asian Pac J Cancer Prev. 2018;19(3):615-23.
Chang L, Karin M. Mammalian MAP kinase signalling cascades. Nature. 2001;410(6824):37-40.
Fruman DA, Rommel C. PI3K and cancer: lessons, challenges and opportunities. Nat Rev Drug Discov. 2014;13(2):140-56.
Fresno Vara JA, Casado E, de Castro J, Cejas P, Belda-Iniesta C, Gonzalez-Baron M. PI3K/Akt signalling pathway and cancer. Cancer Treat Rev. 2004;30(2):193-204.
Lee SB, Kim S, Lee J, Park J, Lee G, Kim Y, et al. ATG1, an autophagy regulator, inhibits cell growth by negatively regulating S6 kinase. EMBO Rep. 2007;8(4):360-5.

Additionalfile1.pdf
Additional file 1: Supplementary Table 1(PDF). The clinical characteristics of 45 glioma patients.
Additionalfile2.pdf
Additional file 2: Supplementary Figure 1−8(PDF). Supplementary Figure 1: (A−B) High ITGA3 expression was linked with poor survival in glioma patients receiving radiotherapy (A) or temozolomide (TMZ) treatment (B) in CGGA dataset. (C−D) High ITGA3 expression was linked with poor survival in GBM patients receiving radiotherapy (C) or temozolomide (TMZ) treatment (D) in TCGA(Aglient-4502A) cohort. Supplementary Figure 2. The expression characteristics of ITGA3 in different tissues and cells. (A) Expression of ITGA3 in different tissues of human body. (B) In tumor cell lines, the expression level of ITGA3 varied with tumor types. (C−G) The expression of ITGA3 in glioma and normal brain tissue samples. Supplementary Figure 3. ITGA3 was closely related to glioma purity in GSE16011 dataset. (A−D) The expression of ITGA3 was significantly positively correlated with ESTIMATE, immune, and stromal scores, but negatively correlated with glioma purity in the GSE16011 cohort. (E) The expression level of ITGA3 in gliomas was significantly positively correlated with the enrichment of most immune cells within the glioma microenvironment in the GSE16011 cohort. (F) Enrichment characteristics of overlapped immune cells in glioma in the GSE16011 dataset. Supplementary Figure 4. The relationship between ITGA3 and immune checkpoints in the GSE16011 cohort. (A) The expression of ITGA3 was significantly related to the expression of most immune checkpoints. (B−G) The correlation between ITGA3 and six overlapped immune checkpoints (CD276, NRP1, CD274, CD44, TNFRSF14, and CD40) in the GSE16011 cohort. Supplementary Figure 5. (A) ITGA3 related immune genes in the GSE16011 dataset. (B) The intersection of immune genes with correlation coefficient ≥ 0.4 significantly correlated with ITGA3 expression level in TCGA, CGGA, and GSE16011 datasets. Supplementary Figure 6. The relationship between ITGA3 and EMT markers. ITGA3 had a significant positive correlation with Vimentin (A−C), N-Cadherin (D−F), Claudin-1 (G−I), and Snai1 (J−L) in TCGA, CGGA, and GSE16011 datasets. Supplementary Figure 7. The relationship between ITGA3 and pro-angiogenic factors. ITGA3 had a significant positive correlation with VEGFA (A−C), TGFB1 (D−F), PDGFA (G−I), and ANGPT2 (J−L) in TCGA, CGGA, and GSE16011 datasets. Supplementary Figure 8. ERK1/2 and PI3K/AKT/mTOR pathways were enriched in ITGA3 high-expressing gliomas in CGGA (A−C) and GSE16011 (D−F) datasets.

Download PDF

Review #1 received at journal
11 Jan, 2021
Reviewer #1 agreed at journal
27 Dec, 2020
Reviewers invited by journal
23 Dec, 2020
Editor assigned by journal
13 Dec, 2020
Submission checks completed at journal
13 Dec, 2020
Editor invited by journal
13 Dec, 2020
First submitted to journal
11 Dec, 2020

You are reading this latest preprint version

ITGA3 Is a Reliable Biomarker and Putative Therapeutic Target for Glioma

Status:

Version 1

Abstract

Figures

Background

Methods

Data source

Screening integrins related to the prognosis of glioma

Determination of tumor purity and immune cells enrichment level of glioma

Functional enrichment analysis

Patients and samples

Immunohistochemistry

Cell cultures and reagents

Migration and invasion assays

Tube formation assay

Real-time quantitative PCR (qPCR)

Western blotting

Data analysis

Results

Expression characteristics and prognostic significance of ITGA3 in glioma are more prominent than other integrins

ITGA3 predicts poor prognosis in glioma

ITGA3 expression is enriched in aggressive subtypes of glioma

Relationship between ITGA3 and tumor purity of glioma

ITGA3 is closely related to immune checkpoints in glioma

ITGA3-related immune signatures in glioma

ITGA3 regulates the EMT of glioma cells

ITGA3 promotes angiogenesis in glioma

Silencing ITGA3 inhibits autophagy in glioma cells

Discussion

Conclusions

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Data Availability Statement

Conflict of Interest

Funding information

Author Contributions

Acknowledgments

References

Supplementary Files

Status:

Version 1