BST2 is an immune-related prognostic biomarker for glioma

doi:10.21203/rs.3.rs-3020015/v1

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BST2 is an immune-related prognostic biomarker for glioma

https://doi.org/10.21203/rs.3.rs-3020015/v1

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Background

Glioma is the most common brain cancer. Research has indicated that the tumor microenvironment (TME) plays an important role in the proliferation, invasion, and treatment response of tumors. However, the role of TME in glioma remains unclear. Here, we try to identify a TME-related gene in glioma that can predict prognosis.

Methods

The transcriptome data and corresponding clinical data of 701 glioma samples were downloaded from the TCGA database. The transcriptome data of normal samples were obtained from the Genotype-Tissue Expression (GTEx) database. R software and R packages were used to perform statistical analysis, calculation of TME scores, survival analysis, Cox regression analysis, and functional enrichment analyses. GSEA software was used to perform Gene Set Enrichment Analysis (GSEA). Protein-protein interaction (PPI) network analysis was performed using Cytoscape software. The protein expression level of BST2 was detected by immunohistochemistry.

Results

By gene differential expression analysis based on TME scores, we obtained two sets of differentially expressed genes (DEGs), respectively, followed by intersection analysis, we obtained shared DEGs. By univariate Cox regression analysis and PPI network analysis of the shared DEGs, we obtained a prognostic gene set and hub gene set, respectively, followed by intersection analysis, we obtained three hub genes associated with prognosis, including BST2, CCL2, and RSAD2. Subsequent analyses were focused on BST2. Compared with normal samples, BST2 expression was higher in glioma samples. Moreover, BST2 expression was positively correlated with pathological grades and was negatively correlated with overall survival time (OS). Consistently, a validation cohort of 42 glioma patients further verified the upregulation of BST2 and its influence on prognosis. Mechanistically, the result of GSEA indicated that BST2 might be involved in regulating tumor immunity. By differential analysis of tumor-infiltrating immune cells (TICs) contents between high- and low-BST2 expression groups, and correlation analyses between the expression level of BST2 and TICs contents, we obtained 7 key TICs, and 5 of them were significantly associated with OS, especially regulatory T cells and M2 macrophages.

Conclusions

The TME-related gene BST2 could promote the progression of glioma via regulating the composition of TICs, and thus might be a novel prognostic biomarker.

BST2

glioma

tumor microenvironment

prognosis

tumor-infiltrating immune cells

Glioma accounts for about 30% of brain tumors and about 80% of malignant brain tumors and can be divided into four tumor grades (WHO I - IV) [1]. Some low-grade gliomas would deteriorate to GBM (WHO IV), whose 5-year survival rate is merely about 4.7% [2]. Currently, surgery, radiotherapy, and chemotherapy are the mainstream treatments. However, the clinical outcome of glioma patients is still dismal [3]. As a promising treatment, immunotherapy has attracted more and more attention. It can inhibit tumor progression by activating the immune system [4]. However, the low response rate is the major limitation [5].

Research indicated that TME is very important for therapeutic response and eventual prognosis of tumors [6]. Stromal component, including endothelial cells, fibroblasts, and pericytes, is one of the major components in TME [7]. Stromal cells can interact with tumor cells and affect proliferation and response to therapy by secreting inflammatory factors or extracellular matrix [8]. TICs, i.e., all kinds of innate and adaptive immune cells, are another major component in TME [9]. Studies have suggested that some kinds of TICs in TME might be predictors of cancer prognosis. Ge et al. reported that high content of M1 macrophages in the TME of colorectal cancer might indicate a poor prognosis [10]. Bi et al. reported that low content of lymphocytes in the TME of lung cancer might indicate a poor prognosis [11]. Furtherly, studies have suggested that some genes were regulators of TME and can predict tumor prognosis [12–16]. Chen et al. reported that gene KCNN4 influences renal carcinoma progression by regulating the immune status of TME [16]. Yue et al. reported that CX3CR1 influences colorectal cancer (CRC) prognosis by regulating TICs in TME and thus acts as a prognostic marker in CRC [15]. Nevertheless, there is still no research published on the TME-related prognostic biomarker in glioma.

To find a TME-related prognostic biomarker in glioma, we calculated the ImmuneScore and StromalScore of each glioma sample and analyzed the clinical prognostic significance with the help of the TCGA database. Two sets of DEGs were obtained by difference analyses of the two kinds of TME scores and shared DEGs were obtained by intersection analyses. Furtherly, we obtained key gene BST2 via univariate Cox analysis and PPI analysis, followed by verification of BST2 expression and its clinical prognostic significance. GSEA suggested underlying mechanisms might be involved in the regulation of tumor immunity. Hence, we calculated the relative contents of 22 TICs in every glioma specimen by the CIBORSORT algorithm. Next, difference analysis and correlation analyses indicated that BST2 was closely related to seven types of TICs, especially regulatory T cells and M2 macrophages. Our study demonstrated that BST2 may be a promising TME-related prognostic biomarker in glioma patients.

The Download of the relevant data and calculation of ImmuneScore, StromalScore, and ESTIMATEScore

Taking advantage of the TCGA database, we successfully downloaded the transcriptome data and corresponding clinical data of glioma on May 02, 2022. In total, we obtained data from 701 tumors and 5 normal samples. Our study complies with the Declaration of Helsinki. Using the transcriptome data and “ESTIMATE” R package, we calculated ImmuneScore, StromalScore, and ESTIMATEScore. Samples lacking follow-up data were deleted. We obtained 671 tumor samples with both clinical data and scores.

Association of the scores with survival and clinical characteristics

In the light of the medians of the ImmuneScore, StromalScore, and ESTIMATEScore, we divided glioma samples into two groups respectively. Taking advantage of “survival” and “survminer” R packages, we constructed survival curves of Kaplan-Meier analyses. Subgroup analyses were conducted by Log-rank test. Taking advantage of the “limma” and “ggpubr” R packages, we performed correlation analyses between the scores and clinical features by the Kruskal-Wallis rank sum test.

Identification of TME-related DEGs

By difference analyses of ImmuneScore and StromalScore, we obtained two kinds of DEGs respectively. Next, shared DEGs were obtained by intersection analysis. Taking advantage of the “limma” R package, the differential expression analyses were carried out. The identification criteria for DEGs were |log2(FC)| > 1.5 and FDR-adjusted p < 0.001. Taking advantage of the “pheatmap” R package, heatmaps were constructed.

Gene function and pathway enrichment analysis

Gene Ontology (GO) analysis, including biological processes, molecular function, and cellular components, is a common method to annotate genes. Kyoto Encyclopedia of Gene and Genomes (KEGG) is a common method to reveal the functional information of target genes. Taking advantage of “enrichplot”, “org.Hs.eg.db”, “ggplot2” and “clusterProfiler” R packages, we conducted GO and KEGG enrichment analyses of the shared DEGs, respectively. P < 0.05 and q < 0.05 were regarded as significantly enriched. Bubble and circos diagrams were used to visualize the results.

Construction of PPI network

Taking advantage of the database of STRING (https://string-db.org/), we preliminarily conducted PPI network analysis, and further processed in version 3.9.1 of Cytoscape. The criterion for selecting nodes was the confidence of interactive association of more than 0.95. According to the number of adjacent nodes, we arranged the shared DEGs and selected the top 30 genes as the hub genes.

Univariate COX regression analysis

Taking advantage of R package “survival”, we conducted univariate COX regression analysis towards the shared DEGs, followed by the identification of genes significantly associated with OS. A forest plot was used to visualize the DEGs with p < 0.001.

Differential expression analysis and survival analysis of BST2, and correlation analysis between BST2 expression and clinical characteristics

With the help of the Sangerbox database (http://www.sangerbox.com/Tool), differential expression analyses of BST2 in mRNA level between tumor and normal specimens were conducted. Based on the median of BST2 expression, glioma tissues were divided into two groups. Taking advantage of the “survival” and “survminer” R packages, the Kaplan-Meier survival curve was constructed. A comparison of subgroups was conducted via a log-rank test. Furthermore, taking advantage of “limma” and “ggpubr” R packages, we performed correlation analyses between BST2 expression and clinical features via the Kruskal Wallis rank sum test.

Validation cohort

A validation cohort of 42 glioma patients, collected from surgery and confirmed by pathology in Sun Yat-Sen Memorial Hospital from May 2017 to May 2022, was used to confirm the bioinformatic results. Corresponding clinical data were obtained from medical records. All patients in the cohort had signed informed consent and the study had obtained approval from the ethics committee of the Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.

Immunohistochemistry

Routine immunohistochemistry on the glioma and adjacent normal tissues was conducted. Antibody against BST2 was purchased from CST (#95940). After deparaffinating of the paraffin-embedded tissues, rehydration, blocking, and incubation with antibodies (dilution: 1:500) were conducted according to the instruction. Images were analyzed by a microscope (Zeiss, Germany).

Gene set enrichment analysis

With the help of version 4.2.3 of GSEA software, GSEA of BST2 was performed, and relevant signaling pathways were obtained. In terms of high BST2 expression, pathways were selected according to the conditions of nominal p < 0.05 and FDR q < 0.05. As to low BST2 expression, pathways were selected according to the condition of nominal p < 0.05.

Comprehensive analysis of TICs in TME

Taking advantage of the CIBERSORT algorithm, the relative contents of 22 TICs in every glioma tissue were calculated. A bar chart was used to visualize the result. In light of the median of BST2 expression, glioma samples were divided into two groups. Using the Wilcoxon rank sum test, a comparison of the contents of TICs between the two glioma groups was conducted. In addition, Pearson’s correlation analyses between the contents of TICs and the expression level of BST2 were conducted. Using “vioplot”, “ggplot2” and “ggpubr” R packages, the above results were visualized in violon and scatter diagrams respectively.

Statistical analysis

Statistical analysis was conducted in R software version 4.2.0 with the help of various packages. P < 0.05 was regarded as statistically significant.

A flow chart of this study

According to the process of this study, we drew a flow diagram (Fig. 1). By TCGA database, we successfully obtained the transcriptome data and corresponding clinical data of glioma. In total, we obtained data from 701 tumors and 5 normal samples. To evaluate the immune status of TME in glioma, we calculated the ImmuneScore, StromalScore, and ESTIMATEScore of each specimen by the ESTIMATE algorithm. To detect the prognostic significance of the three scores, survival analyses were performed, respectively. According to ImmuneScore and StromalScore, two kinds of DEGs were obtained and shared DEGs were identified by intersection analysis. To further screen candidate genes from the shared DEGs, we conducted univariate Cox regression analysis and PPI analysis towards the shared DEGs. The thirty most significant genes were selected from the univariate Cox regression analysis and PPI analysis, respectively. Three candidate genes, including BST2, CCL2, and RSAD2, were identified by intersection analysis. Next, we focus on BST2, and to further verify its expression and clinical prognostic significance by public database and our validation cohort, differential expression analysis, survival analysis, and correlation analyses were carried out. To investigate the mechanisms, GSEA was conducted and results indicated that BST2 might be involved in the regulation of tumor immunity. Relative contents of 22 TICs in the TME of every glioma tissue were calculated using the CIBERSORT algorithm. To detect the key TICs related to BST2, we conducted differential analyses of TICs contents according to BST2 expression, followed by correlation analyses between BST2 expression and TICs contents. Seven key TICs were identified by intersection analysis, and five of them, including M2 macrophages, regulatory T cells, eosinophils, resting mast cells, and activated mast cells, were found to be associated with prognosis by survival analyses. Thus, the TME-related gene BST2 might regulate the composition of TICs in glioma to induce deterioration.

ImmuneScore, StromalScore, and ESTIMATEScore were negatively correlated with OS in patients with glioma respectively

To evaluate the immune status of TME, we calculated the ImmuneScore, StromalScore, and ESTIMATEScore of every specimen by the ESTIMATE algorithm. In the light of the median of each score, we divided glioma samples into two groups respectively, followed by Kaplan-Meier survival analyses. As shown in Fig. 2, the three scores were negatively related to the OS respectively, i.e., compared with the low score groups, the overall survival time of the high score groups was shorter. Thus, the status of TME may influence the prognosis of glioma.

The three scores were related to the clinical characteristics of patients with glioma

Corresponding clinical data was successfully downloaded from the TCGA database. Correlation analyses between the three scores and clinical features were conducted respectively. Results showed that glioma patients over the age of 60 had significantly higher scores than those under the age of 60 (Fig. 3A, D, and G), and male glioma patients had significantly higher scores than female glioma patients (Fig. 3B, E, and H). Of particular importance, these three scores were positively correlated with tumor grades respectively, i.e., scores increased significantly as grade went from G2 to G4 (Fig. 3C, F, and I).

ImmuneScore- and StromalScore-related DEGs were involved in immune regulation

In the light of the medians of ImmuneScore and StromalScore, we divided glioma samples into two groups respectively, followed by gene difference expression analyses. As shown in heatmaps and Venn diagrams, there were 1642 DEGs in terms of ImmuneScore, 1202 of which were up-regulated and 440 down-regulated, and there were 1979 DEGs in terms of StromalScore, 1630 of which were up-regulated and 349 down-regulated. In addition, by intersection analysis of the two kinds of DEGs, we identified 1387 shared DEGs, 1100 of which were up-regulated and 287 down-regulated. The shared DEGs might be key genes regulating the TME (Fig. 4A, B, and C).

To explore the molecular biological functions of the shared DEGs, GO and KEGG analyses were performed. As shown in the bubble diagram and circos diagram of the GO analysis (Fig. 4D and E), shared DEGs were mainly enriched in GO items related to immune regulation, including leukocyte-mediated immunity, the external side of the plasma membrane, and antigen binding. In addition, as shown in the bubble diagram and circos diagram of KEGG analysis (Fig. 4F and G), shared DEGs are significantly enriched in signaling pathways related to cytokine-cytokine receptor interaction.

Further identification of candidate genes from shared DEGs

To further identify candidate genes from the shared DEGs, both univariate Cox regression analysis and PPI analysis towards DEGs were conducted (Fig. 5). As shown in the bar chart derived from PPI analysis, 30 genes with the most nodes were obtained. In addition, as shown in the forest plot derived from univariate Cox regression analyses, the 30 most significant genes were obtained. Finally, as shown in the Venn diagram of intersection analysis, three candidate genes, including BST2, CCL2, and RSAD2, were identified.

Verification of BST2 expression and its clinical prognostic significance via a public database and our validation cohort

To verify BST2 expression and its clinical prognostic significance, we performed differential expression analysis, survival analysis according to BST2 expression, and correlation analyses between the BST2 expression and clinical features. As shown in Fig. 6A, compared with normal specimens, BST2 expression in mRNA level was significantly increased in glioma samples. In our cohort, compared with corresponding adjacent normal tissues, the protein expression level of BST2 was higher in glioma tissues (Fig. 6F and G). In the light of the median of BST2 expression level, glioma samples of public datasets and our validation cohort were divided into two groups respectively, followed by survival analyses. Results suggested that the BST2 expression in mRNA and protein levels were negatively correlated with OS respectively (Fig. 6B and H). In correlation analyses between BST2 expression and clinical characteristics, BST2 expression was correlated with age to some extent, and was positively correlated with tumor grades, but not with gender (Fig. 6C-E).

Primary mechanism investigation via GSEA of BST2

To investigate the signaling pathways related to BST2 expression, GSEAs of high and low expression were performed respectively. High expression of BST2 mainly enriched in signaling pathways related to immune regulation, such as cytokine-cytokine receptor interaction, Toll-like receptor pathway, B cell receptor pathway, antigen processing, and presentation, and NK mediated cytotoxicity (ten significant signaling pathways are shown in Fig. 7A). In addition, low expression of BST2 is mainly enriched in signaling pathways associated with metabolisms, such as butanoate metabolism, vasopressin-regulated water reabsorption, and inositol phosphate metabolism (five significant pathways are shown in Fig. 7B). Thus, high expression of BST2 is closely related to the regulation of tumor immunity.

BST2 regulates the composition of TICs in TME

To evaluate the composition of TICs in the TME of glioma, the relative contents of 22 kinds of TICs in every glioma tissue were calculated using the CIBERSORT algorithm. The composition of TICs in glioma tissues as well as correlations between every two TICs were shown in Fig. 8. To verify BST2 participates in the regulation of TICs composition, difference analysis of TICs contents according to BST2 expression, as well as correlation analyses between BST2 expression and TICs contents were conducted respectively. Results indicated that 7 kinds of TICs showed significant differences (Fig. 9A). Furthermore, 8 kinds of TICs correlated (6 positively and 2 negatively) with the expression level of BST2 (Fig. 9B). Seven shared TICs, including M2 macrophages, regulatory T cells, eosinophils, M1 macrophages, activated mast cells, resting mast cells, and CD8⁺ T cells were identified by intersection analysis (Fig. 9C). According to the median of the content of each shared TIC, we divided glioma samples into two groups respectively, followed by survival analyses. Results indicated that patients with a higher content of M2 macrophages or resting mast cells or regulatory T cells had a shorter overall survival time, while those with higher content of activating mast cells had a longer OS time (Fig. 10). In conclusion, BST2 might influence the overall survival time in glioma by regulating the composition of TICs in TME.

Glioma, accounting for about 80% of malignancies in the CNS, is the most common primary brain tumor. For low-grade glioma patients, the median overall survival time is approximately 12 years. As for grade III glioma, it should be about 3.1 years. Unfortunately, in grade IV glioma, the median survival time is about 15 months [17]. Currently, surgical resection, temozolomide, and radiation remain the mainstay of treatment, but the effect is still dismal. As a promising treatment, immunotherapy has attracted more and more attention [18]. In addition, research has suggested that TME is very important for therapeutic response and eventual prognosis of tumor [4], especially, the immune component in TME, i.e., TICs. [19]. In recent years, several immune-related prognostic biomarkers, modulating TME, have been reported in lung adenocarcinoma [13], renal clear cell carcinoma [16], colorectal cancer [15], and invasive breast carcinoma [14].

In our study, by assessing the immune status of TME in glioma, the key gene BST2 was obtained. BST2, i.e., CD317, is a specific marker of B cells and can regulate the innate immune response [20]. In many kinds of cancers, BST2 is aberrantly expressed. Liu et al. reported that BST2 can induce migration and proliferation of gastric cancer cells by regulating the NF-κB pathway [21]. Jin et al. reported that BST2 can promote progression and induces gefitinib resistance through the EGFR pathway in oral carcinoma [22]. In addition, BST2 can promote the malignant progression of hepatocellular carcinoma [23]. Consistently, in this study, compared with normal samples, BST2 expression was higher in glioma samples and was negatively correlated with OS. Moreover, BST2 expression was positively correlated with tumor grades. Therefore, BST2 might be a prognostic biomarker of glioma.

To identify the biological pathways related to BST2, GSEA was performed. Results suggested that BST2 is mainly involved in the regulation of tumor immunity, which means that BST2 might modulate the immune component in TME of glioma. Liu et al. reported that BST2 promotes the malignant progression of cervical cancer by inducing M2 macrophage polarization and blocking M1 macrophage polarization at the same time [24]. In our study, the relative contents of 22 TICs in every glioma tissue were calculated with the use of the CIBERSORT algorithm (Fig. 8). Difference analysis of TICs contents according to BST2 expression, as well as correlation analyses between BST2 expression and TICs contents were conducted (Fig. 9). Seven key TICs were obtained by intersection analysis, and five of the seven were found to be related to prognosis by survival analyses (Fig. 10). Thus, BST2 might influence the prognosis of glioma patients by regulating certain kinds of TICs in TME, especially regulatory T cells and M2 macrophages.

Regulatory T cells, also known as Foxp3⁺CD25⁺CD4⁺ Treg cells, are a unique T cells subset and can play a vital role in maintaining immune self-tolerance. Research suggested that Treg cells could secret specific cytokines, including TGF-β and IL-10, to impair anti-tumor immune response, and thus promote glioma-mediated immune suppression [25, 26]. Tumor tissues with extensive infiltration of Treg cells often predict a poor prognosis [27]. Consistently, our study showed that glioma patients with a high proportion of Treg cells had shorter OS. As for macrophages, they are the most abundant TICs in glioma tissues, accounting for about 30% of tumor mass, and they can be divided into two types based on their phenotypes and functions, including the M1 classical type and M2 alternative type [28]. Research has suggested that macrophages infiltrated in glioma are dominated by immunosuppressive M2 macrophages, which leads to an immunosuppressive TME, facilitates the progression of gliomas, and thus results in a poor prognosis [29]. Consistently, our study indicated that the more M2 macrophages in a glioma specimen, the shorter the patient’s OS. As far as mast cells are concerned, studies have reported that they have both anti-tumor and tumor-promoting immunoregulatory effects. Due to the contradictory reports, the specific effect of mast cells in TME remains puzzling and might be related to tumor type and grade [30]. In this study, infiltration of activated mast cells predicts a better prognosis. On the contrary, resting mast cells predict a poor clinical outcome (Fig. 10).

In this study, we comprehensively studied the relationship between TME and the prognosis of glioma patients. Results suggested that the TME-related gene BST2 might regulate the composition of TICs to promote the progression of glioma. Therefore, BST2 might be a potential prognostic biomarker of glioma.

Ethics approval and consent to participate

Our study complies with the Declaration of Helsinki. All experiments and study protocols were approved by the ethics committee of the Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University. Written informed consent was obtained from all participants.

Consent for publication

Not applicable.

Acknowledgements

We thank all the colleagues from the Department of Neurosurgery of Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University for their help in clinical sample collection and experimental guidance.

Data availability

Taking advantage of the TCGA (https://portal.gdc.cancer.gov/) and GTEx (https://gtexportal.org/) databases, we successfully downloaded transcriptome data and the corresponding clinical data.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

AL conceived the project. MH, BZ, and LO collected and analyzed the data. SL conducted experimental research. MH and BZ wrote the manuscript, and AL revised it.

Funding

This work was supported by the National Natural Science Foundation of China (NO. 81672507).

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No competing interests reported.

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Version 1

posted

You are reading this latest preprint version

BST2 is an immune-related prognostic biomarker for glioma

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

The Download of the relevant data and calculation of ImmuneScore, StromalScore, and ESTIMATEScore

Association of the scores with survival and clinical characteristics

Identification of TME-related DEGs

Gene function and pathway enrichment analysis

Construction of PPI network

Univariate COX regression analysis

Validation cohort

Immunohistochemistry

Gene set enrichment analysis

Comprehensive analysis of TICs in TME

Statistical analysis

Results

A flow chart of this study

ImmuneScore, StromalScore, and ESTIMATEScore were negatively correlated with OS in patients with glioma respectively

The three scores were related to the clinical characteristics of patients with glioma

ImmuneScore- and StromalScore-related DEGs were involved in immune regulation

Further identification of candidate genes from shared DEGs

Primary mechanism investigation via GSEA of BST2

BST2 regulates the composition of TICs in TME

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1