SYN1 is associated with immune infiltrates and might be a prognostic biomarker for glioma

doi:10.21203/rs.3.rs-3224307/v1

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SYN1 is associated with immune infiltrates and might be a prognostic biomarker for glioma

https://doi.org/10.21203/rs.3.rs-3224307/v1

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Background

Conventional treatments for glioma patients are not curative, and patient survival remains limited. Therefore, it is crucial to explore additional potent prognostic biomarkers for gliomas. SYN1, a member of the synapsin gene family, has been implicated in various neuropsychiatric illnesses. However, its role in glioma progression remains unclear.

Methods

Clinical and gene expression profile data of primary tumors were obtained from TCGA database. GEPIA was used to investigate SYN1 expression and plot survival curves of the involved patients. TIMER and CIBERSORT were used to analyze the immune infiltrates and responses of 22 TIICs, respectively. Gene set enrichment analysis (GSEA) was performed to identify the potential function of SYN1 in glioma. Furthermore, we collected tissue samples from 25 glioma patients for validation analysis using quantitative real-time PCR, western blot, and immunohistochemistry.

Results

457 eligible glioma samples downloaded from TCGA (including grade II 229 cases and III 228 cases) were involved in the current study. We found SYN1 was poorly expressed in tumor tissue and higher SYN1 expression could significantly improve the survival of glioma patients. Besides, we found that age, grade, and SYN1 were independent prognostic factors for glioma patients. In addition, macrophages M2, macrophages M0, T cells CD4 memory resting, T cells follicular helper, and plasma cells are the main immune cells affected by SYN1 expression. And the expression of SYN1 was negatively correlated with infiltrating levels of B cell, CD4 + T cell, Macrophages, Neutrophil, and Dendritic cell. SYN1 could also significantly influence the cumulative survival of B cell, CD8 + T cell, CD4 + T cell, Macrophages, Neutrophil, and Dendritic cell. The GSEA results indicated that ADIPOGENESIS, G2M_CHECKPOINT, GLYCOLYSIS, KRAS_SIGNALING_DN, KRAS_SIGNALING_UP, MYC_TARGETS_V1, NOTCH_SIGNALING, P53_PATHWAY, PANCREAS_BETA_CELLS, and SPERMATOGENESIS are mainly enriched pathways between the high and low expression groups of SYN1. Further clinical validation found that the expression of SYN1 mRNA and protein was significantly downregulated in glioma tissues.

Conclusion

SYN1 is found to be associated with immune infiltrates and might be a prognostic biomarker for glioma.

SYN1

immune microenvironment

bioinformatics

glioma

biomarker

The majority of primary brain tumor deaths are caused by gliomas, which constitute nearly 30% of all primary brain tumors and 80% of all malignant ones. (1) Gliomas are divided into four classes by the World Health Organization (WHO), with grades I and II indicating low-grade glioma (LGG) and grades III and IV indicating high-grade glioma (HGG). (2) Despite the implementation of conventional treatments including surgery, chemotherapy, and radiotherapy, the prognosis for patients with gliomas remains constrained. Therefore, it is crucial to explore additional potent biomarkers and develop patient-specific interventions based on molecular neuropathology. (3, 4)

Synapsin I (SYN1) belongs to the synapsin gene family and neuronal phosphoproteins that are associated with the cytoplasmic surface of synaptic vesicles are encoded by synapsins. Due to their involvement in synaptogenesis and neurotransmitter release control, synapsins may play a crucial role in various neuropsychiatric disorders. (5)

Previous studies have reported low expression of SYN1 in several medulloblastoma cell lines. (6) Similarly, SYN1 was found to be poorly expressed in glioblastoma biopsies (TZ) compared to their corresponding peritumoral brain zone (PBZ). (7) Musteyde Yucebas et al. investigated SYN1 mRNA expression in primary cell cultures derived from tumor samples of diffuse astrocytoma (Grade II, LGG), anaplastic oligodendroglioma (Grade III, HGG), and glioblastoma multiforme (Grade IV, HGG) cases, and no significant difference for mean values of SYN1 mRNA expression was found among these three groups. However, the relative SYN1 mRNA expression level was slightly higher in diffuse astrocytoma groups. (8)

Nevertheless, the role of SYN1 in glioma progression remains unclear. In the present study, we first identified the relationship between SYN1 and the prognosis of glioma patients. Subsequently, we analyzed the potential role of SYN1 in regulating tumor-infiltrating immune cells (TIICs). Finally, we validated the bioinformatics findings preliminarily using samples derived from different grades of glioma.

Data acquisition

We downloaded the gene expression profile data and clinical characteristics data of glioma from The Cancer Genome Atlas (TCGA).(9) Subsequently, samples missing age and survival data were excluded. Finally, all samples were divided into low SYN1 expression group and high SYN1 expression group according to the median cutoff of the SYN1 level.

Survival and expression analysis

Gene Expression Profiling Interactive Analysis (GEPIA, http://gepia.cancer-pku.cn/index.html)(10) was used to compare SYN1 expression between low-grade glioma (LGG) samples from TCGA and normal samples from Genotype-Tissue Expression (GTEx). In addition, survival analysis was performed on the high/low SYN1 expression group, thus exploring the impact of SYN1 on LGG prognosis. Finally, univariate and multivariate Cox analysis was performed via the "survive" R package to further determine the impact of SYN1 on LGG prognosis.

Analysis of immune infiltrates

To evaluate the role of SYN1 in glioma immune infiltration, we first assessed the relative proportion of 22 TIICs in the LGG using CIBERSORT algorithm. (11, 12) Subsequently, the differences between the low and high SYN1 expression groups were assessed using t-tests or Mann-Whitney Wilcoxon, according to the normality results. Then, violin plots were generated for visualization by the "ggplot2" R package. Finally, we performed correlation analyses of SYN1 expression with the abundance of immune infiltration (including B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells) through the TIMER database.(13, 14)

Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was performed to determine the potential function of SYN1.(15, 16) Specifically, differential expression analysis was performed on the high SYN1 expression group and low SYN1 expression group using the "DEseq2" R package. Subsequently, a genelist for GSEA was constructed in descending order based on the log foldchange of genes. Finally, the "clusterProfiler" R package was used for GSEA based on the "h.all.v2023.1.Hs.symbols.gmt" obtained from the Molecular Signatures Database.

Collection of tissue samples from glioma patients

The studies involving glioma patients were reviewed and approved by the Medical Ethics Committee of Xiangya Hospital, Central South University (Number:201907786). All included patients have been fully informed and signed informed consent. After collection, the samples were transported to -80 ℃ for storage within 2 hours. All samples were collected between January 2022 and April 2023.

RNA extraction and determination of SYN1 mRNA expressin

We collected tumor tissues and their corresponding peritumoral brain zone tissues from 25 glioma patients. Total RNA extraction from the collected tissue was performed with TRIzol (Invitrogen). PrimeSctipt™ RT reagent Kit (Takara) was used to obtain the reversed cDNA. Quantitative Real-time PCR was used to determine the relative expression of SYN1 and the reference gene (Primers used for the detection were shown in Supplementary Table 1).

Western Blot and immunohistochemistry

Methods of western Blot and immunohistochemistry were used to detect the expression of SYN1 protein in tumor tissue. SYN1-Specific Polyclonal antibody used for western Blot and immunohistochemical were purchased from Proteintech (Cat No. 20258-1-AP).

Statistical analysis

SPSS statistics software (v 26.0, Chicago, United States) was used to perform the statistical analysis of the clinical validation study. Data were presented as mean ± SD.

Independent sample t-test or one-way ANOVA was used for the comparison of continuous variables. p < 0.05 was considered to be statistically significant.

Characteristics of glioma samples from TCGA

In March 2023, we obtained gene expression data and clinical characteristics data for 532 "LGG" samples from the TCGA database. After excluding samples missing age or overall survival data, 457 samples were ultimately included in the present study. The "LGG" type glioma samples downloaded by TCGA include grades II and III, with 229 cases (50.1%) of grade II and 228 cases (49.9%) of grade III. The median follow-up time for patients who survived the last contact was 24.74 months, ranging from 0 to 211 months.

Role of SYN1 in glioma prognosis

Compared with the normal group, the SYN1 expression level was down-regulated in the tumor group. (Fig. 1A, p < 0.05) In addition, the expression of SYN1 was significantly lower in patients with glioma grade III than in patients with grade II (Fig. 1B, p < 0.01). Furthermore, higher SYN1 expression significantly improved the overall survival and the disease free survival of glioma patients (Fig. 1C and 1D, p = 0.016 and p = 0.013, respectively). Finally, the results of the univariate analysis of correlation using Cox regression showed that age, glioma grade, and SYN1 were significantly associated with the overall survival of the patients (Table 1). After adjusting for Cox regression multivariate survival analysis, we found that age, grade, and SYN1 were independent prognostic factors for glioma patients (Table 1, Fig. 2).

Table 1

**Association between overall survival and clinical characteristics in TCGA patients using Cox regression.** a. univariate Cox regression, b. multivariate Cox regression.
Clinical characteristics	HR(95%CI)	p Value
a.
age	1.07(1.05–1.09)	0.000
gender	1.50(0.68–1.64)	0.820
grade	3.27(2.00-5.36)	0.000
SYN1	0.83(0.72–0.95)	0.006
b.
age	1.10(1.06–1.10)	< 0.001
grade	3.50(2.06–5.80)	< 0.001
SYN1	0.70(0.60–0.81)	< 0.001

Results of immune infiltrates analysis

Figure 3 shows the differences in the proportion of 22 TIICs identified by CIBERSORT between the SYN1 low expression group and the SYN1 high expression group. Macrophages M2, macrophages M0, T cells CD4 memory resting, T cells follicular helper, and plasma cells were the most significantly differentiated among the two groups. Specifically, compared to the low SYN1 expression group, the high SYN1 expression group had lower levels of macrophages M2, macrophages M0, and T cells CD4 memory resting, and higher levels of T cells follicular helper and plasma cells (Fig. 3). Therefore, these TIICs may be regulated by SYN1 in gliomas. Furthermore, the expression of SYN1 was negatively correlated with infiltrating levels of B cell (r = -0.397, p = 1.73e-19), CD4 + T cell (r = -0.596, p = 3.61e-47), Macrophages (r = -0.556, p = 1.05e-39), Neutrophil (r = -0.402, p = 6.85e-20), and Dendritic cell (r = -0.508, p = 1.58e-32) (Fig. 4A). In addition, we also found SYN1 could significantly influence the cumulative survival of B cell, CD8 + T cell, CD4 + T cell, Macrophages, Neutrophil, and Dendritic cell. (Fig. 4B)

GSEA identified the potential function of SYN1

The most significantly enriched hallmark pathway was identified by performing GSEA on the high/low SYN1 expression group. Specifically, the following pathways were significantly enriched: ADIPOGENESIS, G2M_CHECKPOINT, GLYCOLYSIS, KRAS_SIGNALING_DN, KRAS_SIGNALING_UP, MYC_TARGETS_V1, NOTCH_SIGNALING, P53_PATHWAY, PANCREAS_BETA_CELLS, and SPERMATOGENESIS (Fig. 5). Thus, SYN1 may be involved in regulating these pathways in glioma progression.

Validation of SYN1 expression in glioma tissue

The results of real-time PCR showed that the expression of SYN1 mRNA was significantly downregulated in glioma tissues (Fig. 6A). Besides, SYN1 protein was also found to be markedly low expressed in glioma tissue (Fig. 6B). The images of immunohistochemistry also indicated similar results (Fig. 6C).

The current study found SYN1 might be a new biomarker that influence the prognosis of glioma patients using bioinformatics methods. In order to further explore the potential mechanism of SYN1 affecting glioma progression, we identified the TIICs and pathways associated with SYN1 by immune infiltration analysis and GSEA, respectively. In addition, we preliminarily explored the expression of SYN1 mRNA and protein in different grades of collected glioma samples in our clinical practice.

Since the existence, differentiation, and location of tumor-infiltrating lymphocyte were related to the clinical outcome of cancer patients (17–19), we performed an immune infiltration analysis on the two groups of data with high and low expression of SYN1. The results revealed that there was a significant difference in the proportion of macrophages M2 between the two groups with high and low expression of SYN1 and the fraction of macrophages M2 was significantly elevated in the group with low SYN1 expression. However, macrophages M2 were found to have a protective effect on cancer cell apoptosis (20) and had the function of promoting angiogenesis and promoting tumorigenesis and development (21–24). We found that the glioma patients with low expression of SYN1 have a higher proportion of macrophages M2 and a poorer prognosis, which is consistent with and mutually supportive of previous findings.

Our enrichment analysis based on the GSEA platform found that the differential gene set between the two groups with high and low SYN1 expression was mainly enriched in KRAS_SIGNALING_DN, KRAS_SIGNALING_UP, NOTCH_ Signaling, P53_ PATHWAY, and other pathways. However, the KRAS signaling pathway was found to play important roles in a variety of cancers, including pilocytic astrocytoma (a kind of glioma, WHO grade I)(25), pancreatic cancer(26, 27), colorectal cancer(28), lung cancer(29) and so on. NOTCH signaling pathway plays important roles in promoting tumor development in various types of cancer, such as glioma(30, 31), lung adenocarcinoma(32, 33), breast cancer(34, 35), leukemia(36, 37), etc. Besides, wild-type p53 can suppress the development of tumors by multiple pathways and mutation of tumor protein p53 (TP53) gene and the resultant inactivation of p53 can lead to evasion of tumor cell death and rapid tumor progression.(38, 39) All of the above evidence suggests that SYN1 has a considerable probability of involving in the occurrence and progression of glioma.

Subsequently, we conducted preliminary validation in the collected clinical samples to support our hypothesis. And as we inferred, the results of clinical validation showed that the expression of SYN1 mRNA and protein was significantly reduced in tumor tissue from glioma patients.

There are some limitations in the current study. Firstly, we do not have sufficient clinical samples to verify the impact of SYN1 expression on the prognosis of glioma patients. Secondly, the effect of SYN1 on the occurrence and development of gliomas requires further research on its mechanisms.

SYN1 is found to be associated with immune infiltrates and might be a prognostic biomarker for glioma.

Conflict of Interest

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

Author Contributions

ZH and XH designed the study, analyzed the data, and wrote the manuscript. HX and MJ performed validation in clinical tissue samples. YZ assisted in analyzing the data and revising the manuscript. DL and TS critically read and edited the manuscript.

Funding

This research was funded by the National Natural Science Foundation of Hunan Province (2022JJ80120).

Data Availability Statement

The publicly available datasets used in this study can be found in The Cancer Genome Atlas (TCGA) (GDC (cancer.gov)).

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No competing interests reported.

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SYN1 is associated with immune infiltrates and might be a prognostic biomarker for glioma

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1 Introduction

2 Methods

3 Results

4 Discussion

5 Conclusion

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