S1P3 promotes low-grade glioma progression and affects tumor microenvironment infiltration

S1P3 promotes low-grade glioma progression and affects tumor microenvironment infiltration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article S1P3 promotes low-grade glioma progression and affects tumor microenvironment infiltration Fan Chen, Peigang Ji, Fang Sun, Gang Zhu, Xuan Xie, Kehinde Alare, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7329130/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Sphingosine-1-phosphate receptor 3 (S1P3) has been implicated in the progression of various tumors, but its role in low-grade glioma (LGG) remains unclear. This study investigates the impact of S1P3 expression on glioma progression, prognosis, and immune cell infiltration within the tumor microenvironment (TME). Methods: We analyzed clinicopathological and gene expression data from The Cancer Genome Atlas (TCGA) and performed univariate/multivariate Cox regression analyses to assess the prognostic value of S1P3 in LGG. Gene Set Enrichment Analysis (GSEA) was used to identify signaling pathways associated with S1P3 expression. In vitro validation was performed using quantitative PCR, cell viability assays, wound healing assays, and Boyden chamber migration assays. Results: S1P3 overexpression was significantly associated with poor overall survival and molecular subtypes of LGG. GSEA revealed that S1P3 upregulation was linked to key oncogenic pathways, including DNA replication, cell cycle, MAPK, p53, and TGF-β signaling. Moreover, S1P3 expression correlated with increased infiltration of immune cells, including macrophages and T cells, as well as higher levels of immune checkpoint molecules. In vitro experiments confirmed that inhibiting S1P3 reduced glioma cell proliferation and migration. Conclusion: S1P3 serves as a potential prognostic biomarker in LGG and plays a critical role in TME immune cell infiltration. Understanding the S1P3-regulated pathways could provide new therapeutic targets for glioma treatment. S1P3 glioma tumor microenvironment immune infiltration prognosis signaling pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The fundamental role of sphingosine 1-phosphate (S1P) is a lipid mediator generated by sphingolipid metabolism, which is present in the extracellular environment and is engaged in various physiological and pathological processes, with the main function of regulating cell-matrix and cell-cell adhesion, significantly influencing cell proliferation, migration, invasion, and differentiation [ 1 ]. S1P has five distinct G protein (G12/13, Gi/o, and Gq) coupled receptors on the cell surface, designated S1P1–5[ 2 ]. Multiple studies indicate that the S1P/S1P3 axis is closely linked with the proliferation, migration, and angiogenesis in various types of human cancer cells, including breast cancer, ovarian cancer, ependymomas, and nasopharyngeal carcinoma cells[ 3 – 6 ]. S1P3 has the ability to couple to the Gi/o, Gq, and G12/13 families, activating small GTPases like Rho, Rac, and Ras7. S1P3 is notably prevalent in the central nervous system (CNS), immune system, and cardiovascular system in humans [ 7 ]. Low-grade gliomas (LGG), which are classified as WHO grade II and III tumors, are a frequent type of tumor in the central nervous system (CNS), making up about 20–29% of all primary CNS tumors. While LGGs can progress to more severe glioblastoma (GBM), they typically have a median survival time of over 7 years, longer than the 5-year survival rate of about 4–5% for GBM. Surgery is the primary treatment for LGG, but due to the presence of resistant glioma stem cells and immune cell infiltration in the tumor microenvironment (TME), it doesn't completely prevent recurrence[ 8 ], [ 9 ]. Additionally, glioma recurrence brought on by tumor remnants has been documented to happen soon after the procedure [ 10 ]. Although the current standard of care involves a mix of surgery, temozolomide (TMZ) chemotherapy, and radiation therapy, strategies adapted to the glioma's innate immune system and stem cell characteristics may ultimately result in a survival advantage. Notably, S1P3 is drawing attention as a current biological research hotspot with significant potential to discover its novel role in cancer, and the study on S1P3 offers new targets for enhancing cancer therapy[ 11 – 13 ]. S1P3 expression has been linked to tumor differentiation and TME formation in cancer cells, according to previous research[ 14 ]. In fact, gliomas are more heterogeneous than other types of cancer. Previous reports have confirmed the high expression of S1P3 in glioblastoma, but the impact of S1P3 on the biological behavior and microenvironment of glioma has not been further investigated. The immune profile of the tumor and infiltration of TME immune cells may influence the developmental plasticity and novel immunotherapy of glioma cells. The identification of lymphatic vessels in the CNS has supported immunotherapy to cross the blood-brain barrier, making it a therapeutic option with significant promise for CNS malignancies[ 15 ]. Some recent studies have shown cancer treatment strategies using S1P3 as a therapeutic target and their associated mechanisms. It was pointed out that S1P3 was significantly upregulated in GBM. The increased expression of S1P3 inhibits the phosphorylation of YAP, which leads to enhanced movement of YAP into the nucleus. This promotes the formation of the YAP-c-MYC complex and supports the translocation of PGAM1, a critical enzyme involved in glycolysis that impacts the energy metabolism in cancer cells[ 11 ]. S1P3 and the TGF-/SMAD3 signaling pathway acted synergistically to promote the development of human lung adenocarcinoma [ 16 ]. Cancer formation involves the inactivation of oncogenes and the activation of proto-oncogenes. As a promising immune target, the significance of S1P3 in glioma has not been well investigated by researchers, and our overall knowledge of the influence of S1P3 expression on the development of glioma and the relevant mechanisms is limited. Therefore, it is imperative to conduct a thorough investigation of S1P3 expression in LGG. Additional research on cancer samples categorized according to S1P3 expression will lead to the development of novel LGG therapy strategies. We evaluated S1P3 expression thoroughly and identified the prognostic and immunological properties of LGG and other cancer cells with various S1P3 expressions using the genomic information of 518 LGG samples from the TCGA database in our work. We discovered two distinct immunological phenotypes based on S1P3 expression in LGG: the immune desert dedifferentiation phenotype and the immune activation differentiation phenotype. Additionally, we discovered that several classical oncogenic pathways and DNA damage were related to S1P3 expression. 2. Materials and Methods 2.1 Cell culture We used human astrocytes HA-1800, M2 macrophages, oligodendroglioma HS683 cell line, and U251 glioma cell line obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA) for in vitro experiments. All cells were maintained in DMEM supplemented with 10% FCS, 2 mM non-essential amino acids, and 2 mM glutamine at 37°C, 5% CO2, and 95% humidity. In S1P (Sigma-Aldrich, Germany) and S1P3 inhibitor (CAY10444, CAYMAN Chemicals, Michigan, USA) stimulation experiments, glioma cells were cultured in DMEM containing 0.05% FCS. 2.2 Quantitative real-time PCR (qRT‐PCR) Using Trizol reagent, we isolated total RNA from cultured normal and tumor cells (Life Technologies Corporation). The first strand of cDNA was synthesized using a High-Capacity cDNA Reverse Transcription Kit (Fermentas). S1P3 (Hs01019574_m1) and eukaryotic 18S rRNA endogenous control (4310893E), two on-demand gene expression assays from Applied Biosystems, were utilized to assess expression levels. Quantitative real-time PCR was performed using a 7900 HT Fast Real-Time PCR system. Subsequently, the level of each mRNA was normalized to 18S rRNA, and fold changes were calculated using the relative quantification (2-ΔΔCt) method. 2.3 Cell viability analysis At a density of 15,000 cells per well, glioma cells were inoculated into 96-well plates. The medium was withdrawn after 24 hours of incubation. Following initial preparation, the cells were incubated for 48 hours in fresh medium supplemented with either CAY10444, S1P, or M2 cells. The medium in the multi-well plate was withdrawn once the incubation period had passed and replaced with new medium that contained 10% resazurine. The plate was placed back into the incubator until the medium turned from blue to pink. 2.4 Boyden chamber assay The Transwell migration assay was performed using a Boyden chamber. Glioma cells were first cultured in 0.05% FCS medium for 24 hours. After digestion, 5000 cells in 30µL of 0.05% FCS medium were seeded into the upper wells of the chamber. The bottom wells were filled with medium containing 10% FCS to serve as a migration stimulus. An 8µm pore size polycarbonate membrane was located between the upper and lower chambers. Glioma cells were treated with CAY10444 or M2 macrophages' conditioned medium for 3 h. After incubation, cells on the membrane were fixed with 4% paraformaldehyde and stained with crystal violet solution for 30 minutes. They were then counted using ImageJ cell counting software. 2.5 Data Acquisition and Bioinformatic Analysis All the data on gliomas collected in our study were derived from the Cancer Genome Atlas dataset (TCGA-LGG, https://portal.gdc.cancer.gov/projects/TCGA-LGG ). 518 low-grade glioma patients were collected for further study, and their clinical information and gene expression patterns were analyzed. We utilized the R programming language to compare normalized RNA-sequencing data against S1P3 gene expression data. We first identified the genes associated with S1P3, and then their correlation with S1P expression was analyzed by Pearson analysis. By combining data from Genotype-Tissue Expression (GTEx) and TCGA, it was also possible to detect the differential expression of S1P3 in different tissues and malignancies. We used R and the R Bioconductor packages to analyze all of our data (version 3.6.1). 2.6 Gene set variation analysis and Gene Ontology annotation In our study, genes within the low and high S1P3 expression groups were differentially analyzed using the limma and DESeq2 packages in the R programming language. The intersection of these analyses was used to identify differentially expressed genes (DEGs). The variation in biological processes (BP) among different S1P3 expression groups was assessed using Gene Set Variation Analysis (GSVA) and the GSVA package in R. All biological functions were characterized using the Kyoto Encyclopedia of Genes and Genomes (KEGG) gene set from the MSigDB database v7.1. Additionally, gene ontology (GO) annotation of S1P3-related genes was conducted with the clusterProfiler package in R, using a false discovery rate (FDR) cutoff of < 0.05. 2.7 Assessment of independent prognostic factors Using the RMS package in R, we ran a nomogram-based model to illustrate the relationship between survival rates and specific variables. To evaluate the potential of our model as an independent prognostic indicator, we performed both univariate and multivariate Cox regression analyses for overall survival (OS) and progression-free survival (PFS). We also assessed the prognostic performance of our model using the area under the curve (AUC) and receiver operating characteristic (ROC) analysis, utilizing the "survival ROC" package in R. 2.8 Estimation of TME immune cell infiltration and assessment of the correlation of S1P3 gene features with other relevant BP We utilized the single sample gene set enrichment analysis (ssGSEA) algorithm to estimate the relative abundance of each type of cell infiltrating the glioma tumor microenvironment (TME). The gene signatures used to identify each type of immune cell infiltrating the TME were derived from Charoentong's research [ 17 ]. Based on their biological roles, we divided immune cells into pro-tumor immune cells (TAM, imDC, pDC, CD56dimNK, Th2, MDSC, Neutrophil, and Treg) and anti-tumor immune cells (TcmCD4, TcmCD8, NKT, ActCD4, ActCD8, ActDC, CD56briNK, Th1, Th17, and NK). Furthermore, we identified characteristics across different S1P3 expression groups using 29 immunological signatures [ 18 ]. Analysis of glioma gene expression patterns and the diversity of leukocyte subsets using the deconvolution technique CIBERSORT [ 19 ]. To explore the connection between S1P3 gene characteristics and various biological pathways, we collected gene sets encompassing numerous genes related to biological processes (BP) [ 20 ], [ 21 ]. Furthermore, our study includes 10 sets of oncogenic pathways genomes to better explore the mechanisms of S1P3 gene signatures in response to different treatments [ 22 ]. 2.9 Statistical analysis Data from experiments were analyzed by using GraphPad Prism 5.0 and SPSS 22.0. All statistical analyses for our bioinformatics research were conducted using R (version 3.6.1). For comparisons between two groups, the t-test was employed. For comparisons among three or more groups, we used the Kruskal-Wallis and one-way ANOVA tests as non-parametric and parametric methods, respectively. Statistical significance was established using one-way analysis of variance (ANOVA) with a subsequent Student-Newman-Keuls test, applying a significance threshold of p < 0.05. Pearson and distance correlation analyses were performed to determine association coefficients between glioma sample phenotypes (TME immune cell infiltration, immune signature, single gene expression, oncogenic pathways, TMB, MSI, CD274, CD8A) and expression of S1P3. The "survminer" package in R was utilized to identify the optimal cutoff point for each data set subgroup. Hazard ratios for S1P3 expression and other prognostic factors were calculated using Cox regression models. Statistical results were considered significant at a p-value of less than 0.05. 3. Results 3.1 The landscape of genetic variation of S1P3 in glioma We initially verified S1P3 expression in healthy brain tissue in the GTEx database before examining S1P3 expression levels in glioma cells. S1P3 was widely expressed in brain tissue (Figure S1A). Next, we confirmed S1P3 expression in LGG and GBM by qRT-PCR. S1P3 expression was significantly greater in HS683 and U251 cells than in HA-1800 cells, and S1P3 expression was higher in U251 cells than in HS683 cells (Fig. 1 A). By examining S1P3 expression levels in normal, LGG, and GBM tissues, the S1P3 expression pattern in gliomas was further confirmed. The findings demonstrated that gliomas have considerably higher levels of S1P3 expression. In GBM tissues, the difference was more noticeable than in LGG tissues (Fig. 1 B-C, S1 B). Moreover, S1P3 was differentially expressed in different histological subtypes of LGG, with the highest expression level in astrocytomas (Fig. 1 D). Immunohistochemical staining corroborated the findings from database analyses, demonstrating an upregulation of S1P3 expression in glioma tissues as compared to normal brain tissues (Fig. 1 E). Furthermore, it was observed that S1P3 expression levels were significantly elevated in GBM relative to LGG. We examined OS and PFS in patients with low and high S1P3 expression (Fig. 1 F, S1 C) to examine the relationship between S1P3 expression and patient survival. Patients in the low group fared far better in terms of survival than those in the high group. We analyzed S1P3 expression variations amongst molecular subtypes to further evaluate the clinical influence of S1P3 overexpression in LGG. According to our findings, the grade, IDH mutation status, MGMT promoter status, x1p19q codeletion status, and primary/recurrence type were all substantially correlated with S1P3 expression (Fig. 1 G-K). Our further investigation through univariate and multivariate Cox regression analyses suggests that S1P3 expression might be an independent factor affecting the prognosis of LGG, as indicated by its influence on OS (Fig. 1 L-M). We also examined the distribution of somatic mutations between groups with high and low S1P3 expression (Fig. 1 N-O). It was found that the rate of IDH1 mutations [ 23 ], which are associated with a better prognosis for LGG, was significantly higher in the low expression group (86%) compared to the high expression group (65%). Conversely, the high expression group exhibited a greater tumor TP53 mutation burden (59%) compared to the low expression group (34%), aligning with the poorer prognosis observed in glioma patients with high TP53 expression [ 24 ]. 3.2 Establishment of a prognostic prediction nomogram for glioma associated with S1P3 expression We conducted ROC analysis on 518 samples to further evaluate the prognostic value of S1P3 expression in gliomas. As shown in Fig. 2 A, the 1-year survival AUC linked with S1P3 expression was 0.650, the 3-year survival AUC was 0.684 (Fig. 2 B), and the 5-year AUC was 0.730 (Fig. 2 C). The results of the ROC study reconfirmed that S1P3 expression could be an independent prognostic factor in LGG patients. We developed a nomogram incorporating S1P3 expression and clinicopathological variables based on the findings of the ROC study, which can be used as a quantitative method to predict the prognostic risk for LGG (Fig. 2 D). The 1-, 3-, and 5-year survival rates were re-predicted using the nomogram to ensure its accuracy (Fig. 2 E-G), and the C-index was used to gauge its predictive abilities. The final C index value was 0.87361, which means that our nomogram had a high predictive accuracy in terms of survival predictability. Our nomogram could be used not only for a comprehensive analysis of S1P3 expression and other clinical parameters in LGG patients but also to serve as a prediction of patient survival. 3.3 Annotation of classification functions determined by consensus clustering analysis We compared the DEGs across the groups with high and low S1P3 expression in order to better understand the mechanism by which S1P3 overexpression influences glioma development. The DEGs in the S1P3 high expression group were then subjected to KEGG and GO analyses. S1P3 overexpression may be connected to identified oncogenic and immune-related pathways, such as neuroactive ligand, ECM, cAMP, cytokine-cytokine receptor interaction, NF-B, PI3K-Akt, and others, according to KEGG enrichment analysis of DEGs (Fig. 3 A). Further GO enrichment analysis of DEGs suggests that overexpression of S1P3 might play a role in regulating various aspects of immune cell activity, such as T cell activation, regulation of cell adhesion, and interactions between immune cells (Fig. 3 B). Specifically, overexpression seems to affect processes including T cell activation, positive regulation of cell activation, and regulation of intercellular adhesion. Additionally, the top 15 GO terms for LGG indicate that DEGs are predominantly involved in gene expression, cellular metabolic processes, and immune responses, among others (Fig. 3 C). In addition, we also performed GSEA analysis on LGG tissues with elevated S1P3 expression. In Fig. 3 D-N, we presented representative BP, including cell cycle, DNA replication, cytokine-cytokine receptor interaction, and various signaling pathways such as JAK-STAT, mTOR, MAPK, p53, and TGF-β, among others. These processes are indicative of the roles and impacts in cellular mechanisms and interactions. S1P3 expression in LGG was connected to oncogenic pathways and tumor immunity. High expression played a role in the glioma malignant progression. 3.4 Identification of S1P3-related genes and the effect of S1P3 on the proliferation and migration of glioma cells We looked at S1P3-related genes to learn more about the processes through which S1P3 expression impacts the development of gliomas. The top five positively (Fig. 4 A-E) and the top five negatively (Fig. 4 F-J) linked genes were chosen for further investigation. The five genes with the strongest positive correlations were IQGAP1, ELF4, ECM2, ANQ6, and VIM. The five genes with the strongest negative correlations were RHBDL1, KCNIP2, CKMT1A, CKMT1B, and RUNDC3A. Furthermore, we selected the top 20 LGG somatic mutation genes for the Pearson correlation study with S1P3. The findings revealed that 12 of them, including EGFR, IDH1, IDH2, HMCN1, NF1, NIPBL, PTEN, RYR2, PIK3CA, and TP53, were associated with S1P3 expression (Figure S1D-O). The top 20 differentially expressed genes in LGG were further analyzed, and the results showed that 12 of them were correlated with S1P3 expression, including TNC, SPARC, RP11, PIGY, NMB, MTHFD2, MDFI, LINC01088, CD68, CD44, CCDC80, and AP003391.1 (Figure S2). The results of these genetic correlations suggested that IQGAP1, ELF4, ECM2, ANQ6, and VIM may have synergistic effects with S1P3 high expression to promote the malignant progression of LGG, providing a direction for further research. The study of the effect of S1P3 on glioma cells was then validated by in vitro experiments. Following a 48-hour treatment with 10 and 20 µM CAY10444 on HS683 cells, cell proliferation was significantly reduced (Fig. 4 K). Interestingly, 20 µM CAY10444 inhibited HS683 cells' proliferation more potently than TMZ did. When we examined U251 cells, we found that CAY10444 could also effectively inhibit the proliferation of U251 cells (Fig. 4 L). We found similar outcomes to CAY10444 when TY-52156, a different S1P3-specific inhibitor, was used to block the S1P3 pathway in HS683 and U251 glioma cells. TY-52156 could effectively inhibit the proliferation of HS683 and U251 cells (Fig. 4 M-N). When S1P was used to stimulate U251 and HS683 cells, the cell proliferation increased to 113.35% and 114.35%, respectively. Cell proliferation was significantly lower in U251 and HS683 cells co-treated with 10 µM and 20 µM of CAY10444 and 2.5 µM S1P, respectively, than in the S1P alone treatment group (Fig. 4 O-P). These results suggest that CAY10444 may have an impact on S1P-mediated proliferation in glioma cells. A Boyden chamber assay was performed to further investigate the migration of HS683 and U251 cells. The findings demonstrated that CAY10444 could influence the role of S1P in enhancing glioma cell migration (Fig. 4 Q). 3.5 Characterization of TME immune cell infiltration in gliomas with different S1P3 expression The interaction between tumor cells and the TME is crucial in determining tumor initiation, progression, metastasis, and response to treatment. S1P3 significantly contributes to the formation of the TME and the infiltration of immune cells within it. We initially looked at the enrichment of 23 immune cells in high and low S1P3 expression groups to examine the impact of S1P3 expression on LGG TME immune cell infiltration. Comparing the high expression group to the low expression group, we discovered that the high expression group had an enrichment advantage for nearly all immune cells (Fig. 5 A). Additionally, in comparison to the S1P3 low expression group, practically all immune signatures were elevated in the high expression group (Figure S3A). Interestingly, the high expression group was considerably elevated in both anti-tumor and pro-tumor immune cells and signatures, indicating that the effect of S1P3 expression on tumor immunity is complicated and requires additional research. Additionally, prior research has shown that S1P3 encourages immune cell recruitment by causing leukocytes to adhere to endothelial cells[ 25 ]. We explored the mechanisms involved by investigating the relationship between pro-tumor immune cells (Treg, Th2, CD56dimNK, imDC, TAM, MDSC, Neutrophil, and pDC) and anti-tumor immune cells (ActCD4, ActCD8, TcmCD4, TcmCD8, TemCD4, TemCD8, Th1, Th17, ActDC, CD56briNK, NK, NKT) across different S1P3 expression groups. Specifically, we analyzed the association between pro- and anti-tumor immune cells in the entire sample set as well as in the distinct high and low S1P3 expression groups (Fig. 5 B-D). We found that pro-tumor immune cells had a stronger correlation in the S1P3 high expression group, while anti-tumor immune cells had a stronger correlation in the S1P3 low expression group. We further analyzed the differences in the expression of typical immune-related genes between the high and low S1P3 expression groups to gain a deeper insight into how S1P3 expression affects the TME. There were no discernible variations in the expression of genes linked with checkpoint inhibitors between the high and low S1P3 expression groups (Fig. 5 E). Major Histocompatibility Complex (MHC) expression was notably higher in the group with low S1P3 expression (Fig. 5 G), while the expression of genes associated with immune stimulation was significantly increased in the group with high S1P3 expression (Fig. 5 F). On the basis of this, we hypothesized that varying S1P3 expression, including various DNA damage-related phenotypes, may either limit or enhance the anti-tumor ability of immune cells. Considering the differences in S1P3 expression, we analyzed the enrichment of BP related to DNA damage in both high and low S1P3 expression groups. Our findings indicated that the high expression group exhibited significantly increased levels of angiogenesis, antigen processing machinery, CD8 T effector functions, EMT, FGFR3-related genes, immune checkpoint activity, mismatch repair mechanisms, and pan-F-Target pathways compared to the low expression group (Fig. 5 H). Additionally, we evaluated the enrichment of 10 oncogenic pathways in both high and low S1P3 expression groups, finding that nearly all these pathways were more pronounced in the high expression group (Fig. 5 I). Next, we conducted an OS study on immune cells infiltrating TME, which were influenced by S1P3 expression. In the immune cell high enrichment group compared with the low enrichment group, OS was significantly different, including CD4 + T, CD8 + T, Macrophages, Neutrophils, T cell regulatory tregs (Figure S3 B-F). Our findings were further supported by the fact that the prognosis was worse in the high immune cell enrichment group and was comparable to the prognosis of the high S1P3 group. We used the ESTIMATE algorithm to determine the immuneScore, stromalScore, and ESTIMATEScore for LGG and GBM in order to further explore the probable processes connected to S1P3 expression. The findings demonstrated that S1P3 expression in LGG and GBM (Figs. 5 J-L and S3 G-I) was linked with all three scoring systems. Additionally, we analyzed the correlations between BP and oncogenic pathways in low and high S1P3 expression groups, respectively. We looked at the relationships between BP across the whole sample and the S1P3 high and low expression groups, respectively (Figure S3J-L). It was further demonstrated that the high-expression group was enriched for DNA damage. Then we investigated the correlation between oncogenic pathways in the whole sample, S1P3 high and low expression groups (Figure S3M-O), respectively. It was observed that while oncogenic pathways were more prevalent in the high S1P3 expression group, the interrelationship between these pathways was more pronounced in the low S1P3 expression group compared to the high group, suggesting a complex dynamic that warrants further investigation. We verified the impact of CAY10444 and macrophages on glioma cells in more detail. After 48 hours of treatment, M2 macrophages could reverse the inhibitory proliferative effect of CAY10444 on U251 glioma cells, increasing from 17.69–28.14% (Fig. 6 A). Similar outcomes were shown in HS683 cells, where M2 macrophages may reverse the inhibitory proliferative effect of CAY10444, increasing from 16.75–30.02% (Fig. 6 B). As measured by the Boyden chamber assay experiment, CAY10444 dramatically reduced the ability of M2 macrophages to promote U251 and HS683 cells migration (Fig. 6 C). We identified significant variations in immune cell infiltration, tumor differentiation, and clinical features by comparing the characteristics of immune cell infiltration in the TME between high and low S1P3 expression groups. In terms of immunological traits, the group with high expression had a dedifferentiated immune phenotype that promoted tumor immune infiltration and immunosuppression. The group with low S1P3 expression exhibited a differentiated phenotype that activated the immune system, showing signs of anti-tumor immune infiltration. Conversely, the high S1P3 expression group was associated with increased DNA damage and dedifferentiation compared to the low expression group. 3.6 Expression of S1P3 in pan-cancer and the effect of S1P3 expression on immune cell infiltration across tumor types S1P3 was expressed in a range of cancer tissues after we initially examined the expression levels in a pan-cancer cohort (Figure S4A). Additionally, data from the GTEx database demonstrated that although S1P3 expression was consistently greater in tumor tissues than in healthy tissues, certain malignancies still exhibited reverse alterations, indicating that S1P3 expression is not regulated in the same way in all cancers (Fig. 6 D). Since each cancer TME has different immune infiltration characteristics, different immune cells and immune features may be involved. We investigated the relationship between immune cells, their signatures, and S1P3 expression across different cancer types (Fig. 6 E, S4 B). Utilizing ssGSEA on samples from each cell population in the pan-cancer cohort, we observed that S1P3 expression occurs in both anti-tumor and pro-tumor immune cells across the majority of malignancies. This suggests that the influence of S1P3 expression on immune activity may vary and is not universally consistent. Checkpoint blockade immunotherapy is currently a hot topic in glioma treatment, and the key clinically validated biomarkers reflecting treatment response include TME cell infiltration, Tumor mutation burden (TMB), and Microsatellite instability (MSI) (31). Radar plots of labeled TMB and MSI revealed a significant association between S1P3 expression and TMB and MSI in multiple cancers (Fig. 6 F-G). Inconsistent patterns were seen across malignancies throughout the examination of checkpoint blockade immunotherapy markers, with both positive and negative correlations between markers and S1P3 expression. 4. Discussion Short-term recurrence and malignant development of LGG are currently intractable despite vigorous multimodal therapy; therefore, it is essential to understand the signaling pathways that result in fast recurrence and a poor prognosis in order to improve existing treatment approaches. S1P3 inhibitors have been shown to affect GBM cell migration and proliferation. However, results on the S1P/S1P3 signaling system's impact on GBM cell migration and proliferation were partially inconsistent in their conclusions. In the current work, we performed a detailed analysis of S1P3 expression in glioma to investigate the effects of various S1P3 expression patterns on the malignant progression and prognosis of glioma. In addition, we developed a nomogram for predicting survival. In the TCGA database, we examined the S1P3 expression levels of 518 LGG patients and identified two patterns of high and low expression. By conducting ROC analysis, we showed that S1P3 expression can predict prognosis and confirmed that low levels of S1P3 had a substantial survival benefit. We have a better grasp of classical biological pathways with different expression patterns through GO, KEGG, and ssGSEA analyses. To determine the probable pathways by which S1P3 expression promotes carcinogenesis, we also carried out immune cell infiltration, BP, and oncogenic pathways gene analysis. Finally, the results of our study revealed the existence of two immune phenotypes related to S1P3 expression: the low S1P3 expression group displayed an immune differentiation characterized by immune activation, and the high expression group displayed an immune dedifferentiation characterized by immune desert. The findings of this investigation indicate the utility of S1P3 expression as a possible prognosis predictor in LGG patients, which promotes tumor development and immune evasion through a variety of pathways. Our PCR results demonstrated that glioma cells greatly overexpressed S1P3, compared to normal cells. The analysis of glioma samples from the TCGA database revealed that S1P3 expression was much greater in glioma tissue than in normal cells adjacent to normal tissue. This finding validates earlier studies in the field [ 27 ]. High S1P3 expression in LGG patients was substantially correlated with higher WHO grade, histological grade, and worse OS. Although S1P3 expression levels were related to many clinical parameters [ 28 ], the results of our COX and correlation analyses showed that S1P3 expression can not only affect the prognosis of LGG together with other parameters, but also serve as an independent predictor of OS in LGG patients. By using somatic mutational analysis, we were able to demonstrate that the high and low S1P3 expression groups had different distributions of IDH1 mutations and TP53 mutations, with the high expression group having fewer IDH1 mutations and more TP53 mutations. While lower IDH1 mutations [ 29 ] and higher TP53 mutations [ 30 ] were associated with poorer survival in glioma patients, this further confirmed the impact of S1P3 expression on OS in LGG patients. In order to estimate the prognosis of LGG patients, we developed a risk rating system. We then further evaluated the system's efficacy by projecting survival rates for 1, 3, and 5 years. The results demonstrated the high accuracy of our system. Our GSEA analysis indicated that high S1P3 expression correlates with various cellular and molecular processes, including the cell cycle, DNA replication, and several signaling pathways such as JAK-STAT, MAPK, mTOR, p53, TGF-β, as well as PD-L1 expression, PD-1 checkpoint pathway, and RNA degradation. Aberrant activation of the essential signaling pathways JAK-STAT, MAPK, mTOR, p53, and TGF-β showed association with the development of glioma [ 31 – 35 ]. The TCGA dataset was used to study and validate the link between S1P3 and related genes, and the five genes with the most positive associations were found to be ANO6, ECM2, ELF4, IQGAP, and VIM. In comparison, the five most negatively associated genes were CKMT1A, CKMT1B, KCNIP2, RHBDL1, and RUNDC3. ANO6, ECM2, ELF4, IQGAP, and VIM have been shown to be positively associated with the malignant progression of gliomas [ 36 – 40 ]. We further demonstrated by cellular assays that blocking S1P3 expression inhibited the proliferation of glioma cells and that S1P3 inhibitors reversed the facilitative role of S1P in the migration of glioma cells. We identified two different immune patterns based on S1P3 expression that have significantly distinct TME cell infiltration profiles. The high expression group revealed an immunosuppressed immune desert dedifferentiation phenotype, while the low expression group revealed an immune-activated differentiation phenotype corresponding to immune and stromal activation. The immune desert phenotype is known to be associated with immune tolerance and the absence of T cell initiation and activation [ 41 ]. Contrary to this phenotype, the immune activation phenotype does not exhibit an abundance of enriched immune cells. Instead, it is characterized by the presence of numerous immune cells, which are primarily located in the stroma surrounding tumor cell clusters, rather than infiltrating the stroma. The stroma may either be confined to the outer layers of the tumor or may extend into the tumor itself, creating an appearance that the immune cells are situated within the tumor[ 42 ], [ 43 ]. S1P3 has been shown to exert a multiple-factor role in immunity by affecting macrophage chemotaxis and killing, dendritic cell maturation, eosinophil and neutrophil recruitment, as well as facilitating immune cell recruitment by activating leukocyte rolling on endothelial cells [ 25 ]. It has been reported that EMT is a classical process of mesenchymal acquisition by epithelial cells and is related to tumorigenesis, metastasis, invasion, and drug resistance in various cancers [ 44 ]. In addition, EMT can drive the tumorigenic and stemness properties of cells and induce stem cell-like properties in tumor cells. In our study of BP, the S1P3 high-expression group was highly enriched in EMT. This property may lead to alkylator TMZ resistance. Higher levels of TGF-β expression were significantly correlated with poor glioma prognosis, and TGF-β expression facilitated the proliferation and migration of glioma cells and induced the aggressive phenotype of glioma [ 45 ]. In addition, TGF-β-induced EMT in hepatocellular carcinoma cells was correlated with changes in stem cell marker expression [ 46 ]. Our examination of oncogenic pathways also found that TGF-β signaling was significantly enriched in the S1P3 high expression group, suggesting a potential correlation with LGG. Additionally, M2 macrophages, which are integral in tumor progression and commonly regarded as tumor-associated macrophages, contribute to various pro-tumorigenic outcomes in cancer. They are involved in angiogenic and lymphangiogenic regulation, immune suppression, hypoxia induction, tumor cell proliferation, and metastasis. We also demonstrated the reversal of M2 macrophage-induced glioma cell proliferation and migration after blocking the S1P3 pathway in our in vitro experiments. When S1P3 expression patterns and associated immune infiltration patterns were applied to other cancers, no significant concordance was found between cancers. This indicated that S1P3 was heterogeneously expressed in different cancers. However, S1P3 expression was significantly different in almost all cancers and normal tissues. The association between the immune cell infiltration profile of the TME and S1P3 expression in cancer could result in immune dysregulation and unchecked dedifferentiation, impacting tumor progression and treatment outcomes. A study on TMB, MSI, CD274, and CD8A indicated that S1P3 is a potentially effective target for immunotherapy. In the pan-cancer cohort study, the correlation of S1P3 expression with various tumors of different phenotypes might indicate the specificity of immune infiltration in the TME, the differential expression of immune checkpoints, and the diversity of biological processes involved. 5. Conclusions Overall, our findings imply that S1P3 expression may have a significant role in glioma patient prognosis. Our research offers preliminary support for the regulating mechanism of S1P3 expression on LGG and other tumor BP, as well as TME immune cell infiltration. Our nomogram demonstrated good predictive capacity for S1P3 expression alone or in conjunction with other clinical factors to predict LGG prognosis. Additionally, we discovered genes and signaling pathways linked to S1P3 expression in LGG. These offer theoretical backing for further study. Our study will contribute to the creation of prediction tools for glioma prognostics, quantification of S1P3 expression in individual tumors, and potential future application in predicting cancer recurrence and the choice of treatment modality. An earlier version of this paper was presented as a preprint. [47] Declarations Data availabilalsoity The original data presented in the study were included in this article. Further inquiries could be directed to the corresponding author. Acknowledgments This research was funded by the Fund of Tang Du Hospital (No.2021YFJH005, No.2021SHRC033). This manuscript was submitted as a pre-print in the link " https://www.researchsquare.com/article/rs-1251483/v3". Conflict of Interest The authors declare no competing interests. Author Contributions Conceptualization: FC, XX; data curation and formal analysis: JHZ, TH, KLG, XX, and KA; funding acquisition: FC; investigation: FC, and XX; methodology: FC, TH, JXD, KA; project administration: TH and KA; resources: FC; supervision: FC, XX, and LW; validation: FC, KA, and TH; visualization: writing—original draft: FC; writing—review and editing: all authors contributed. All authors have reviewed and approved the manuscript. References Y. Pan, F. Gao, S. Zhao, J. Han, and F. Chen, “Role of the SphK- S1P- S1PRs pathway in invasion of the nervous system by SARS- CoV- 2 infection,” Clin Exp Pharmacol Physiol , vol. 48(5), no. May, pp. 637–650, 2021, doi: 10.1111/1440-1681.13483. A. Kumar and S. Nagar, “Regulation of Immune Cell Migration by Sphingosine-1- Phosphate,” Cell Mol Biol (OMICS) , vol. 61, no. 2, p. 121, 2018. L. Dai, Y. Liu, L. Xie, X. Wu, L. Qiu, and W. Di, “Sphingosine kinase 1/sphingosine-1-phosphate (S1P)/S1P receptor axis is involved in ovarian cancer angiogenesis,” Oncotarget , vol. 8, no. 43, pp. 74947–74961, 2017. E. Kim, J. Kim, S. G. Kim, S. Hwang, C. H. Lee, and A. Moon, “Sphingosine 1-phosphate regulates matrix metalloproteinase-9 expression and breast cell invasion through S1P3-Gαq coupling,” J ķCell Sci , vol. 124, no. Pt 13, pp. 2220–2230, 2011, doi: 10.1242/jcs.076794. L. Magrassi et al. , “EDG3 and SHC3 on chromosome 9q22 are co-amplified in human ependymomas,” Cancer Lett , vol. 290, no. 1, pp. 36–42, 2010, doi: 10.1016/j.canlet.2009.08.023. H. M. Lee et al. , “Oncogenic S1P signalling in EBV-associated nasopharyngeal carcinoma activates AKT and promotes cell migration through S1P receptor 3,” J Pathol , vol. 242, no. 1, pp. 62–72, 2017. X. Fan, D. Zhong, and G. Li, “Recent advances of the function odatf sphingosine 1 ‐ phosphate ( S1P ) receptor S1P3,” J Cell Physiol , vol. 236, no. 3, pp. 1564–1578, 2020, doi: 10.1002/jcp.29958. M. Weller et al. , “Glioma,” Nat Rev Dis Primers , vol. 16;1, no. July, p. 15017, 2015, doi: 10.1038/nrdp.2015.17. M. Lim, Y. Xia, C. Bettegowda, and M. Weller, “Current state of immunotherapy for glioblastoma,” Nat Rev Clin Oncol , vol. 15, no. 7, pp. 422–442, 2018. M. Weller et al. , “Glioma,” Nat Rev Dis Primers , vol. 16;1, no. July, p. 15017, 2015, doi: 10.1038/nrdp.2015.17. Y. Shen et al. , “S1P/S1PR3 axis promotes aerobic glycolysis by YAP/c-MYC/PGAM1 axis in osteosarcoma,” EBioMedicine , vol. 40, pp. 210–223, 2019. M. Toyomoto et al. , “Article S1PR3 – G 12 -biased agonist ALESIA targets cancer metabolism and promotes glucose starvation Article S1PR3 – G 12 -biased agonist ALESIA targets cancer metabolism and promotes glucose starvation,” Cell Chem Biol , pp. 1–13, 2021, doi: 10.1016/j.chembiol.2021.01.004. C. Physiology, “Intracellular Sphingosine-1-Phosphate Receptor 3 Contributes to Lung Tumor Cell Proliferation,” pp. 539–552, 2021, doi: 10.33594/000000431. M. J. Tate K, Chase Cornelison R, Bhargava S, “TMIC-33. INVESTIGATING S1PR3-MEDIATED GLIOBLASTOMA INVASION MECHANISMS USING 3D HYDROGEL - TISSUE CULTURE INSERT MODEL,” Neuro Oncol , vol. 21, pp. 254–255, 2019. S. Xu, L. Tang, X. Li, F. Fan, and Z. Liu, “Immunotherapy for glioma : Current management and future application,” Cancer Lett , vol. 476, no. February, pp. 1–12, 2020, doi: 10.1016/j.canlet.2020.02.002. S. Chen, Y. Ahn, F. Lonardo, and M. Lee, “TGF-β/SMAD3 Pathway Stimulates Sphingosine-1 Phosphate Receptor 3 Expression: IMPLICATION OF SPHINGOSINE-1 PHOSPHATE RECEPTOR 3 IN LUNG ADENOCARCINOMA PROGRESSION,” J Biol Chem , vol. 3, no. 16, pp. 27343–27353, 2016, doi: 10.1074/jbc.M116.740084. P. Charoentong et al. , “Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade Graphical,” CellReports , vol. 18, no. 1, pp. 248–262, 2017, doi: 10.1016/j.celrep.2016.12.019. Y. He, Z. Jiang, C. Chen, and X. Wang, “Classification of triple-negative breast cancers based on Immunogenomic profiling,” Journal of Experimental & Clinical Cancer Research , vol. 37, no. 1, pp. 1–13, 2018. A. M. Newman et al. , “Robust enumeration of cell subsets from tissue expression profiles,” Nat Methods , vol. 12, no. 5, pp. 453–457, 2015. S. Mariathasan et al. , “TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells,” Nature Publishing Group , vol. 22, no. 554(7693), pp. 544–548, 2018, doi: 10.1038/nature25501. Ş. Yasin et al. , “Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures,” Genome Biol , vol. 17, no. 231, pp. 1–25, 2016, doi: 10.1186/s13059-016-1092-z. F. Sanchez-Vega, M. Mina, and Armenia, “Pathways, Oncogenic Signaling Cancer, The Atlas, Genome,” vol. 173, no. 2, pp. 321–337, 2019, doi: 10.1016/j.cell.2018.03.035.Oncogenic. G. Karpel, M. Trang, T. T. N. Enyuan, and S. Markus, “Novel IDH1 ‑ Targeted Glioma Therapies,” CNS Drugs , vol. 33, no. 12, pp. 1155–1166, 2019, doi: 10.1007/s40263-019-00684-6. Y. Zhang et al. , “The p53 Pathway in Glioblastoma,” Cancers (Basel) , vol. 10, no. 9, p. 297, 2019, doi: 10.3390/cancers10090297. A. M. Bryan and M. Del Poeta, “Sphingosine‐1‐phosphate receptors and innate immunity,” Cell Microbiol , vol. 20, no. 5, p. e12836, 2018. C. Razvan et al. , “Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy.,” Science (1979) , vol. 362, p. 6411, 2018. D. B. James Ross, Monica Chau, Brandon Miller, Subhas Mukherjee, Changming Zhang, Jun Kong, Emily Kaissi, Austin Newsam, Darius Mahboubi, Jameson Berry, Carol Tucker-Burden, Daniel BratJames Ross, Monica Chau, Brandon Miller, Subhas Mukherjee, Changming Zhang, “TMIC-14. HYPOXIA INDUCIBLE OLIG2 MEDIATES GLIOMA STEM CELL MIGRATION,” Neuro Oncol , vol. 19, no. suppl_6, p. 246, 2018. Q. Li, Y. Li, C. Lei, Y. Tan, and G. Yi, “Sphingosine-1-phosphate receptor 3 signaling,” Clin Chim Acta , vol. 519, no. November 2020, pp. 32–39, 2021, doi: 10.1016/j.cca.2021.03.025. I. K. Arita H, Narita Y, Yoshida A, Hashimoto N, Yoshimine T, “IDH1/2 mutation detection in gliomas,” Brain Tumor Pathol , vol. 32, no. 2, pp. 79–89, 2014, doi: 10.1007/s10014-014-0197-x. Z. J. Cancer Genome Atlas Research Network, Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, Cooper LA, Rheinbay E, Miller CR, Vitucci M, Morozova O, Robertson AG, Noushmehr H, Laird PW, Cherniack AD, Akbani R, Huse JT, Ciriello G, Poisson LM, Barnholtz-Sloa, “Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas,” N Engl J Med , vol. 372, no. 26, pp. 2481–98, 2015, doi: 10.1056/NEJMoa1402121. I. Ciechomska, C. Jayaprakash, M. Maleszewska, and B. Kaminska, Histone Modifying Enzymes and Chromatin Modifiers in Glioma Pathobiology and Therapy Responses , no. 1202. 2020. doi: 10.1007/978-3-030-30651-9. S. Perreault et al. , “A phase 2 study of trametinib for patients with pediatric glioma or plexiform neurofibroma with refractory tumor and activation of the MAPK/ERK pathway: TRAM-01,” BMC Cancer , vol. 19, no. 1, p. 1250, 2019. X. Li, C. Wu, N. Chen, H. Gu, A. Yen, and L. Cao, “PI3K / Akt / mTOR signaling pathway and targeted therapy for glioblastoma,” Oncotarget , vol. 7, no. 22, pp. 33440–50, 2016. J. Sheng et al. , “p53-targeted lncRNA ST7-AS1 acts as a tumour suppressor by interacting with PTBP1 to suppress the Wnt / β -catenin signalling pathway in glioma,” Cancer Lett , vol. 503, no. November 2020, pp. 54–68, 2021, doi: 10.1016/j.canlet.2020.12.039. C. Zhang et al. , “TGF- β 2 initiates autophagy via Smad and non-Smad pathway to promote glioma cells ’ invasion,” J Exp Clin Cancer Res , vol. 36, no. 1, p. 162, 2017, doi: 10.1186/s13046-017-0628-8. Z. Xuan and Y. Wang, “ANO6 promotes cell proliferation and invasion in glioma through regulating the ERK signaling pathway,” Onco Targets Ther , vol. 20, no. 12, pp. 6721–6731, 2019. N. I. Bernstock JD, Mooney JH, Ilyas A, Chagoya G, Estevez-Ordonez D, Ibrahim A, “Molecular and cellular intratumoral heterogeneity in primary glioblastoma: clinical and translational implications,” J Neurosurg , vol. 23, no. August, pp. 1–9, 2019, doi: 10.3171/2019.5.JNS19364. R. Gong, Z. Li, K. Fu, C. Ma, W. Wang, and J. Chen, “Long Noncoding RNA PVT1 Promotes Stemness and Temozolomide Resistance through miR-365 / ELF4 / SOX2 Axis in Glioma,” Exp Neurobiol , vol. 30, no. 3, pp. 244–255, 2021. C. Gao, C. Liang, Z. Nie, Y. Liu, J. Wang, and D. Zhang, “Alkannin inhibits growth and invasion of glioma cells C6 through IQGAP / mTOR signal pathway,” vol. 8, no. 4, pp. 5287–5294, 2015. G. E. Herrera-oropeza, G. E. Herrera-oropeza, C. Angulo-rojo, M. Hernández-rosales, K. Aviña-padilla, and M. Hernández-rosales, “Glioblastoma multiforme: a multi-omics analysis of driver genes and tumour heterogeneity,” Interface Focus , vol. 11, no. 4, p. 20200072, 2021, doi: 10.1098/rsfs.2020.0072. C. D. Kim JM, “Immune escape to PD-L1/PD-1 blockade: seven steps to success (or failure),” Ann Oncol , vol. 27, no. 8, pp. 1492–504, 2016. T. F. Gajewski, “The next hurdle in cancer immunotherapy: overcoming the non–T-cell–inflamed tumor microenvironment,” in Seminars in oncology , Elsevier, 2015, pp. 663–671. H. Salmon et al. , “Matrix architecture defines the preferential localization and migration of T cells into the stroma of human lung tumors,” J Clin Invest , vol. 122, no. 3, pp. 899–910, 2012. B. Du and J. S. Shim, “Targeting Epithelial–Mesenchymal Transition (EMT) to Overcome Drug Resistance in Cancer,” Molecules , vol. 21, no. 7, p. 965, 2016, doi: 10.3390/molecules21070965. C. Zhang et al. , “TGF- β 2 initiates autophagy via Smad and non-Smad pathway to promote glioma cells ’ invasion,” J Exp Clin Cancer Res , vol. 36, no. 1, p. 162, 2017, doi: 10.1186/s13046-017-0628-8. F. Dituri, S. Mancarella, G. Giannelli, and A. Chieti, “TGF- β as Multifaceted Orchestrator in HCC Progression : Signaling , EMT , Immune Microenvironment , and Novel Therapeutic Perspectives,” Semin Liver Dis , vol. 39, no. 1, pp. 53–69, 2019. Chen, Fan and Ji, Peigang and Sun, Fang and Zhu, Gang and Xie, Xuan and Alare, Kehinde, S1P3 Promotes Low-Grade Glioma Progression and Affects Tumor Microenvironment Infiltration. Available at SSRN: https://ssrn.com/abstract=5151339 or http://dx.doi.org/10.2139/ssrn.5151339 Supplementary Figures Supplementary Figures are not available with this version Figure S1 . S1P3 expression in normal tissues and relationship with somatic mutant genes. A. S1P3 expression in normal tissues in the GTEx database. B. Differential expression of S1P3 between LGG and GBM tissues. C. The DFS in patients with low and high S1P3 expression. D-O. The LGG somatic mutation genes for the Pearson correlation study with S1P3. IDH1 (D), HMCN1 (E), EGFR (F), ARID1B (G), ARID1A (H), IDH2 (I), RYR2 (J), PTEN (K), PIK3CA (L), NIPBL (M), NF1 (N), TP53 (O). Figure S2. Association of S1P3 expression with differentially expressed genes in LGG. A-L. In the top 20 differentially expressed genes in LGG, 12 of them were associated with S1P3 expression. TNC (A), SPARC (B), RP11-40C6.2 (C), PIGY (D), NMB (E), MTHFD2 (F), MDFI (G), LINC01088 (H), CD68 (I), CD44 (J), CCDC80 (K), AP003391.1 (L). Figure S3. Effect of S1P3 expression on LGG microenvironment. A. Effect of S1P3 expression on the immune signatures of glioma TME. B-F. OS analysis of immune cells infiltrating TME affected by S1P3 expression. CD4+ T (B), CD8+ T (C), Macrophages (D), Neutrophils (E), T cell regulatory Tregs (F). G-I. The relationship between S1P3 expression and the three scoring systems in GBM. ESTIMATEScore (G), immuneScore (H), and stromalScore (I). J-L. Correlation between S1P3 expression and BP. M-O Correlation between S1P3 expression and oncogenic pathways. The upper and lower ends of the boxes represented an interquartile range of values. The lines in the boxes represented the median value, and the dots showed outliers. The asterisks represented the statistical P-value (*P < 0.05; **P < 0.01; ***P < 0.001). Figure S4. A. The expression levels of S1P3 in a pan-cancer cohort. B. The correlation between S1P3 expression and immune signatures in the pan-cancer cohort. Supplementary Tables Supplementary Tables are not available with this version Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7329130","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502211414,"identity":"ee5c0c00-e748-43a2-b4d8-5a6910d987cb","order_by":0,"name":"Fan Chen","email":"","orcid":"","institution":"Tangdu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Chen","suffix":""},{"id":502211415,"identity":"2eb6df27-5495-4780-bf34-585bf392a910","order_by":1,"name":"Peigang Ji","email":"","orcid":"","institution":"Tangdu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peigang","middleName":"","lastName":"Ji","suffix":""},{"id":502211416,"identity":"2afb2362-69df-4285-a0e3-50ffc2639e44","order_by":2,"name":"Fang Sun","email":"","orcid":"","institution":"Tangdu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Sun","suffix":""},{"id":502211417,"identity":"f1b9983b-64c6-4231-bb60-9d3879fcbb82","order_by":3,"name":"Gang Zhu","email":"","orcid":"","institution":"Tangdu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Zhu","suffix":""},{"id":502211418,"identity":"51e2a7c4-4a5b-43ac-b201-95246644fda6","order_by":4,"name":"Xuan Xie","email":"","orcid":"","institution":"Hunan Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Xie","suffix":""},{"id":502211419,"identity":"f0e9b2f0-4c0f-41cc-92a7-ec2a1e8f2880","order_by":5,"name":"Kehinde Alare","email":"data:image/png;base64,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","orcid":"","institution":"Ladoke Akintola University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Kehinde","middleName":"","lastName":"Alare","suffix":""},{"id":502211420,"identity":"91bbdcd9-3fc7-4865-a3fc-c72c27349697","order_by":6,"name":"Tirenioluwa Ojo","email":"","orcid":"","institution":"Ladoke Akintola University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Tirenioluwa","middleName":"","lastName":"Ojo","suffix":""}],"badges":[],"createdAt":"2025-08-08 16:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7329130/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7329130/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89408616,"identity":"c8a6d9d7-81c9-49ef-9b1c-f82eab0f5bf5","added_by":"auto","created_at":"2025-08-19 15:39:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2653446,"visible":true,"origin":"","legend":"\u003cp\u003eThe landscape of genetic variation of S1P3 in LGG. A. Relative qPCR expression levels of S1P3 in HA1800, HS683, and U251 cells. B. Differential expression of S1P3 between LGG and normal tissues. C. Differential expression of S1P3 between GBM and normal tissues. D. Differential expression of S1P3 between different pathological subtypes of LGG and normal tissues. T: tumor tissue; N: normal tissue. E. Immunohistochemical staining of S1P3 in normal brain (N), low-grade glioma (LGG), and glioblastoma (GBM). F. Kaplan–Meier overall survival curves of LGG patients for high and low S1P3 expression groups in the TCGA-LGG cohort. G-K. Correlation of S1P3 expression with (G) Grade, (H) IDH status, (I) MGMT promoter status, (J) x1p19q codeletion status, (K) primary/recurrence type. K‐L. Forest plot showing univariate (L) and multivariate (M) Cox regression analyses of S1P3 mRNA levels and clinicopathological variables predictive of overall survival. M-N. The waterfall plot of tumor somatic mutation was established by those with Low S1P3 expression (N) and High S1P3 expression (O). The upper and lower ends of the boxes represented an interquartile range of values. The lines in the boxes represented the median value, and the dots showed outliers. The asterisks represented the statistical P-value (*P \u0026lt; 0.05; **P \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7329130/v1/3586ab9b5309c08cb7a072d9.png"},{"id":89409088,"identity":"7c2ab14f-6bcf-4d44-bfe7-1f7a4db95b36","added_by":"auto","created_at":"2025-08-19 15:47:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":819896,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of S1P3 expression as a prognostic indicator for LGG. A‐C. ROC curves with calculated area under the curve (AUC) for risk prediction in 1 year (A), 3 years (B), and 5 years (C), respectively. D. Nomogram to predict the overall survival of LGG patients based on clinical parameters and S1P3 expression. E‐G: Nomogram‐predicted probabilities of 1year (E), 3 years (F), and 5 years(G) survival.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7329130/v1/168148b8197c42ccee45d8f2.png"},{"id":89408618,"identity":"a76e2561-0a19-46a8-88e1-626488c3f8f3","added_by":"auto","created_at":"2025-08-19 15:39:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2193874,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction among functional annotation of LGG and different S1P3 expression groups. A. Functional annotation for S1P3-related DEGs using KEGG enrichment analysis in the high S1P3 expression group. B. Functional annotation for S1P3-related DEGs using GO enrichment analysis in the high S1P3 expression group. C. GO cluster plot showing a chord dendrogram of the clustering of the expression spectrum of significantly DEGs. D-N. Enrichment of pathways and genes identified by gene set enrichment analysis (GSEA). (D) cell cycle, (E) cytokine-cytokine receptor interaction, (F) DNA replication, (G) JAK-STAT signaling pathway, (H) MAPK signaling pathway, (I) mTOR signaling pathway, (J) p53 signaling pathway, (K) TGF-β signaling pathway, (L) PD-L1 expression and PD-1 checkpoint pathway in cancer, (M) RNA degradation, (N) EGFR tyrosine kinase inhibitor resistance.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7329130/v1/79ba6d5e6dec6809c796b90b.png"},{"id":89409089,"identity":"a2ae05c7-b966-4722-9044-5ea503133f29","added_by":"auto","created_at":"2025-08-19 15:47:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2864830,"visible":true,"origin":"","legend":"\u003cp\u003eS1P3-related genes and S1P3 inhibitors inhibited the proliferation and migration of glioma cells. A-J. Correlation of S1P3 expression in LGG with the expression of other genes. The expression of S1P3 in glioma samples from the CGGA dataset showed positive correlations with IQGAP1 (A), ELF4 (B), ECM2 (C), ANQ6 (D) and VIM (E); and negative correlations with RHBDL1 (F), KCNIP2 (G), CKMT1B (H), CKMT1A (I) and RUNDC3A (J). K-L. Determination of HS683 (K) and U251 (L) cell viability by using the resazurine assay after treatment with TMZ and CAY10444 (0.5, 1, 5, 10, and 20 μM) for 48h. M-N. Determination of HS683 (M) and U251 (N) cell viability by using the resazurine assay after treatment with TMZ and TY-52156 (0.5, 1, 5, 10, and 20 μM) for 48h. O-P. Determination of U251 (O) and HS683 (P) cell viability by using the resazurine assay after treatment with CAY10444 alone or together with S1P for 48h. Q. O. Analysis of HS683 and U251 cell migration using the Boyden chamber assay, treatment with S1P alone or together with 20 μM CAY10444. Cell viability is shown in relation to the 0 μM CAY10444 (100%), mean values and SD, n = 3, One-way analysis of variance with Dunnett's multiple comparison test, *p \u0026lt; 0.05, **p \u0026lt; 0.005 and ***p \u0026lt; 0.001 vs. control.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7329130/v1/a39ee81e3f724f0499e45699.png"},{"id":89409091,"identity":"37fc62fa-0a52-4c30-b62d-6e3d490b29d1","added_by":"auto","created_at":"2025-08-19 15:47:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2084794,"visible":true,"origin":"","legend":"\u003cp\u003eTME immune cell infiltration in LGG with different S1P3 expression. A. The enrichment fraction differences of immune cells between the high S1P3-expressing and low S1P3-expressing groups in LGG. (B-D) Correlation of samples in the total samples (B), in the high S1P3 expression group (C), and low S1P3 expression group (D) between infiltration of cell types executing anti-tumor immunity (ActCD4, ActCD8, TcmCD4, TcmCD8, TemCD4, TemCD8, Th1, Th17, ActDC, CD56briNK, NK, NKT) and cell types executing pro-tumor, immune-suppressive functions (Treg, Th2, CD56dimNK, imDC, TAM, MDSC, Neutrophil, and pDC). E. Differences in the expression of inhibitor genes in the high and low S1P3 expression groups. F. Differences in the expression of stimulator genes in the high and low S1P3 expression groups. G. Differences in the expression of MHC genes in the high and low S1P3 expression groups. H-I. The enrichment differences of typical biological processes (H) and oncogenic pathways (I) between high and low S1P3 expression. J-L. Association between S1P3 expression and immune infiltration and the tumor microenvironment. J. ESTIMATEScore, K. ImmuneScore, L. StromalScore. R coefficient of Pearson's correlation. The upper and lower ends of the boxes represented an interquartile range of values. The lines in the boxes represented the median value, and the dots showed outliers. The asterisks represented the statistical p-value (*P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001, ns, no significant).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7329130/v1/3a8e85bceaff049d42c8997a.png"},{"id":89409094,"identity":"e15cee22-3a8d-45e2-b66d-8f29becb1082","added_by":"auto","created_at":"2025-08-19 15:47:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4182938,"visible":true,"origin":"","legend":"\u003cp\u003eS1P3 inhibitor inhibited the effect of M2 cells on glioma cells. A-B. Determination of U251 (A) and HS683 (B) cell viability by using the resazurine assay after treatment with 20 μM CAY10444 alone or together with M2 cells for 48h. C. Analysis of U251 and HS683 cell migration using the Boyden chamber assay, treatment with M2 alone or together with CAY10444. Cell viability is shown in relation to the 0 μM CAY10444 (100%), mean values and SD, n = 3, One-way analysis of variance with Dunnett's multiple comparison test, *p \u0026lt; 0.05, **p \u0026lt; 0.005 and ***p \u0026lt; 0.001 vs. control. Effect of S1P3 expression on immune cell infiltration across tumor types. D. The expression values of S1P3 in different tumors and corresponding normal tissues. E. Correlations between the S1P3 expression and immune cell fractions for each cancer type (Pearson test). F-G. Radar chart of the correlation between S1P3 expression and TMB (F) and MSI (G). ACC, Adrenocortical carcinoma; BLCA, Bladder Urothelial Carcinoma; BRCA, Breast invasive carcinoma; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, Cholangiocarcinoma; COAD, Colon adenocarcinoma; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA, Esophageal carcinoma; GBM, Glioblastoma multiforme; HNSC, Head and Neck squamous cell carcinoma; KICH, Kidney Chromophobe; KIRC, Kidney renal clear cell carcinoma; KIRP, Kidney renal papillary cell carcinoma; LAML, Acute Myeloid Leukemia; LGG, Lower Grade Glioma; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; LUSC, Lung squamous cell carcinoma; MESO, Mesothelioma; OV, Ovarian serous cystadenocarcinoma; PAAD, Pancreatic adenocarcinoma; PCPG, Pheochromocytoma and Paraganglioma; PRAD, Prostate adenocarcinoma; READ, Rectum adenocarcinoma; SARC, Sarcoma; SKCM, Skin Cutaneous Melanoma; STAD, Stomach adenocarcinoma; TGCT, Testicular Germ Cell Tumors; THCA, Thyroid carcinoma; THYM, Thymoma; UCEC, Uterine Corpus Endometrial Carcinoma; UCS, Uterine Carcinosarcoma; UVM, Uveal Melanoma. The asterisks represented the statistical p-value (*P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001; ****P \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7329130/v1/dffdc0fcd16358659885ad95.png"},{"id":93334579,"identity":"efb92f54-0a5d-4383-ad21-9601a8554a61","added_by":"auto","created_at":"2025-10-12 13:47:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15288336,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7329130/v1/7ba0eaa9-dd23-4777-be8b-3b42e4a53427.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"S1P3 promotes low-grade glioma progression and affects tumor microenvironment infiltration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe fundamental role of sphingosine 1-phosphate (S1P) is a lipid mediator generated by sphingolipid metabolism, which is present in the extracellular environment and is engaged in various physiological and pathological processes, with the main function of regulating cell-matrix and cell-cell adhesion, significantly influencing cell proliferation, migration, invasion, and differentiation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. S1P has five distinct G protein (G12/13, Gi/o, and Gq) coupled receptors on the cell surface, designated S1P1\u0026ndash;5[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Multiple studies indicate that the S1P/S1P3 axis is closely linked with the proliferation, migration, and angiogenesis in various types of human cancer cells, including breast cancer, ovarian cancer, ependymomas, and nasopharyngeal carcinoma cells[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. S1P3 has the ability to couple to the Gi/o, Gq, and G12/13 families, activating small GTPases like Rho, Rac, and Ras7. S1P3 is notably prevalent in the central nervous system (CNS), immune system, and cardiovascular system in humans [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLow-grade gliomas (LGG), which are classified as WHO grade II and III tumors, are a frequent type of tumor in the central nervous system (CNS), making up about 20\u0026ndash;29% of all primary CNS tumors. While LGGs can progress to more severe glioblastoma (GBM), they typically have a median survival time of over 7 years, longer than the 5-year survival rate of about 4\u0026ndash;5% for GBM. Surgery is the primary treatment for LGG, but due to the presence of resistant glioma stem cells and immune cell infiltration in the tumor microenvironment (TME), it doesn't completely prevent recurrence[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, glioma recurrence brought on by tumor remnants has been documented to happen soon after the procedure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although the current standard of care involves a mix of surgery, temozolomide (TMZ) chemotherapy, and radiation therapy, strategies adapted to the glioma's innate immune system and stem cell characteristics may ultimately result in a survival advantage. Notably, S1P3 is drawing attention as a current biological research hotspot with significant potential to discover its novel role in cancer, and the study on S1P3 offers new targets for enhancing cancer therapy[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. S1P3 expression has been linked to tumor differentiation and TME formation in cancer cells, according to previous research[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In fact, gliomas are more heterogeneous than other types of cancer. Previous reports have confirmed the high expression of S1P3 in glioblastoma, but the impact of S1P3 on the biological behavior and microenvironment of glioma has not been further investigated. The immune profile of the tumor and infiltration of TME immune cells may influence the developmental plasticity and novel immunotherapy of glioma cells.\u003c/p\u003e\u003cp\u003eThe identification of lymphatic vessels in the CNS has supported immunotherapy to cross the blood-brain barrier, making it a therapeutic option with significant promise for CNS malignancies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Some recent studies have shown cancer treatment strategies using S1P3 as a therapeutic target and their associated mechanisms. It was pointed out that S1P3 was significantly upregulated in GBM. The increased expression of S1P3 inhibits the phosphorylation of YAP, which leads to enhanced movement of YAP into the nucleus. This promotes the formation of the YAP-c-MYC complex and supports the translocation of PGAM1, a critical enzyme involved in glycolysis that impacts the energy metabolism in cancer cells[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. S1P3 and the TGF-/SMAD3 signaling pathway acted synergistically to promote the development of human lung adenocarcinoma [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Cancer formation involves the inactivation of oncogenes and the activation of proto-oncogenes. As a promising immune target, the significance of S1P3 in glioma has not been well investigated by researchers, and our overall knowledge of the influence of S1P3 expression on the development of glioma and the relevant mechanisms is limited. Therefore, it is imperative to conduct a thorough investigation of S1P3 expression in LGG. Additional research on cancer samples categorized according to S1P3 expression will lead to the development of novel LGG therapy strategies.\u003c/p\u003e\u003cp\u003eWe evaluated S1P3 expression thoroughly and identified the prognostic and immunological properties of LGG and other cancer cells with various S1P3 expressions using the genomic information of 518 LGG samples from the TCGA database in our work. We discovered two distinct immunological phenotypes based on S1P3 expression in LGG: the immune desert dedifferentiation phenotype and the immune activation differentiation phenotype. Additionally, we discovered that several classical oncogenic pathways and DNA damage were related to S1P3 expression.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Cell culture\u003c/h2\u003e\u003cp\u003eWe used human astrocytes HA-1800, M2 macrophages, oligodendroglioma HS683 cell line, and U251 glioma cell line obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA) for in vitro experiments. All cells were maintained in DMEM supplemented with 10% FCS, 2 mM non-essential amino acids, and 2 mM glutamine at 37\u0026deg;C, 5% CO2, and 95% humidity. In S1P (Sigma-Aldrich, Germany) and S1P3 inhibitor (CAY10444, CAYMAN Chemicals, Michigan, USA) stimulation experiments, glioma cells were cultured in DMEM containing 0.05% FCS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Quantitative real-time PCR (qRT‐PCR)\u003c/h2\u003e\u003cp\u003eUsing Trizol reagent, we isolated total RNA from cultured normal and tumor cells (Life Technologies Corporation). The first strand of cDNA was synthesized using a High-Capacity cDNA Reverse Transcription Kit (Fermentas). S1P3 (Hs01019574_m1) and eukaryotic 18S rRNA endogenous control (4310893E), two on-demand gene expression assays from Applied Biosystems, were utilized to assess expression levels. Quantitative real-time PCR was performed using a 7900 HT Fast Real-Time PCR system. Subsequently, the level of each mRNA was normalized to 18S rRNA, and fold changes were calculated using the relative quantification (2-ΔΔCt) method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Cell viability analysis\u003c/h2\u003e\u003cp\u003eAt a density of 15,000 cells per well, glioma cells were inoculated into 96-well plates. The medium was withdrawn after 24 hours of incubation. Following initial preparation, the cells were incubated for 48 hours in fresh medium supplemented with either CAY10444, S1P, or M2 cells. The medium in the multi-well plate was withdrawn once the incubation period had passed and replaced with new medium that contained 10% resazurine. The plate was placed back into the incubator until the medium turned from blue to pink.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Boyden chamber assay\u003c/h2\u003e\u003cp\u003eThe Transwell migration assay was performed using a Boyden chamber. Glioma cells were first cultured in 0.05% FCS medium for 24 hours. After digestion, 5000 cells in 30\u0026micro;L of 0.05% FCS medium were seeded into the upper wells of the chamber. The bottom wells were filled with medium containing 10% FCS to serve as a migration stimulus. An 8\u0026micro;m pore size polycarbonate membrane was located between the upper and lower chambers. Glioma cells were treated with CAY10444 or M2 macrophages' conditioned medium for 3 h. After incubation, cells on the membrane were fixed with 4% paraformaldehyde and stained with crystal violet solution for 30 minutes. They were then counted using ImageJ cell counting software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data Acquisition and Bioinformatic Analysis\u003c/h2\u003e\u003cp\u003eAll the data on gliomas collected in our study were derived from the Cancer Genome Atlas dataset (TCGA-LGG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/projects/TCGA-LGG\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/projects/TCGA-LGG\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e 518 low-grade glioma patients were collected for further study, and their clinical information and gene expression patterns were analyzed. We utilized the R programming language to compare normalized RNA-sequencing data against S1P3 gene expression data. We first identified the genes associated with S1P3, and then their correlation with S1P expression was analyzed by Pearson analysis. By combining data from Genotype-Tissue Expression (GTEx) and TCGA, it was also possible to detect the differential expression of S1P3 in different tissues and malignancies. We used R and the R Bioconductor packages to analyze all of our data (version 3.6.1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Gene set variation analysis and Gene Ontology annotation\u003c/h2\u003e\u003cp\u003eIn our study, genes within the low and high S1P3 expression groups were differentially analyzed using the limma and DESeq2 packages in the R programming language. The intersection of these analyses was used to identify differentially expressed genes (DEGs). The variation in biological processes (BP) among different S1P3 expression groups was assessed using Gene Set Variation Analysis (GSVA) and the GSVA package in R. All biological functions were characterized using the Kyoto Encyclopedia of Genes and Genomes (KEGG) gene set from the MSigDB database v7.1. Additionally, gene ontology (GO) annotation of S1P3-related genes was conducted with the clusterProfiler package in R, using a false discovery rate (FDR) cutoff of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Assessment of independent prognostic factors\u003c/h2\u003e\u003cp\u003eUsing the RMS package in R, we ran a nomogram-based model to illustrate the relationship between survival rates and specific variables. To evaluate the potential of our model as an independent prognostic indicator, we performed both univariate and multivariate Cox regression analyses for overall survival (OS) and progression-free survival (PFS). We also assessed the prognostic performance of our model using the area under the curve (AUC) and receiver operating characteristic (ROC) analysis, utilizing the \"survival ROC\" package in R.\u003c/p\u003e\u003cp\u003e\u003cem\u003e2.8 Estimation of TME immune cell infiltration and assessment of the correlation of S1P3 gene features with other relevant BP\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe utilized the single sample gene set enrichment analysis (ssGSEA) algorithm to estimate the relative abundance of each type of cell infiltrating the glioma tumor microenvironment (TME). The gene signatures used to identify each type of immune cell infiltrating the TME were derived from Charoentong's research [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Based on their biological roles, we divided immune cells into pro-tumor immune cells (TAM, imDC, pDC, CD56dimNK, Th2, MDSC, Neutrophil, and Treg) and anti-tumor immune cells (TcmCD4, TcmCD8, NKT, ActCD4, ActCD8, ActDC, CD56briNK, Th1, Th17, and NK). Furthermore, we identified characteristics across different S1P3 expression groups using 29 immunological signatures [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Analysis of glioma gene expression patterns and the diversity of leukocyte subsets using the deconvolution technique CIBERSORT [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To explore the connection between S1P3 gene characteristics and various biological pathways, we collected gene sets encompassing numerous genes related to biological processes (BP) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, our study includes 10 sets of oncogenic pathways genomes to better explore the mechanisms of S1P3 gene signatures in response to different treatments [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e\u003cp\u003eData from experiments were analyzed by using GraphPad Prism 5.0 and SPSS 22.0. All statistical analyses for our bioinformatics research were conducted using R (version 3.6.1). For comparisons between two groups, the t-test was employed. For comparisons among three or more groups, we used the Kruskal-Wallis and one-way ANOVA tests as non-parametric and parametric methods, respectively. Statistical significance was established using one-way analysis of variance (ANOVA) with a subsequent Student-Newman-Keuls test, applying a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Pearson and distance correlation analyses were performed to determine association coefficients between glioma sample phenotypes (TME immune cell infiltration, immune signature, single gene expression, oncogenic pathways, TMB, MSI, CD274, CD8A) and expression of S1P3. The \"survminer\" package in R was utilized to identify the optimal cutoff point for each data set subgroup. Hazard ratios for S1P3 expression and other prognostic factors were calculated using Cox regression models. Statistical results were considered significant at a p-value of less than 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 The landscape of genetic variation of S1P3 in glioma\u003c/h2\u003e\u003cp\u003eWe initially verified S1P3 expression in healthy brain tissue in the GTEx database before examining S1P3 expression levels in glioma cells. S1P3 was widely expressed in brain tissue (Figure S1A). Next, we confirmed S1P3 expression in LGG and GBM by qRT-PCR. S1P3 expression was significantly greater in HS683 and U251 cells than in HA-1800 cells, and S1P3 expression was higher in U251 cells than in HS683 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). By examining S1P3 expression levels in normal, LGG, and GBM tissues, the S1P3 expression pattern in gliomas was further confirmed. The findings demonstrated that gliomas have considerably higher levels of S1P3 expression. In GBM tissues, the difference was more noticeable than in LGG tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Moreover, S1P3 was differentially expressed in different histological subtypes of LGG, with the highest expression level in astrocytomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Immunohistochemical staining corroborated the findings from database analyses, demonstrating an upregulation of S1P3 expression in glioma tissues as compared to normal brain tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Furthermore, it was observed that S1P3 expression levels were significantly elevated in GBM relative to LGG.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe examined OS and PFS in patients with low and high S1P3 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC) to examine the relationship between S1P3 expression and patient survival. Patients in the low group fared far better in terms of survival than those in the high group. We analyzed S1P3 expression variations amongst molecular subtypes to further evaluate the clinical influence of S1P3 overexpression in LGG. According to our findings, the grade, IDH mutation status, MGMT promoter status, x1p19q codeletion status, and primary/recurrence type were all substantially correlated with S1P3 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eG-K). Our further investigation through univariate and multivariate Cox regression analyses suggests that S1P3 expression might be an independent factor affecting the prognosis of LGG, as indicated by its influence on OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eL-M). We also examined the distribution of somatic mutations between groups with high and low S1P3 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eN-O). It was found that the rate of IDH1 mutations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which are associated with a better prognosis for LGG, was significantly higher in the low expression group (86%) compared to the high expression group (65%). Conversely, the high expression group exhibited a greater tumor TP53 mutation burden (59%) compared to the low expression group (34%), aligning with the poorer prognosis observed in glioma patients with high TP53 expression [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Establishment of a prognostic prediction nomogram for glioma associated with S1P3 expression\u003c/h2\u003e\u003cp\u003eWe conducted ROC analysis on 518 samples to further evaluate the prognostic value of S1P3 expression in gliomas. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, the 1-year survival AUC linked with S1P3 expression was 0.650, the 3-year survival AUC was 0.684 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and the 5-year AUC was 0.730 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The results of the ROC study reconfirmed that S1P3 expression could be an independent prognostic factor in LGG patients. We developed a nomogram incorporating S1P3 expression and clinicopathological variables based on the findings of the ROC study, which can be used as a quantitative method to predict the prognostic risk for LGG (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The 1-, 3-, and 5-year survival rates were re-predicted using the nomogram to ensure its accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-G), and the C-index was used to gauge its predictive abilities. The final C index value was 0.87361, which means that our nomogram had a high predictive accuracy in terms of survival predictability. Our nomogram could be used not only for a comprehensive analysis of S1P3 expression and other clinical parameters in LGG patients but also to serve as a prediction of patient survival.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Annotation of classification functions determined by consensus clustering analysis\u003c/h2\u003e\u003cp\u003eWe compared the DEGs across the groups with high and low S1P3 expression in order to better understand the mechanism by which S1P3 overexpression influences glioma development. The DEGs in the S1P3 high expression group were then subjected to KEGG and GO analyses. S1P3 overexpression may be connected to identified oncogenic and immune-related pathways, such as neuroactive ligand, ECM, cAMP, cytokine-cytokine receptor interaction, NF-B, PI3K-Akt, and others, according to KEGG enrichment analysis of DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Further GO enrichment analysis of DEGs suggests that overexpression of S1P3 might play a role in regulating various aspects of immune cell activity, such as T cell activation, regulation of cell adhesion, and interactions between immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Specifically, overexpression seems to affect processes including T cell activation, positive regulation of cell activation, and regulation of intercellular adhesion. Additionally, the top 15 GO terms for LGG indicate that DEGs are predominantly involved in gene expression, cellular metabolic processes, and immune responses, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eIn addition, we also performed GSEA analysis on LGG tissues with elevated S1P3 expression. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-N, we presented representative BP, including cell cycle, DNA replication, cytokine-cytokine receptor interaction, and various signaling pathways such as JAK-STAT, mTOR, MAPK, p53, and TGF-β, among others. These processes are indicative of the roles and impacts in cellular mechanisms and interactions. S1P3 expression in LGG was connected to oncogenic pathways and tumor immunity. High expression played a role in the glioma malignant progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e3.4 Identification of S1P3-related genes and the effect of S1P3 on the proliferation and migration of glioma cells\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe looked at S1P3-related genes to learn more about the processes through which S1P3 expression impacts the development of gliomas. The top five positively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-E) and the top five negatively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-J) linked genes were chosen for further investigation. The five genes with the strongest positive correlations were IQGAP1, ELF4, ECM2, ANQ6, and VIM. The five genes with the strongest negative correlations were RHBDL1, KCNIP2, CKMT1A, CKMT1B, and RUNDC3A. Furthermore, we selected the top 20 LGG somatic mutation genes for the Pearson correlation study with S1P3. The findings revealed that 12 of them, including EGFR, IDH1, IDH2, HMCN1, NF1, NIPBL, PTEN, RYR2, PIK3CA, and TP53, were associated with S1P3 expression (Figure S1D-O). The top 20 differentially expressed genes in LGG were further analyzed, and the results showed that 12 of them were correlated with S1P3 expression, including TNC, SPARC, RP11, PIGY, NMB, MTHFD2, MDFI, LINC01088, CD68, CD44, CCDC80, and AP003391.1 (Figure S2). The results of these genetic correlations suggested that IQGAP1, ELF4, ECM2, ANQ6, and VIM may have synergistic effects with S1P3 high expression to promote the malignant progression of LGG, providing a direction for further research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe study of the effect of S1P3 on glioma cells was then validated by in vitro experiments. Following a 48-hour treatment with 10 and 20 \u0026micro;M CAY10444 on HS683 cells, cell proliferation was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eK). Interestingly, 20 \u0026micro;M CAY10444 inhibited HS683 cells' proliferation more potently than TMZ did. When we examined U251 cells, we found that CAY10444 could also effectively inhibit the proliferation of U251 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eL). We found similar outcomes to CAY10444 when TY-52156, a different S1P3-specific inhibitor, was used to block the S1P3 pathway in HS683 and U251 glioma cells. TY-52156 could effectively inhibit the proliferation of HS683 and U251 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eM-N). When S1P was used to stimulate U251 and HS683 cells, the cell proliferation increased to 113.35% and 114.35%, respectively. Cell proliferation was significantly lower in U251 and HS683 cells co-treated with 10 \u0026micro;M and 20 \u0026micro;M of CAY10444 and 2.5 \u0026micro;M S1P, respectively, than in the S1P alone treatment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eO-P). These results suggest that CAY10444 may have an impact on S1P-mediated proliferation in glioma cells. A Boyden chamber assay was performed to further investigate the migration of HS683 and U251 cells. The findings demonstrated that CAY10444 could influence the role of S1P in enhancing glioma cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eQ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Characterization of TME immune cell infiltration in gliomas with different S1P3 expression\u003c/h2\u003e\u003cp\u003eThe interaction between tumor cells and the TME is crucial in determining tumor initiation, progression, metastasis, and response to treatment. S1P3 significantly contributes to the formation of the TME and the infiltration of immune cells within it. We initially looked at the enrichment of 23 immune cells in high and low S1P3 expression groups to examine the impact of S1P3 expression on LGG TME immune cell infiltration. Comparing the high expression group to the low expression group, we discovered that the high expression group had an enrichment advantage for nearly all immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Additionally, in comparison to the S1P3 low expression group, practically all immune signatures were elevated in the high expression group (Figure S3A). Interestingly, the high expression group was considerably elevated in both anti-tumor and pro-tumor immune cells and signatures, indicating that the effect of S1P3 expression on tumor immunity is complicated and requires additional research. Additionally, prior research has shown that S1P3 encourages immune cell recruitment by causing leukocytes to adhere to endothelial cells[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We explored the mechanisms involved by investigating the relationship between pro-tumor immune cells (Treg, Th2, CD56dimNK, imDC, TAM, MDSC, Neutrophil, and pDC) and anti-tumor immune cells (ActCD4, ActCD8, TcmCD4, TcmCD8, TemCD4, TemCD8, Th1, Th17, ActDC, CD56briNK, NK, NKT) across different S1P3 expression groups. Specifically, we analyzed the association between pro- and anti-tumor immune cells in the entire sample set as well as in the distinct high and low S1P3 expression groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-D). We found that pro-tumor immune cells had a stronger correlation in the S1P3 high expression group, while anti-tumor immune cells had a stronger correlation in the S1P3 low expression group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe further analyzed the differences in the expression of typical immune-related genes between the high and low S1P3 expression groups to gain a deeper insight into how S1P3 expression affects the TME. There were no discernible variations in the expression of genes linked with checkpoint inhibitors between the high and low S1P3 expression groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Major Histocompatibility Complex (MHC) expression was notably higher in the group with low S1P3 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), while the expression of genes associated with immune stimulation was significantly increased in the group with high S1P3 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). On the basis of this, we hypothesized that varying S1P3 expression, including various DNA damage-related phenotypes, may either limit or enhance the anti-tumor ability of immune cells. Considering the differences in S1P3 expression, we analyzed the enrichment of BP related to DNA damage in both high and low S1P3 expression groups. Our findings indicated that the high expression group exhibited significantly increased levels of angiogenesis, antigen processing machinery, CD8 T effector functions, EMT, FGFR3-related genes, immune checkpoint activity, mismatch repair mechanisms, and pan-F-Target pathways compared to the low expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Additionally, we evaluated the enrichment of 10 oncogenic pathways in both high and low S1P3 expression groups, finding that nearly all these pathways were more pronounced in the high expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). Next, we conducted an OS study on immune cells infiltrating TME, which were influenced by S1P3 expression. In the immune cell high enrichment group compared with the low enrichment group, OS was significantly different, including CD4\u0026thinsp;+\u0026thinsp;T, CD8\u0026thinsp;+\u0026thinsp;T, Macrophages, Neutrophils, T cell regulatory tregs (Figure S3 B-F). Our findings were further supported by the fact that the prognosis was worse in the high immune cell enrichment group and was comparable to the prognosis of the high S1P3 group. We used the ESTIMATE algorithm to determine the immuneScore, stromalScore, and ESTIMATEScore for LGG and GBM in order to further explore the probable processes connected to S1P3 expression. The findings demonstrated that S1P3 expression in LGG and GBM (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ-L and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003eS3\u003c/span\u003eG-I) was linked with all three scoring systems. Additionally, we analyzed the correlations between BP and oncogenic pathways in low and high S1P3 expression groups, respectively. We looked at the relationships between BP across the whole sample and the S1P3 high and low expression groups, respectively (Figure S3J-L). It was further demonstrated that the high-expression group was enriched for DNA damage. Then we investigated the correlation between oncogenic pathways in the whole sample, S1P3 high and low expression groups (Figure S3M-O), respectively. It was observed that while oncogenic pathways were more prevalent in the high S1P3 expression group, the interrelationship between these pathways was more pronounced in the low S1P3 expression group compared to the high group, suggesting a complex dynamic that warrants further investigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe verified the impact of CAY10444 and macrophages on glioma cells in more detail. After 48 hours of treatment, M2 macrophages could reverse the inhibitory proliferative effect of CAY10444 on U251 glioma cells, increasing from 17.69\u0026ndash;28.14% (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Similar outcomes were shown in HS683 cells, where M2 macrophages may reverse the inhibitory proliferative effect of CAY10444, increasing from 16.75\u0026ndash;30.02% (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). As measured by the Boyden chamber assay experiment, CAY10444 dramatically reduced the ability of M2 macrophages to promote U251 and HS683 cells migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). We identified significant variations in immune cell infiltration, tumor differentiation, and clinical features by comparing the characteristics of immune cell infiltration in the TME between high and low S1P3 expression groups. In terms of immunological traits, the group with high expression had a dedifferentiated immune phenotype that promoted tumor immune infiltration and immunosuppression. The group with low S1P3 expression exhibited a differentiated phenotype that activated the immune system, showing signs of anti-tumor immune infiltration. Conversely, the high S1P3 expression group was associated with increased DNA damage and dedifferentiation compared to the low expression group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e3.6 Expression of S1P3 in pan-cancer and the effect of S1P3 expression on immune cell infiltration across tumor types\u003c/em\u003e\u003c/p\u003e\u003cp\u003eS1P3 was expressed in a range of cancer tissues after we initially examined the expression levels in a pan-cancer cohort (Figure S4A). Additionally, data from the GTEx database demonstrated that although S1P3 expression was consistently greater in tumor tissues than in healthy tissues, certain malignancies still exhibited reverse alterations, indicating that S1P3 expression is not regulated in the same way in all cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Since each cancer TME has different immune infiltration characteristics, different immune cells and immune features may be involved. We investigated the relationship between immune cells, their signatures, and S1P3 expression across different cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB). Utilizing ssGSEA on samples from each cell population in the pan-cancer cohort, we observed that S1P3 expression occurs in both anti-tumor and pro-tumor immune cells across the majority of malignancies. This suggests that the influence of S1P3 expression on immune activity may vary and is not universally consistent. Checkpoint blockade immunotherapy is currently a hot topic in glioma treatment, and the key clinically validated biomarkers reflecting treatment response include TME cell infiltration, Tumor mutation burden (TMB), and Microsatellite instability (MSI) (31). Radar plots of labeled TMB and MSI revealed a significant association between S1P3 expression and TMB and MSI in multiple cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G). Inconsistent patterns were seen across malignancies throughout the examination of checkpoint blockade immunotherapy markers, with both positive and negative correlations between markers and S1P3 expression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eShort-term recurrence and malignant development of LGG are currently intractable despite vigorous multimodal therapy; therefore, it is essential to understand the signaling pathways that result in fast recurrence and a poor prognosis in order to improve existing treatment approaches. S1P3 inhibitors have been shown to affect GBM cell migration and proliferation. However, results on the S1P/S1P3 signaling system's impact on GBM cell migration and proliferation were partially inconsistent in their conclusions. In the current work, we performed a detailed analysis of S1P3 expression in glioma to investigate the effects of various S1P3 expression patterns on the malignant progression and prognosis of glioma. In addition, we developed a nomogram for predicting survival.\u003c/p\u003e\u003cp\u003eIn the TCGA database, we examined the S1P3 expression levels of 518 LGG patients and identified two patterns of high and low expression. By conducting ROC analysis, we showed that S1P3 expression can predict prognosis and confirmed that low levels of S1P3 had a substantial survival benefit. We have a better grasp of classical biological pathways with different expression patterns through GO, KEGG, and ssGSEA analyses. To determine the probable pathways by which S1P3 expression promotes carcinogenesis, we also carried out immune cell infiltration, BP, and oncogenic pathways gene analysis. Finally, the results of our study revealed the existence of two immune phenotypes related to S1P3 expression: the low S1P3 expression group displayed an immune differentiation characterized by immune activation, and the high expression group displayed an immune dedifferentiation characterized by immune desert. The findings of this investigation indicate the utility of S1P3 expression as a possible prognosis predictor in LGG patients, which promotes tumor development and immune evasion through a variety of pathways.\u003c/p\u003e\u003cp\u003eOur PCR results demonstrated that glioma cells greatly overexpressed S1P3, compared to normal cells. The analysis of glioma samples from the TCGA database revealed that S1P3 expression was much greater in glioma tissue than in normal cells adjacent to normal tissue. This finding validates earlier studies in the field [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. High S1P3 expression in LGG patients was substantially correlated with higher WHO grade, histological grade, and worse OS. Although S1P3 expression levels were related to many clinical parameters [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the results of our COX and correlation analyses showed that S1P3 expression can not only affect the prognosis of LGG together with other parameters, but also serve as an independent predictor of OS in LGG patients. By using somatic mutational analysis, we were able to demonstrate that the high and low S1P3 expression groups had different distributions of IDH1 mutations and TP53 mutations, with the high expression group having fewer IDH1 mutations and more TP53 mutations. While lower IDH1 mutations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and higher TP53 mutations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] were associated with poorer survival in glioma patients, this further confirmed the impact of S1P3 expression on OS in LGG patients. In order to estimate the prognosis of LGG patients, we developed a risk rating system. We then further evaluated the system's efficacy by projecting survival rates for 1, 3, and 5 years. The results demonstrated the high accuracy of our system. Our GSEA analysis indicated that high S1P3 expression correlates with various cellular and molecular processes, including the cell cycle, DNA replication, and several signaling pathways such as JAK-STAT, MAPK, mTOR, p53, TGF-β, as well as PD-L1 expression, PD-1 checkpoint pathway, and RNA degradation. Aberrant activation of the essential signaling pathways JAK-STAT, MAPK, mTOR, p53, and TGF-β showed association with the development of glioma [\u003cspan additionalcitationids=\"CR32 CR33 CR34\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The TCGA dataset was used to study and validate the link between S1P3 and related genes, and the five genes with the most positive associations were found to be ANO6, ECM2, ELF4, IQGAP, and VIM. In comparison, the five most negatively associated genes were CKMT1A, CKMT1B, KCNIP2, RHBDL1, and RUNDC3. ANO6, ECM2, ELF4, IQGAP, and VIM have been shown to be positively associated with the malignant progression of gliomas [\u003cspan additionalcitationids=\"CR37 CR38 CR39\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. We further demonstrated by cellular assays that blocking S1P3 expression inhibited the proliferation of glioma cells and that S1P3 inhibitors reversed the facilitative role of S1P in the migration of glioma cells.\u003c/p\u003e\u003cp\u003eWe identified two different immune patterns based on S1P3 expression that have significantly distinct TME cell infiltration profiles. The high expression group revealed an immunosuppressed immune desert dedifferentiation phenotype, while the low expression group revealed an immune-activated differentiation phenotype corresponding to immune and stromal activation. The immune desert phenotype is known to be associated with immune tolerance and the absence of T cell initiation and activation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Contrary to this phenotype, the immune activation phenotype does not exhibit an abundance of enriched immune cells. Instead, it is characterized by the presence of numerous immune cells, which are primarily located in the stroma surrounding tumor cell clusters, rather than infiltrating the stroma. The stroma may either be confined to the outer layers of the tumor or may extend into the tumor itself, creating an appearance that the immune cells are situated within the tumor[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. S1P3 has been shown to exert a multiple-factor role in immunity by affecting macrophage chemotaxis and killing, dendritic cell maturation, eosinophil and neutrophil recruitment, as well as facilitating immune cell recruitment by activating leukocyte rolling on endothelial cells [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It has been reported that EMT is a classical process of mesenchymal acquisition by epithelial cells and is related to tumorigenesis, metastasis, invasion, and drug resistance in various cancers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, EMT can drive the tumorigenic and stemness properties of cells and induce stem cell-like properties in tumor cells. In our study of BP, the S1P3 high-expression group was highly enriched in EMT. This property may lead to alkylator TMZ resistance. Higher levels of TGF-β expression were significantly correlated with poor glioma prognosis, and TGF-β expression facilitated the proliferation and migration of glioma cells and induced the aggressive phenotype of glioma [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In addition, TGF-β-induced EMT in hepatocellular carcinoma cells was correlated with changes in stem cell marker expression [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Our examination of oncogenic pathways also found that TGF-β signaling was significantly enriched in the S1P3 high expression group, suggesting a potential correlation with LGG. Additionally, M2 macrophages, which are integral in tumor progression and commonly regarded as tumor-associated macrophages, contribute to various pro-tumorigenic outcomes in cancer. They are involved in angiogenic and lymphangiogenic regulation, immune suppression, hypoxia induction, tumor cell proliferation, and metastasis. We also demonstrated the reversal of M2 macrophage-induced glioma cell proliferation and migration after blocking the S1P3 pathway in our in vitro experiments.\u003c/p\u003e\u003cp\u003eWhen S1P3 expression patterns and associated immune infiltration patterns were applied to other cancers, no significant concordance was found between cancers. This indicated that S1P3 was heterogeneously expressed in different cancers. However, S1P3 expression was significantly different in almost all cancers and normal tissues. The association between the immune cell infiltration profile of the TME and S1P3 expression in cancer could result in immune dysregulation and unchecked dedifferentiation, impacting tumor progression and treatment outcomes. A study on TMB, MSI, CD274, and CD8A indicated that S1P3 is a potentially effective target for immunotherapy. In the pan-cancer cohort study, the correlation of S1P3 expression with various tumors of different phenotypes might indicate the specificity of immune infiltration in the TME, the differential expression of immune checkpoints, and the diversity of biological processes involved.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOverall, our findings imply that S1P3 expression may have a significant role in glioma patient prognosis. Our research offers preliminary support for the regulating mechanism of S1P3 expression on LGG and other tumor BP, as well as TME immune cell infiltration. Our nomogram demonstrated good predictive capacity for S1P3 expression alone or in conjunction with other clinical factors to predict LGG prognosis. Additionally, we discovered genes and signaling pathways linked to S1P3 expression in LGG. These offer theoretical backing for further study. Our study will contribute to the creation of prediction tools for glioma prognostics, quantification of S1P3 expression in individual tumors, and potential future application in predicting cancer recurrence and the choice of treatment modality. An earlier version of this paper was presented as a preprint. [47]\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availabilalsoity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data presented in the study were included in this article. Further inquiries could be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Fund of Tang Du Hospital (No.2021YFJH005, No.2021SHRC033). This manuscript was submitted as a pre-print in the link \u0026nbsp;\"\u0026nbsp;https://www.researchsquare.com/article/rs-1251483/v3\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: FC, XX; data curation and formal analysis: JHZ, TH, KLG, XX, and KA; funding acquisition: FC; investigation: FC, and XX; methodology: FC, TH, JXD, KA; project administration: TH and KA; resources: FC; supervision: FC, XX, and LW; validation: FC, KA, and TH; visualization: writing—original draft: FC; writing—review and editing: all authors contributed. All authors have reviewed and approved the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eY. Pan, F. Gao, S. Zhao, J. Han, and F. Chen, \u0026ldquo;Role of the SphK- S1P- S1PRs pathway in invasion of the nervous system by SARS- CoV- 2 infection,\u0026rdquo; \u003cem\u003eClin Exp Pharmacol Physiol\u003c/em\u003e, vol. 48(5), no. May, pp. 637\u0026ndash;650, 2021, doi: 10.1111/1440-1681.13483.\u003c/li\u003e\n \u003cli\u003eA. Kumar and S. Nagar, \u0026ldquo;Regulation of Immune Cell Migration by Sphingosine-1- Phosphate,\u0026rdquo; \u003cem\u003eCell Mol Biol (OMICS)\u003c/em\u003e, vol. 61, no. 2, p. 121, 2018.\u003c/li\u003e\n \u003cli\u003eL. Dai, Y. Liu, L. Xie, X. Wu, L. Qiu, and W. Di, \u0026ldquo;Sphingosine kinase 1/sphingosine-1-phosphate (S1P)/S1P receptor axis is involved in ovarian cancer angiogenesis,\u0026rdquo; \u003cem\u003eOncotarget\u003c/em\u003e, vol. 8, no. 43, pp. 74947\u0026ndash;74961, 2017.\u003c/li\u003e\n \u003cli\u003eE. Kim, J. Kim, S. G. Kim, S. Hwang, C. H. Lee, and A. Moon, \u0026ldquo;Sphingosine 1-phosphate regulates matrix metalloproteinase-9 expression and breast cell invasion through S1P3-G\u0026alpha;q coupling,\u0026rdquo; \u003cem\u003eJ ķCell Sci\u003c/em\u003e, vol. 124, no. Pt 13, pp. 2220\u0026ndash;2230, 2011, doi: 10.1242/jcs.076794.\u003c/li\u003e\n \u003cli\u003eL. Magrassi \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;EDG3 and SHC3 on chromosome 9q22 are co-amplified in human ependymomas,\u0026rdquo; \u003cem\u003eCancer Lett\u003c/em\u003e, vol. 290, no. 1, pp. 36\u0026ndash;42, 2010, doi: 10.1016/j.canlet.2009.08.023.\u003c/li\u003e\n \u003cli\u003eH. M. Lee \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Oncogenic S1P signalling in EBV-associated nasopharyngeal carcinoma activates AKT and promotes cell migration through S1P receptor 3,\u0026rdquo; \u003cem\u003eJ Pathol\u003c/em\u003e, vol. 242, no. 1, pp. 62\u0026ndash;72, 2017.\u003c/li\u003e\n \u003cli\u003eX. Fan, D. Zhong, and G. Li, \u0026ldquo;Recent advances of the function odatf sphingosine 1 ‐ phosphate ( S1P ) receptor S1P3,\u0026rdquo; \u003cem\u003eJ Cell Physiol\u003c/em\u003e, vol. 236, no. 3, pp. 1564\u0026ndash;1578, 2020, doi: 10.1002/jcp.29958.\u003c/li\u003e\n \u003cli\u003eM. Weller \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Glioma,\u0026rdquo; \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e, vol. 16;1, no. July, p. 15017, 2015, doi: 10.1038/nrdp.2015.17.\u003c/li\u003e\n \u003cli\u003eM. Lim, Y. Xia, C. Bettegowda, and M. Weller, \u0026ldquo;Current state of immunotherapy for glioblastoma,\u0026rdquo; \u003cem\u003eNat Rev Clin Oncol\u003c/em\u003e, vol. 15, no. 7, pp. 422\u0026ndash;442, 2018.\u003c/li\u003e\n \u003cli\u003eM. Weller \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Glioma,\u0026rdquo; \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e, vol. 16;1, no. July, p. 15017, 2015, doi: 10.1038/nrdp.2015.17.\u003c/li\u003e\n \u003cli\u003eY. Shen \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;S1P/S1PR3 axis promotes aerobic glycolysis by YAP/c-MYC/PGAM1 axis in osteosarcoma,\u0026rdquo; \u003cem\u003eEBioMedicine\u003c/em\u003e, vol. 40, pp. 210\u0026ndash;223, 2019.\u003c/li\u003e\n \u003cli\u003eM. Toyomoto \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Article S1PR3 \u0026ndash; G 12 -biased agonist ALESIA targets cancer metabolism and promotes glucose starvation Article S1PR3 \u0026ndash; G 12 -biased agonist ALESIA targets cancer metabolism and promotes glucose starvation,\u0026rdquo; \u003cem\u003eCell Chem Biol\u003c/em\u003e, pp. 1\u0026ndash;13, 2021, doi: 10.1016/j.chembiol.2021.01.004.\u003c/li\u003e\n \u003cli\u003eC. Physiology, \u0026ldquo;Intracellular Sphingosine-1-Phosphate Receptor 3 Contributes to Lung Tumor Cell Proliferation,\u0026rdquo; pp. 539\u0026ndash;552, 2021, doi: 10.33594/000000431.\u003c/li\u003e\n \u003cli\u003eM. J. Tate K, Chase Cornelison R, Bhargava S, \u0026ldquo;TMIC-33. INVESTIGATING S1PR3-MEDIATED GLIOBLASTOMA INVASION MECHANISMS USING 3D HYDROGEL - TISSUE CULTURE INSERT MODEL,\u0026rdquo; \u003cem\u003eNeuro Oncol\u003c/em\u003e, vol. 21, pp. 254\u0026ndash;255, 2019.\u003c/li\u003e\n \u003cli\u003eS. Xu, L. Tang, X. Li, F. Fan, and Z. Liu, \u0026ldquo;Immunotherapy for glioma : Current management and future application,\u0026rdquo; \u003cem\u003eCancer Lett\u003c/em\u003e, vol. 476, no. February, pp. 1\u0026ndash;12, 2020, doi: 10.1016/j.canlet.2020.02.002.\u003c/li\u003e\n \u003cli\u003eS. Chen, Y. Ahn, F. Lonardo, and M. Lee, \u0026ldquo;TGF-\u0026beta;/SMAD3 Pathway Stimulates Sphingosine-1 Phosphate Receptor 3 Expression: IMPLICATION OF SPHINGOSINE-1 PHOSPHATE RECEPTOR 3 IN LUNG ADENOCARCINOMA PROGRESSION,\u0026rdquo; \u003cem\u003eJ Biol Chem\u003c/em\u003e, vol. 3, no. 16, pp. 27343\u0026ndash;27353, 2016, doi: 10.1074/jbc.M116.740084.\u003c/li\u003e\n \u003cli\u003eP. Charoentong \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade Graphical,\u0026rdquo; \u003cem\u003eCellReports\u003c/em\u003e, vol. 18, no. 1, pp. 248\u0026ndash;262, 2017, doi: 10.1016/j.celrep.2016.12.019.\u003c/li\u003e\n \u003cli\u003eY. He, Z. Jiang, C. Chen, and X. Wang, \u0026ldquo;Classification of triple-negative breast cancers based on Immunogenomic profiling,\u0026rdquo; \u003cem\u003eJournal of Experimental \u0026amp; Clinical Cancer Research\u003c/em\u003e, vol. 37, no. 1, pp. 1\u0026ndash;13, 2018.\u003c/li\u003e\n \u003cli\u003eA. M. Newman \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Robust enumeration of cell subsets from tissue expression profiles,\u0026rdquo; \u003cem\u003eNat Methods\u003c/em\u003e, vol. 12, no. 5, pp. 453\u0026ndash;457, 2015.\u003c/li\u003e\n \u003cli\u003eS. Mariathasan \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;TGF\u0026beta; attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells,\u0026rdquo; \u003cem\u003eNature Publishing Group\u003c/em\u003e, vol. 22, no. 554(7693), pp. 544\u0026ndash;548, 2018, doi: 10.1038/nature25501.\u003c/li\u003e\n \u003cli\u003eŞ. Yasin \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures,\u0026rdquo; \u003cem\u003eGenome Biol\u003c/em\u003e, vol. 17, no. 231, pp. 1\u0026ndash;25, 2016, doi: 10.1186/s13059-016-1092-z.\u003c/li\u003e\n \u003cli\u003eF. Sanchez-Vega, M. Mina, and Armenia, \u0026ldquo;Pathways, Oncogenic Signaling Cancer, The Atlas, Genome,\u0026rdquo; vol. 173, no. 2, pp. 321\u0026ndash;337, 2019, doi: 10.1016/j.cell.2018.03.035.Oncogenic.\u003c/li\u003e\n \u003cli\u003eG. Karpel, M. Trang, T. T. N. Enyuan, and S. Markus, \u0026ldquo;Novel IDH1 ‑ Targeted Glioma Therapies,\u0026rdquo; \u003cem\u003eCNS Drugs\u003c/em\u003e, vol. 33, no. 12, pp. 1155\u0026ndash;1166, 2019, doi: 10.1007/s40263-019-00684-6.\u003c/li\u003e\n \u003cli\u003eY. Zhang \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;The p53 Pathway in Glioblastoma,\u0026rdquo; \u003cem\u003eCancers (Basel)\u003c/em\u003e, vol. 10, no. 9, p. 297, 2019, doi: 10.3390/cancers10090297.\u003c/li\u003e\n \u003cli\u003eA. M. Bryan and M. Del Poeta, \u0026ldquo;Sphingosine‐1‐phosphate receptors and innate immunity,\u0026rdquo; \u003cem\u003eCell Microbiol\u003c/em\u003e, vol. 20, no. 5, p. e12836, 2018.\u003c/li\u003e\n \u003cli\u003eC. Razvan \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Pan-tumor genomic biomarkers for PD-1 checkpoint blockade\u0026ndash;based immunotherapy.,\u0026rdquo; \u003cem\u003eScience (1979)\u003c/em\u003e, vol. 362, p. 6411, 2018.\u003c/li\u003e\n \u003cli\u003eD. B. James Ross, Monica Chau, Brandon Miller, Subhas Mukherjee, Changming Zhang, Jun Kong, Emily Kaissi, Austin Newsam, Darius Mahboubi, Jameson Berry, Carol Tucker-Burden, Daniel BratJames Ross, Monica Chau, Brandon Miller, Subhas Mukherjee, Changming Zhang, \u0026ldquo;TMIC-14. HYPOXIA INDUCIBLE OLIG2 MEDIATES GLIOMA STEM CELL MIGRATION,\u0026rdquo; \u003cem\u003eNeuro Oncol\u003c/em\u003e, vol. 19, no. suppl_6, p. 246, 2018.\u003c/li\u003e\n \u003cli\u003eQ. Li, Y. Li, C. Lei, Y. Tan, and G. Yi, \u0026ldquo;Sphingosine-1-phosphate receptor 3 signaling,\u0026rdquo; \u003cem\u003eClin Chim Acta\u003c/em\u003e, vol. 519, no. November 2020, pp. 32\u0026ndash;39, 2021, doi: 10.1016/j.cca.2021.03.025.\u003c/li\u003e\n \u003cli\u003eI. K. Arita H, Narita Y, Yoshida A, Hashimoto N, Yoshimine T, \u0026ldquo;IDH1/2 mutation detection in gliomas,\u0026rdquo; \u003cem\u003eBrain Tumor Pathol\u003c/em\u003e, vol. 32, no. 2, pp. 79\u0026ndash;89, 2014, doi: 10.1007/s10014-014-0197-x.\u003c/li\u003e\n \u003cli\u003eZ. J. Cancer Genome Atlas Research Network, Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, Cooper LA, Rheinbay E, Miller CR, Vitucci M, Morozova O, Robertson AG, Noushmehr H, Laird PW, Cherniack AD, Akbani R, Huse JT, Ciriello G, Poisson LM, Barnholtz-Sloa, \u0026ldquo;Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas,\u0026rdquo; \u003cem\u003eN Engl J Med\u003c/em\u003e, vol. 372, no. 26, pp. 2481\u0026ndash;98, 2015, doi: 10.1056/NEJMoa1402121.\u003c/li\u003e\n \u003cli\u003eI. Ciechomska, C. Jayaprakash, M. Maleszewska, and B. Kaminska, \u003cem\u003eHistone Modifying Enzymes and Chromatin Modifiers in Glioma Pathobiology and Therapy Responses\u003c/em\u003e, no. 1202. 2020. doi: 10.1007/978-3-030-30651-9.\u003c/li\u003e\n \u003cli\u003eS. Perreault \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;A phase 2 study of trametinib for patients with pediatric glioma or plexiform neurofibroma with refractory tumor and activation of the MAPK/ERK pathway: TRAM-01,\u0026rdquo; \u003cem\u003eBMC Cancer\u003c/em\u003e, vol. 19, no. 1, p. 1250, 2019.\u003c/li\u003e\n \u003cli\u003eX. Li, C. Wu, N. Chen, H. Gu, A. Yen, and L. Cao, \u0026ldquo;PI3K / Akt / mTOR signaling pathway and targeted therapy for glioblastoma,\u0026rdquo; \u003cem\u003eOncotarget\u003c/em\u003e, vol. 7, no. 22, pp. 33440\u0026ndash;50, 2016.\u003c/li\u003e\n \u003cli\u003eJ. Sheng \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;p53-targeted lncRNA ST7-AS1 acts as a tumour suppressor by interacting with PTBP1 to suppress the Wnt / \u0026beta; -catenin signalling pathway in glioma,\u0026rdquo; \u003cem\u003eCancer Lett\u003c/em\u003e, vol. 503, no. November 2020, pp. 54\u0026ndash;68, 2021, doi: 10.1016/j.canlet.2020.12.039.\u003c/li\u003e\n \u003cli\u003eC. Zhang \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;TGF- \u0026beta; 2 initiates autophagy via Smad and non-Smad pathway to promote glioma cells \u0026rsquo; invasion,\u0026rdquo; \u003cem\u003eJ Exp Clin Cancer Res\u003c/em\u003e, vol. 36, no. 1, p. 162, 2017, doi: 10.1186/s13046-017-0628-8.\u003c/li\u003e\n \u003cli\u003eZ. Xuan and Y. Wang, \u0026ldquo;ANO6 promotes cell proliferation and invasion in glioma through regulating the ERK signaling pathway,\u0026rdquo; \u003cem\u003eOnco Targets Ther\u003c/em\u003e, vol. 20, no. 12, pp. 6721\u0026ndash;6731, 2019.\u003c/li\u003e\n \u003cli\u003eN. I. Bernstock JD, Mooney JH, Ilyas A, Chagoya G, Estevez-Ordonez D, Ibrahim A, \u0026ldquo;Molecular and cellular intratumoral heterogeneity in primary glioblastoma: clinical and translational implications,\u0026rdquo; \u003cem\u003eJ Neurosurg\u003c/em\u003e, vol. 23, no. August, pp. 1\u0026ndash;9, 2019, doi: 10.3171/2019.5.JNS19364.\u003c/li\u003e\n \u003cli\u003eR. Gong, Z. Li, K. Fu, C. Ma, W. Wang, and J. Chen, \u0026ldquo;Long Noncoding RNA PVT1 Promotes Stemness and Temozolomide Resistance through miR-365 / ELF4 / SOX2 Axis in Glioma,\u0026rdquo; \u003cem\u003eExp Neurobiol\u003c/em\u003e, vol. 30, no. 3, pp. 244\u0026ndash;255, 2021.\u003c/li\u003e\n \u003cli\u003eC. Gao, C. Liang, Z. Nie, Y. Liu, J. Wang, and D. Zhang, \u0026ldquo;Alkannin inhibits growth and invasion of glioma cells C6 through IQGAP / mTOR signal pathway,\u0026rdquo; vol. 8, no. 4, pp. 5287\u0026ndash;5294, 2015.\u003c/li\u003e\n \u003cli\u003eG. E. Herrera-oropeza, G. E. Herrera-oropeza, C. Angulo-rojo, M. Hern\u0026aacute;ndez-rosales, K. Avi\u0026ntilde;a-padilla, and M. Hern\u0026aacute;ndez-rosales, \u0026ldquo;Glioblastoma multiforme: a multi-omics analysis of driver genes and tumour heterogeneity,\u0026rdquo; \u003cem\u003eInterface Focus\u003c/em\u003e, vol. 11, no. 4, p. 20200072, 2021, doi: 10.1098/rsfs.2020.0072.\u003c/li\u003e\n \u003cli\u003eC. D. Kim JM, \u0026ldquo;Immune escape to PD-L1/PD-1 blockade: seven steps to success (or failure),\u0026rdquo; \u003cem\u003eAnn Oncol\u003c/em\u003e, vol. 27, no. 8, pp. 1492\u0026ndash;504, 2016.\u003c/li\u003e\n \u003cli\u003eT. F. Gajewski, \u0026ldquo;The next hurdle in cancer immunotherapy: overcoming the non\u0026ndash;T-cell\u0026ndash;inflamed tumor microenvironment,\u0026rdquo; in \u003cem\u003eSeminars in oncology\u003c/em\u003e, Elsevier, 2015, pp. 663\u0026ndash;671.\u003c/li\u003e\n \u003cli\u003eH. Salmon \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Matrix architecture defines the preferential localization and migration of T cells into the stroma of human lung tumors,\u0026rdquo; \u003cem\u003eJ Clin Invest\u003c/em\u003e, vol. 122, no. 3, pp. 899\u0026ndash;910, 2012.\u003c/li\u003e\n \u003cli\u003eB. Du and J. S. Shim, \u0026ldquo;Targeting Epithelial\u0026ndash;Mesenchymal Transition (EMT) to Overcome Drug Resistance in Cancer,\u0026rdquo; \u003cem\u003eMolecules\u003c/em\u003e, vol. 21, no. 7, p. 965, 2016, doi: 10.3390/molecules21070965.\u003c/li\u003e\n \u003cli\u003eC. Zhang \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;TGF- \u0026beta; 2 initiates autophagy via Smad and non-Smad pathway to promote glioma cells \u0026rsquo; invasion,\u0026rdquo; \u003cem\u003eJ Exp Clin Cancer Res\u003c/em\u003e, vol. 36, no. 1, p. 162, 2017, doi: 10.1186/s13046-017-0628-8.\u003c/li\u003e\n \u003cli\u003eF. Dituri, S. Mancarella, G. Giannelli, and A. Chieti, \u0026ldquo;TGF- \u0026beta; as Multifaceted Orchestrator in HCC Progression : Signaling , EMT , Immune Microenvironment , and Novel Therapeutic Perspectives,\u0026rdquo; \u003cem\u003eSemin Liver Dis\u003c/em\u003e, vol. 39, no. 1, pp. 53\u0026ndash;69, 2019.\u003c/li\u003e\n \u003cli\u003eChen, Fan and Ji, Peigang and Sun, Fang and Zhu, Gang and Xie, Xuan and Alare, Kehinde, S1P3 Promotes Low-Grade Glioma Progression and Affects Tumor Microenvironment Infiltration. Available at SSRN: https://ssrn.com/abstract=5151339 or http://dx.doi.org/10.2139/ssrn.5151339\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Figures","content":"\u003cp\u003eSupplementary Figures are not available with this version\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eS1P3 expression in normal tissues and relationship with somatic mutant genes. A. S1P3 expression in normal tissues in the GTEx database. B. Differential expression of S1P3 between LGG and GBM tissues. C. The DFS in patients with low and high S1P3 expression. D-O. The LGG somatic mutation genes for the Pearson correlation study with S1P3. IDH1 (D), HMCN1 (E), EGFR (F), ARID1B (G), ARID1A (H), IDH2 (I), RYR2 (J), PTEN (K), PIK3CA (L), NIPBL (M), NF1 (N), TP53 (O).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2.\u0026nbsp;\u003c/strong\u003eAssociation of S1P3 expression with differentially expressed genes in LGG. A-L. In the top 20 differentially expressed genes in LGG, 12 of them were associated with S1P3 expression. TNC (A), SPARC (B), RP11-40C6.2 (C), PIGY (D), NMB (E), MTHFD2 (F), MDFI (G), LINC01088 (H), CD68 (I), CD44 (J), CCDC80 (K), AP003391.1 (L).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3.\u003c/strong\u003e Effect of S1P3 expression on LGG microenvironment. A. Effect of S1P3 expression on the immune signatures of glioma TME. B-F. OS analysis of immune cells infiltrating TME affected by S1P3 expression. CD4+ T (B), CD8+ T (C), Macrophages (D), Neutrophils (E), T cell regulatory Tregs (F). G-I. The relationship between S1P3 expression and the three scoring systems in GBM. ESTIMATEScore (G), immuneScore (H), and stromalScore (I). J-L. Correlation between S1P3 expression and BP. M-O Correlation between S1P3 expression and oncogenic pathways. The upper and lower ends of the boxes represented an interquartile range of values. The lines in the boxes represented the median value, and the dots showed outliers. The asterisks represented the statistical P-value (*P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S4.\u003c/strong\u003e A. The expression levels of S1P3 in a pan-cancer cohort. B. The correlation between S1P3 expression and immune signatures in the pan-cancer cohort.\u003c/p\u003e\n"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplementary Tables are not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"S1P3, glioma, tumor microenvironment, immune infiltration, prognosis, signaling pathways","lastPublishedDoi":"10.21203/rs.3.rs-7329130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7329130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e\n\u003cp\u003eSphingosine-1-phosphate receptor 3 (S1P3) has been implicated in the progression of various tumors, but its role in low-grade glioma (LGG) remains unclear. This study investigates the impact of S1P3 expression on glioma progression, prognosis, and immune cell infiltration within the tumor microenvironment (TME).\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eWe analyzed clinicopathological and gene expression data from The Cancer Genome Atlas (TCGA) and performed univariate/multivariate Cox regression analyses to assess the prognostic value of S1P3 in LGG. Gene Set Enrichment Analysis (GSEA) was used to identify signaling pathways associated with S1P3 expression. In vitro validation was performed using quantitative PCR, cell viability assays, wound healing assays, and Boyden chamber migration assays.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eS1P3 overexpression was significantly associated with poor overall survival and molecular subtypes of LGG. GSEA revealed that S1P3 upregulation was linked to key oncogenic pathways, including DNA replication, cell cycle, MAPK, p53, and TGF-β signaling. Moreover, S1P3 expression correlated with increased infiltration of immune cells, including macrophages and T cells, as well as higher levels of immune checkpoint molecules. In vitro experiments confirmed that inhibiting S1P3 reduced glioma cell proliferation and migration.\u003c/p\u003e\n\u003cp\u003eConclusion:\u003c/p\u003e\n\u003cp\u003eS1P3 serves as a potential prognostic biomarker in LGG and plays a critical role in TME immune cell infiltration. Understanding the S1P3-regulated pathways could provide new therapeutic targets for glioma treatment.\u003c/p\u003e","manuscriptTitle":"S1P3 promotes low-grade glioma progression and affects tumor microenvironment infiltration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 15:39:31","doi":"10.21203/rs.3.rs-7329130/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ac53e46-6ed8-402b-9783-bd7a6d16cd75","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-12T13:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 15:39:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7329130","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7329130","identity":"rs-7329130","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}