Role of SYT11 in human pan-cancer using comprehensive approaches

Role of SYT11 in human pan-cancer using comprehensive approaches | 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 Role of SYT11 in human pan-cancer using comprehensive approaches Kyunghee Noh, Wonbeak Yoo, Kyung Chan Park This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3909545/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jun, 2024 Read the published version in European Journal of Medical Research → Version 1 posted 12 You are reading this latest preprint version Abstract Background Synaptotagmin 11 (SYT11) plays a pivotal role in neuronal vesicular trafficking and exocytosis. However, no independent prognostic studies have focused on various cancers. In this study, we aimed to summarize the clinical significance and molecular landscape of SYT11 in various tumor types. Methods Using several available public databases, we investigated abnormal SYT11 expression in different tumor types and its potential clinical association with prognosis, methylation profiling, immune infiltration, gene enrichment analysis, and protein–protein interaction analysis, and identified common pathways. Results TCGA and Genotype-Tissue Expression (GTEx) showed that SYT11 was widely expressed across tumor and corresponding normal tissues. Survival analysis showed that SYT11 expression correlated with the prognosis of seven cancer types. Additionally, SYT11 mRNA expression was not affected by promoter methylation, but regulated by certain miRNAs and associated with cancer patient prognosis. Moreover, aberrant SYT11 expression was significantly associated with immune infiltration. Pathway enrichment analysis revealed that the biological and molecular processes of SYT11 were related to clathrin-mediated endocytosis, Rho GTPase signaling, and cell motility-related functions. Conclusions Our results provide a clear understanding of the role of SYT11 in various cancer types and suggest that SYT11 may be of prognostic and clinical significance. SYT11 prognosis genetic alteration immune cell infiltration enrichment analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cancer is one of the leading causes of death worldwide [ 1 ] and considered an important factor affecting healthy human life and decreasing life expectancy. Therefore, a novel strategy to overcome cancer is one of the most urgent public health challenges. In the past few years, the advent of standard chemotherapy and supportive therapy has provided new ideas for cancer treatment [ 2 – 4 ]; however, the prognosis of patients with advanced cancer remains poor. Therefore, there is an urgent need to identify novel therapeutic targets with enhanced prognostic potential. Synaptotagmin-11 (SYT11), a member of the synaptotagmin (SYT) family, is almost exclusively expressed in the brain tissue [ 5 ]. Recent studies have reported that SYT11 is a functional protein that binds calcium, phospholipids, or SNARE proteins throughout the neuronal cell body, axons, and dendrites and mediates vesicular trafficking [ 6 , 7 ]. Genome-associated studies and experimental models have shown that SYT11 dysfunction is associated with Parkinson’s disease (PD) and susceptibility to schizophrenia in [ 8 – 12 ]. Interestingly, Bajaj et al. found that SYT11 plays a crucial role in tumorigenic properties such as invasiveness and metastasis in tumor microenvironment (TME) via Golgi-mediated exocytosis in lung cancer [ 13 ]. In addition, it is upregulated with SYT11 expression, associated with the stem-like molecular subtype of gastric cancer, and a prognostic biomarker for histologically classified diffuse-type gastric cancer [ 14 ]. However, owing to the importance of SYT11 in several cancers, new role of SYT11 in various cancers should be investigated. In this study, we examined the expression profile and prognostic value of SYT11 in various cancer types. To further explore the aberrant patterns and possible clinical significance of SYT11 expression, correlation analysis was conducted between SYT11 expression and genetic alteration, methylation, miRNA interaction, and immune cell infiltration. Furthermore, interaction analysis of SYT11-correlated genes, protein–protein interaction (PPI), and functional enrichment analysis were also performed to explore their potential roles in biological and molecular processes. Based on a comprehensive analysis, we aim to provide a new understanding of the clinical value and clarification of SYT11 expression in various tumors. Material and Methods Data acquisition and processing Gene expression profiling and interactive analyses based on TCGA and The Genotype-Tissue Expression (GTEx) information were obtained from GEPIA2 [ 15 ]. To further elucidate the overall survival (OS) and disease-free survival (RFS), we used the GEPIA2 database and further validated the data using the Kaplan–Meier Plotter (KM-plot) with [ 16 ]. The log-rank p-values and hazard ratios (HR) were calculated. The cBioportal for Cancer Genomics database ( https://www.cbioportal.org/ ) was used to analyze genetic alterations in SYT11 in TCGA PanCancer Atlas Studies. Comprehensive analysis of SYT11 methylation The SMART [ 17 ] was used to analyze the differential expression of methylated SYT11 in various cancers, and the UALCAN database [ 18 ] was used to analyze promoter methylation. Bioinformatic analysis of miRNA target prediction To identify potential SYT11 targets, candidate miRNAs were predicted using miRWALK [ 19 ], TargetScan [ 20 ], and miRDB [ 21 ]. We screened 13 common miRNAs in miRWALK and miRDB based on the remaining miRNAs, excluding miRNAs without Pct values from TargetScan. The dbDEMC 3.0 database [ 22 ] was used to identify promising biomarkers for the expression levels of 13 candidate miRNAs in various cancers. Candidate miRNA expression correlation, prognosis, and enrichment analysis with SYT11 were conducted in pan-cancer using the Starbase database [ 23 ]. The criteria for miRNA interactomes were set such that the prediction program with miRanda–PicTar–TargetScan and suggested usage were selected. Exploring immune-related analysis in TME The Tumor Immune Estimation Resource database (TIMER 2.0) was employed to investigate the correlation between SYT11 and tumor-infiltrating immune cells such as CD8 + T cells, macrophages, B cells, NK cells, MDSCs, and CAFs across diverse tumors from TCGA dataset [ 24 ]. The strength of the correlation heatmap with the purity-adjusted Spearman's ρ was statistically significant and automatically calculated online. Functional and pathway enrichment analysis of SYT11 The 24 genes with the strongest correlation with SYT11 were selected using Pathway Commons [ 25 ] and this result was used to explore mRNA expression in various cancers. To further elucidate the functions and pathways, pathway-enriched analysis (including Reactome_2022 and BioPlanet_2019) and ontological analysis (including GO Biological Processes 2023 and GO Molecular Function 2023) for the 25 selected genes forming a cluster with SYT11 were performed using Enrichr [ 26 ] and the p-values were calculated online using Fisher’s exact test. Subsequently, the PPI network was evaluated using STRING to further understand the functions of interaction network for SYT11 protein. The strength and false discovery rates were automatically calculated online. Results Differential SYT11 expression in various cancers According to the results obtained from the TIMER2 database, SYT11 was weakly expressed in most cancers, such as bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), glioblastoma multiforme (GBM), renal hepatocellular carcinoma (KICH), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), and uterine corpus endometrial carcinoma (UCEC) than in adjacent normal tissues, while being strongly expressed in cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (NHSC), liver hepatocellular carcinoma (LIHC), pheochromocytoma and paraganglioma (PCPG), and thyroid carcinoma (THCA) (Fig. 1 A). Since TCGA database contains relatively insufficient information for normal tissues, we also included samples from the GTEx database for further analysis. SYT11 expression in the normal tissues of patients with adrenocortical cancer (ACC), BLCA, CESC, COAD, KICH, LUSC, READ, testicular germ cell tumors (TGCT), and UCEC is lower than the corresponding tumor tissues according to the GTEx database, while the pattern was opposite for the patients with CHOL, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), pancreatic ductal adenocarcinoma (PAAD), PCPG, and skin cutaneous melanoma (SKCM) (Fig. 1 B). However, SYT11 expression in the normal and tumor tissues of the patients with BRCA, ESCA (esophageal carcinoma), GBM, NHSC, KIRC, KRIP, LIHC, LUAD, ovarian serous cystadenocarcinoma (OV), PRAD, sarcoma (SARC), stomach adenocarcinoma (STAD), thyroid carcinoma (THYM), and UCS (uterine carcinosarcoma) were not significantly different (Supplementary Fig. 1). To investigate the SYT11 expression in detail, we generated a pathological stage plot using the GEPIA2 module. SYT11 RNA expression levels were significantly and positively associated with the late clinical stages of BLCA and STAD (Fig. 1 C and Supplementary Fig. 2). Prognostic analysis of SYT11 in pan-cancer To investigate the influence of SYT11 on the prognosis of various tumors, a heat map of the SYT11 gene with significant prognostic value was generated using the GEPIA2 database, and the samples were divided according to the median SYT11 expression. Aberrant SYT11 expression mainly affected the improved prognosis of overall survival (OS) for patients with KIRC (p = 0.00088) and LUAD (p = 0.0053), whereas high SYT11 expression was associated with poor prognosis for patients with ACC (p = 0.054), BLCA (p = 0.05), LAML (p = 0.023), MESO (p = 0.0036), and UVM (p = 0.023) (Fig. 2 A). Moreover, high SYT11 expression displayed unfavorable disease-free survival (RFS) for patients with ACC (p = 0.0083), BLCA (p = 0.02), and COAD (p = 0.031) tumors (Fig. 2 B), but not others (Supplementary Figs. 3 and 4). Subsequently, we assessed the K–M plot to evaluate the relationship between SYT11 expression and prognosis of patients with different tumors. High SYT11 expression was associated with poor OS in patients with UCEC (p = 0.043), whereas patients with KIRC (p = 0.0098) and LUAD (p = 0.004) showed better OS (Fig. 3 C). In addition, high SYT11 expression level was significantly associated with poor RFS in patients with OV (p = 0.033). Genetic alteration of SYT11 in various cancers Since genetic alterations are closely associated with tumorigenesis, the genetic variation of SYT11 in various cancers were determined using the cBioPortal TCGA cohort. As shown in Fig. 3 A, alteration frequencies were high in the patients with UCS, CHOL, and LIHC, and the highest alteration frequency (> 10%) with ‘amplification’ in the patients with UCS. Accordingly, the most common alterations in SYT11 genes were missense (n = 71), truncating (n = 6), fusion (n = 4), and splice (n = 3) mutations, and the T68N mutation (Thr to Asn) was observed in the phosphorylation site. In addition, R342C was the main genetic alteration (one case in PRAD, two cases in UCEC, and one case in COAD) among the missense mutations (Fig. 3 B). Based on the UCS showing the highest genetic alteration frequencies, we further analyzed the association between SYT11 and clinical attributes in UCS-TCGA. In the analysis of the putative copy number, SYT11 expression was the highest in the amplification group compared to that in the other groups, including shallow deletion, diploidy, and gain. Simultaneously, it was positively associated with copy number (Spearman r = 0.24, p = 0.0692; Pearson r = 0.32, p = 0.016; Fig. 3 C). Regarding the association between SYT11 expression and copy number, we identified the molecular profiles of SYT11 genomic alterations. As shown in Fig. 3 D, SNORA80E and UBQLN4 were significantly associated with SYT11 alteration as shown by volcano plots. Additionally, GON4L, RIT1, SCARNA4, SNORA80E, ARHGEF2, KHDC4, LAMTOR2, RAB25, RXFP4, SSR2, and UBQLN4 were significantly associated with SYT11 alterations. Difference of SYT11 methylation level in pan-cancer The Shiny Methylation Analysis Resource Tool (SMART) database was used to analyze the difference in SYT11 methylation levels between normal and primary tumor tissues. As shown in Fig. 4 A, CpG-aggregated SYT11 methylation was significantly lower in tumor tissues than that in corresponding normal tissues for patients with BLCA (p ≤ 0.0001), BRCA (p ≤ 0.01), COAD (p ≤ 0.01), HNSC (p ≤ 0.0001), KIRC (p ≤ 0.0001), LIHC (p ≤ 0.001), LUAD (p ≤ 0.01), LUSC (p ≤ 0.0001), PCPG (p ≤ 0.05), PRAD (p ≤ 0.0001), READ (p ≤ 0.001), and UCEC (p ≤ 0.0001), while being the opposite for patients with CHOL (p ≤ 0.05). Since promoter methylation alters gene expression, we explored the promoter methylation level of SYT11 in tumor and normal tissues using the ULCAN database. The results showed that SYT11 promoter methylation was downregulated in patients with various tumors, including BLCA, BRCA, COAD, CESC, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, TGCT, and UCEC, but was lower in the primary tumor tissue then that in normal tissue only for the patients with CHOL. These results suggest that low SYT11 expression is less strongly associated with promoter methylation in most tumors. Prediction of SYT11 upstream miRNA and differential expression MicroRNAs (miRNAs) play crucial roles in post-transcriptional gene expression via base pairing within mRNAs. Since SYT11 expression is downregulated in various cancers, regulatory miRNAs are possibly highly expressed in cancer. To identify the target miRNAs of SYT11, we used miRNA prediction tools, including miRDB, TargetScan, and miRWalk, and then intersected 13 miRNAs by the Venn diagram (Fig. 5 A). These 13 miRNAs were further analyzed to explore their differential expression profiles using a meta-profile heatmap of tissue samples from various cancer patients and healthy participants (Fig. 5 B). Based on meta-profile heatmap, hsa-miR-19a-3p, hsa-let-7g-5p, hsa-let-7i-5p, and hsa-miR-98-5p showed significant differential expression in tissue samples of cancer patients and healthy participants, and presented binding sites with SYT11 3’-UTR (Fig. 5 C). Simultaneously, biological network analysis showed that miRNA-mediated regulation was mostly enriched in intercellular signaling, environmental information processing, and cytoskeletal interactions, such as the MAPK signaling pathway, ECM receptor interaction, focal adhesion, and adherens junction (Fig. 5 D and Supplementary Table 1). To further assess the relationship between expression and clinical significance, correlation analyses and Kaplan–Meier estimation were conducted between the four candidate miRNAs and SYT11 expression in pan-cancer samples. Among the miRNA/SYT11 pairs, hsa-let-7g-5p/SYT11, hsa-miR-19a-3p/SYT11, and hsa-miR-98-5p/SYT11 were negatively correlated with 11, 15, and 8 tumors, respectively. Conversely, the hsa-let-7i-5p/SYT11 pair was positively associated with most cancers (Fig. 6 A). In terms of clinical survival prognosis, highly expressed hsa-let-7g-5p was linked to poor OS in the patients with THCA, COAD, SARC, and KIRC; hsa-miR-19a-3p in the patients with KIRC, THCA, SKCM, SARC, ACC, DLBC, BRCA, and LAML; hsa-miR-98-5p in the patients with TGCT, PRAD, ESCA, LGG, and HNSC; and hsa-let-7i-5p in the patients with KIRC, LGG, KIRP, and TGCT (Fig. 6 B). These results indicate that the candidate miRNAs may play an important role in reducing SYT11 expression and prognosis. Immune infiltration analysis of SYT11 Since immune cell infiltration plays a crucial role in tumor progression, we investigated the relationship between SYT11 expression and immune cell infiltration in various tumors. As shown in Fig. 7 , SYT11 expression was significantly positively associated with CD8 + T cell (in 14 types of cancer) and macrophage (in 13 types of cancer) infiltration. HNSC, LUSC, STAD, and THCA showed a positive tendency in B cells, but there was no clear trend in natural killer (NK) cells. Interestingly, SYT11 expression in myeloid-derived suppressor cells (MDSCs) showed a significant negative association with almost all cancer types, excluding ACC, MESO, OV, SKCM, and UCEC, while these negative correlations were associated with few CD8 + T cells and macrophages. In addition, SYT11 expression positively correlated with cancer-associated fibroblasts (CAFs) in most cancer types, except for DLBC, GBM, SARC, and UCS. These results suggest that SYT11 plays an important role in immune cell infiltration and may serve as a novel biomarker of various tumors. SYT11-related gene enrichment analysis data To further explore the potential mechanism of SYT11 in various tumors and clinical outcomes, we attempted to obtain a SYT11-interacted gene network (Fig. 8 A). Twenty-four interacting genes and their expression profiles in various tumor and normal tissues are presented in Fig. 8 B. Our results indicated that PDLIM7, SGIP1, DAB2, INPP5K, and PIP5K1B expression was higher in tumor tissues than that in the corresponding normal tissues, whereas the remaining interacting genes showed opposite tendencies. To assess the relationship between SYT11 and these genes, enriched pathway and ontological analyses were performed simultaneously. Pathway enrichment analysis revealed that SYT11 was significantly associated with clathrin-mediated endocytosis, phosphoinositide metabolism, and Rho GTPase activation in Reactom_2022 and phosphatidylinositol metabolism and cell motility signaling pathway in BioPlanet_2019 (Fig. 8 C and Supplementary Table 2). In the ontological analysis, SYT11 was significantly linked with the cellular response to actin nucleation, phosphatidylinositol metabolism, and membrane ruffle formation in GO Biological Process 2023 and diverse phosphatidylinositol-based activities in GO Molecular Function 2023 (Fig. 8 D and Supplementary Table 2). We also assessed the STRING database to obtain the SYT11-interacting proteins to support gene set enrichment analysis. As shown in Fig. 8 E, SYT11 interact with 10 proteins, and these PPIs were further analyzed to explore their biological and molecular processes. The biological process results showed that SYT11-correlated proteins were involved in neurotransmitter secretion, synaptic vesicle transport regulation, and SNARE complex assembly. The molecular process results suggest that SYT11-correlated proteins are linked to syntaxin-1 binding, SNAP receptor activity, SNARE binding, and clathrin binding. Discussion Recently, Bajaj et al. discovered a novel role for SYT11 in epithelial–mesenchymal transition (EMT)-mediated vesicular trafficking in the development of lung cancer invasion and metastasis [ 13 ]. In addition, SYT11 promoted the stem-like molecular subtype of diffuse gastric cancer [ 14 ]. However, whether SYT11 significantly impacts the pathogenesis of different tumors through common molecular mechanisms is not yet known. This study comprehensively explored the underlying molecular role of SYT11 in different tumor types and clinical prognoses using bioinformatics. SYT11 mRNA expression analysis showed the possibility of predicting the diagnosis of certain tumors, such as COL, DLBC, LAML, LGG, PAAD, PCPG, and SKCM, in TCGA, which showed high SYT11 mRNA expression and decreased expression in other tumors. Meanwhile, SYT11 expression was linked to diverse OS and DFS outcomes and poor prognosis in most highly expressed cancers. Although SYT11 expression does not perfectly align with survival prognosis, no reported studies have focused on tumors other than some lung and gastric cancers; therefore, the differential SYT11 expression is considered to be closely related to the survival prognosis of most tumors in this study. Previous studies have demonstrated that multiple genetic and epigenetic events are highly involved in tumor initiation and progression [ 27 – 29 ]. Genetic alteration profiling of SYT11 revealed that amplification is the most common type of tumor, including UCS and missense mutations. In particular, GON4L, RIT1, SCARNA4, SNORA80E, ARHGEF2, KHDC4, LAMTOR2, RAB25, RXFP4, SSR2, and UBQLN4 were more frequently in the SYT11 altered group. Interestingly, these SYT11 co-occurring genes were enriched in pathways fundamental to cell function and metabolism, such as transcriptional regulation, cell survival, G protein-coupled signaling pathway, and ER function, which play an important role in tumor progression. In epigenetic analyses, such as promoter methylation profiling, SYT11 was hypomethylated in most tumor types than that in normal tissues, while the SYT11 expression was not consistent. Based on the inconsistencies between promoter methylation and mRNA expression, the association between miRNAs and gene expression was further investigated. Thus, hsa-miR-19a-3p, hsa-let-7g-5p, hsa-let-7i-5p, and hsa-miR-98-5p negatively regulates SYT11 and can interact with the SYT11 3’-UTR. Among the four miRNAs, hsa-let-7g-5p, hsa-miR-19a-3p, and hsa-miR-98-5p were negatively associated with SYT11 expression, whereas hsa-let-7i-5p was positively associated. Importantly, we found that COAD, THCA, SARC, and KIRC were significantly correlated with miRNA–mRNA expression and OS (Fig. 6 A, 6 B). It was hypothesized that certain miRNAs other than the four predicted miRNAs, particularly hsa-let-7g-5p, may be related to epigenetic regulation and clinical prognosis in certain cancers. The reasons underlying the difference between epigenetic analysis and clinical outcomes in this study warrant further experimental investigation. Nevertheless, our findings provide useful information for further understanding the role of genetic and epigenetic SYT11 alterations. Next, we visualized the immune infiltration landscape in various cancers, which are important TME components [ 30 , 31 ]. Particularly, tumor-infiltrated B-cell is a prominent feature of the immune response to human cancer, suggesting the importance of strong prediction and prognosis for cancer therapeutics [ 32 ]. CAFs are activated fibroblasts with marked heterogeneity and plasticity in the TME and involved in tumor development, metastasis, and resistance to cancer immunotherapy. In addition, CAFs affect NK cell inactivation by inhibiting the cytolytic granule production signaling pathway, which causes cytotoxicity [ 33 , 34 ]. Our study closely associated SYT11 expression with immune components in various cancers and revealed that SYT11 was weakly correlated with B-cell and NK cell immunity, but highly correlated with CAFs. These findings suggest that abnormally expressed SYT11 plays a role in the relationship between CAFs and antitumor immunity. In our analysis of SYT11-interacted genes, we determined the potential roles of clathrin-mediated endocytosis, Rho GTPase signaling, cell motility, and phosphatidylinositol metabolism. In the PPI network analysis, SYT-related proteins were highly enriched in the regulation of neurotransmitter secretion and transport in biological processes and the binding of syntaxin and clathrin in molecular function. Since Syt11 is an essential component of neuronal vesicle trafficking and synaptic plasticity [ 6 ] and a reliable EMT regulator in lung cancer invasion and metastasis [ 13 ], our study proves the above experimental results bioinformatically. Conclusion This study is the first comprehensive pan-cancer analysis of SYT11 expression, including clinical prognosis, genetic alterations, epigenetic regulation, immune cell infiltration, gene enrichment analysis, and PPI network analysis, contributing to the clarification of the role of SYT11 from various perspectives in cancer. These results were mainly based on bioinformatic analyses, and further studies should be conducted to validate these results. Declarations Funding This work was supported by grants from the KRIBB Research Initiative Program (KGM5192423) in the Republic of Korea. Author’s Contributions KN carried out the data acquisition, preparation, analysis, and drafting the manuscript. WY and KCP conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for critical intellectual content. All the authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Conflict of Interest Statement The authors have no conflicts of interest to declare. Ethical Approval This manuscript is a not involving human or animal subjects were performed. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. doi:10.3322/caac.21763. Mohi-Ud-Din R, Chawla A, Sharma P, Mir PA, Potoo FH, Reiner Z et al. 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Eur J Med Res. 2022;27(1):259. doi:10.1186/s40001-022-00899-2. Hao Y, Yang Q, He Q, Hu H, Weng Z, Su Z et al. Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction. Eur J Med Res. 2022;27(1):276. doi:10.1186/s40001-022-00910-w. Rajbhandary S, Dhakal H, Shrestha S. Tumor immune microenvironment (TIME) to enhance antitumor immunity. Eur J Med Res. 2023;28(1):169. doi:10.1186/s40001-023-01125-3. Liu YG, Jiang ST, Zhang L, Zheng H, Zhang T, Zhang JW et al. Worldwide productivity and research trend of publications concerning tumor immune microenvironment (TIME): a bibliometric study. Eur J Med Res. 2023;28(1):229. doi:10.1186/s40001-023-01195-3. Laumont CM, Banville AC, Gilardi M, Hollern DP, Nelson BH. Tumour-infiltrating B cells: immunological mechanisms, clinical impact and therapeutic opportunities. Nat Rev Cancer. 2022;22(7):414-30. doi:10.1038/s41568-022-00466-1. Liu T, Han C, Wang S, Fang P, Ma Z, Xu L, Yin R. Cancer-associated fibroblasts: an emerging target of anti-cancer immunotherapy. J Hematol Oncol. 2019;12(1):86. doi:10.1186/s13045-019-0770-1. Ping Q, Yan R, Cheng X, Wang W, Zhong Y, Hou Z et al. Cancer-associated fibroblasts: overview, progress, challenges, and directions. Cancer Gene Ther. 2021;28(9):984-99. doi:10.1038/s41417-021-00318-4. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx SupplementaryTable1.miRNAInteractomes.xlsx SupplementaryTable2.Pathwayenrichmentanalysis.xlsx SupplementaryTable3.CancerTypeAbbreviationmiRNAs.xlsx Cite Share Download PDF Status: Published Journal Publication published 18 Jun, 2024 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 13 Apr, 2024 Reviews received at journal 10 Apr, 2024 Reviews received at journal 05 Apr, 2024 Reviews received at journal 25 Mar, 2024 Reviewers agreed at journal 22 Mar, 2024 Reviewers agreed at journal 21 Mar, 2024 Reviewers agreed at journal 21 Mar, 2024 Reviewers agreed at journal 21 Mar, 2024 Reviewers invited by journal 19 Feb, 2024 Editor assigned by journal 01 Feb, 2024 Submission checks completed at journal 31 Jan, 2024 First submitted to journal 29 Jan, 2024 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. 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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-3909545","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270448195,"identity":"9027bdd4-13f4-47f4-a376-ce443292aad8","order_by":0,"name":"Kyunghee Noh","email":"","orcid":"","institution":"Korea Research Institute of Bioscience and Biotechnology (KRIBB)","correspondingAuthor":false,"prefix":"","firstName":"Kyunghee","middleName":"","lastName":"Noh","suffix":""},{"id":270448196,"identity":"4f0b985c-c4e6-4713-bb83-01c00c87dbad","order_by":1,"name":"Wonbeak Yoo","email":"","orcid":"","institution":"Korea Research Institute of Bioscience and Biotechnology (KRIBB)","correspondingAuthor":false,"prefix":"","firstName":"Wonbeak","middleName":"","lastName":"Yoo","suffix":""},{"id":270448197,"identity":"4a7c3ab8-20a3-4ea7-8db9-709e9bed825c","order_by":2,"name":"Kyung Chan Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACCSBmbKhIYGCT4AELGBCp5QzJWhrbEoAsYrVIzm5+9nHmvLR8PuneA8wVFQzG5g0EtEjLHDOeuXFbjmWbzLkExjNnGMxkDhDQIieRYMz4cFuFAZtEjgHQhQw2EoQcJieR/pnx4RyYln9EaJGWyDFm3NiQA9XSwGBGUIvknDPFjDOOpRmwAf1ysOGYhDFBLRK32zcz9tQkG8jP7j34sKHGxnAGIS0MyIYeQOUSo2UUjIJRMApGAVYAABeIN+e3O4B9AAAAAElFTkSuQmCC","orcid":"","institution":"Korea Research Institute of Bioscience and Biotechnology (KRIBB)","correspondingAuthor":true,"prefix":"","firstName":"Kyung","middleName":"Chan","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2024-01-30 02:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3909545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3909545/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-024-01931-3","type":"published","date":"2024-06-18T15:34:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50568377,"identity":"f62afaa8-8e9d-420e-9bc9-7c2186b07e3b","added_by":"auto","created_at":"2024-02-02 15:25:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2085614,"visible":true,"origin":"","legend":"\u003cp\u003eSYT11 expression level in various tumor tissues and stages.\u0026nbsp;(A) The differences of SYT11 expression in various tumors or specific tumor subtype tissues and adjacent normal tissues analyzed by TIMER2 database from TCGA. *p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001. (B) Box plot representation of SYT11 expression level comparison in ACC, BLCA, CESC, CHOL, COAD, DLBC, KICH, LAML, LGG, LUSC, PAAD, PCPG, READ, SKCM, TGCT, and UCEC tumors relative to the corresponding GTEx database. *p\u0026lt;0.05. (C) Pathological stage-dependent (stage I, II, III, IV and V) SYT11 expression level. Expression in BLCA, PAAD, and STAD tumors were assessed and compared using TCGA data. Expression levels are shown as Log\u003csub\u003e2\u003c/sub\u003e (TPM+1).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/9d53d8a4253b7be4e3d7c626.png"},{"id":50569593,"identity":"cf3a3d74-fe0f-4c13-bb9d-b03742aa0bca","added_by":"auto","created_at":"2024-02-02 15:33:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1583842,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between SYT11 and prognosis in TCGA. (A) Overall survival analysis and (B) disease-free survival analysis in various cancer types from TCGA database. The survival map and graphs with positive results are displayed. (C) The forest diagrams of Kaplan–Meier plot analysis of overall survival and relapse-free survival according to SYT11 gene expression in TCGA data.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/62683568fc64211f5ba7c31e.png"},{"id":50566851,"identity":"99d67462-dbb0-4617-b006-8cbcff90e5c7","added_by":"auto","created_at":"2024-02-02 15:17:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1915968,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic alterations of the SYT11 in pan-cancer. (A) Alteration frequency and (B) mutation and phosphorylation sites in SYT11. Analysis of clinical attributes in (C) putative copy number alterations, (D) molecular profiles on SYT11 genomic alteration in UCS. All data are from cBioportal database.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/2747c65c68f19fbddc6dd5ac.png"},{"id":50568379,"identity":"849472be-0c3f-4bd0-b9e6-f05204f14fb0","added_by":"auto","created_at":"2024-02-02 15:25:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1165138,"visible":true,"origin":"","legend":"\u003cp\u003eDNA methylation characteristics of SYT11. (A) SYT11 methylation levels in tumor and normal samples in patients with different cancer types. (B) The difference of promoter methylation between cancer and normal tissues. p\u0026gt;0.05; *p\u0026lt;=0.05; **p\u0026lt;=0.01; ***p\u0026lt;=0.001; ****p\u0026lt;=0.0001. Ns, no significance.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/59839fc88c64a097b301287a.png"},{"id":50566848,"identity":"90412244-ae1a-4d12-9baa-99e44cbb0eb8","added_by":"auto","created_at":"2024-02-02 15:17:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1264287,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of candidate miRNAs. (A) Venn diagram of miRDB, TargetScan, and miRWalk databases predicting miRNAs corresponding to SYT11 targets. (B) The differential expression meta-profiling heatmap of the 13 candidates, in cancer versus normal comparison, across various cancer types. (C) Predicted consequential pairing of miRNA target region. (D) The top five pathway of KEGG enrichment analysis in candidate miRNAs.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/ad66bd3768af40a0c9d9305b.png"},{"id":50566854,"identity":"654ee615-c371-4492-857b-fea39d613de8","added_by":"auto","created_at":"2024-02-02 15:17:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":915080,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between SYT11 expression and candidate miRNAs. (A) SYT11 expression correlated with corresponding four target miRNAs (hsa-let-7g-5p, hsa-miR-19a-3p, hsa-miR-98-5p, and hsa-let-7i-5p) in various tumors. (B) Correlation between candidate miRNAs and prognosis in various cancers.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/70aad9bc1b8e99937dd80107.png"},{"id":50566849,"identity":"0707e69c-5426-4bd4-a778-55e91183fae1","added_by":"auto","created_at":"2024-02-02 15:17:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":648745,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of SYT11 expression with immunological infiltration in various tumors.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/de1eccc3a65391b8f96720fd.png"},{"id":50568382,"identity":"3a278633-a6a5-4027-8026-0220f0d1c0a1","added_by":"auto","created_at":"2024-02-02 15:25:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1769536,"visible":true,"origin":"","legend":"\u003cp\u003eSYT11-related gene and protein enrichment analysis. (A) SYT11-interacting genes. (B) Expression analysis between SYT11-interacting gene in pan-cancers. (C) Pathway enrichment and (D) ontological analyses between SYT11-correlated gene in various tumors. (E) The protein–protein interaction (PPI) diagram to demonstrate the common differential expressed genes (DEGs). The top five lists contain identified biological and molecular process with a false discovery rate (FDR).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/ce7ea95644359daeb8d654bb.png"},{"id":58823326,"identity":"dc6ce24e-ad0b-41d3-8b54-c93f474daa90","added_by":"auto","created_at":"2024-06-21 16:58:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12375881,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/54cc23a7-4e76-4e0e-8e77-0c9aba1441dd.pdf"},{"id":50569594,"identity":"1c35ba81-2bd7-4e63-b2bf-52e781826e29","added_by":"auto","created_at":"2024-02-02 15:33:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2072977,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/98a88da1d386d04155e581a4.docx"},{"id":50566844,"identity":"3e3db59d-e0b8-48f0-b019-7b14556023a3","added_by":"auto","created_at":"2024-02-02 15:17:09","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19540,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.miRNAInteractomes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/71d7859929992b371abefb59.xlsx"},{"id":50566855,"identity":"8e235681-cdd7-4d83-b472-d4f3e5ffd356","added_by":"auto","created_at":"2024-02-02 15:17:09","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":80047,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.Pathwayenrichmentanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/bc76472bb8111e9c20695ebb.xlsx"},{"id":50568381,"identity":"4dd97e8b-0163-4ed8-ae41-b9fddfea0b18","added_by":"auto","created_at":"2024-02-02 15:25:09","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10983,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.CancerTypeAbbreviationmiRNAs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3909545/v1/6d8ba4ec6efd80f0446b2a33.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Role of SYT11 in human pan-cancer using comprehensive approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is one of the leading causes of death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and considered an important factor affecting healthy human life and decreasing life expectancy. Therefore, a novel strategy to overcome cancer is one of the most urgent public health challenges. In the past few years, the advent of standard chemotherapy and supportive therapy has provided new ideas for cancer treatment [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; however, the prognosis of patients with advanced cancer remains poor. Therefore, there is an urgent need to identify novel therapeutic targets with enhanced prognostic potential.\u003c/p\u003e \u003cp\u003eSynaptotagmin-11 (SYT11), a member of the synaptotagmin (SYT) family, is almost exclusively expressed in the brain tissue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent studies have reported that SYT11 is a functional protein that binds calcium, phospholipids, or SNARE proteins throughout the neuronal cell body, axons, and dendrites and mediates vesicular trafficking [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Genome-associated studies and experimental models have shown that SYT11 dysfunction is associated with Parkinson\u0026rsquo;s disease (PD) and susceptibility to schizophrenia in [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Interestingly, Bajaj et al. found that SYT11 plays a crucial role in tumorigenic properties such as invasiveness and metastasis in tumor microenvironment (TME) via Golgi-mediated exocytosis in lung cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, it is upregulated with SYT11 expression, associated with the stem-like molecular subtype of gastric cancer, and a prognostic biomarker for histologically classified diffuse-type gastric cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, owing to the importance of SYT11 in several cancers, new role of SYT11 in various cancers should be investigated.\u003c/p\u003e \u003cp\u003eIn this study, we examined the expression profile and prognostic value of SYT11 in various cancer types. To further explore the aberrant patterns and possible clinical significance of SYT11 expression, correlation analysis was conducted between SYT11 expression and genetic alteration, methylation, miRNA interaction, and immune cell infiltration. Furthermore, interaction analysis of SYT11-correlated genes, protein\u0026ndash;protein interaction (PPI), and functional enrichment analysis were also performed to explore their potential roles in biological and molecular processes. Based on a comprehensive analysis, we aim to provide a new understanding of the clinical value and clarification of SYT11 expression in various tumors.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eData acquisition and processing\u003c/p\u003e \u003cp\u003eGene expression profiling and interactive analyses based on TCGA and The Genotype-Tissue Expression (GTEx) information were obtained from GEPIA2 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To further elucidate the overall survival (OS) and disease-free survival (RFS), we used the GEPIA2 database and further validated the data using the Kaplan\u0026ndash;Meier Plotter (KM-plot) with [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The log-rank p-values and hazard ratios (HR) were calculated. The cBioportal for Cancer Genomics database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to analyze genetic alterations in \u003cem\u003eSYT11\u003c/em\u003e in TCGA PanCancer Atlas Studies.\u003c/p\u003e \u003cp\u003eComprehensive analysis of SYT11 methylation\u003c/p\u003e \u003cp\u003eThe SMART [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was used to analyze the differential expression of methylated SYT11 in various cancers, and the UALCAN database [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] was used to analyze promoter methylation.\u003c/p\u003e \u003cp\u003eBioinformatic analysis of miRNA target prediction\u003c/p\u003e \u003cp\u003eTo identify potential SYT11 targets, candidate miRNAs were predicted using miRWALK [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], TargetScan [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and miRDB [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We screened 13 common miRNAs in miRWALK and miRDB based on the remaining miRNAs, excluding miRNAs without Pct values from TargetScan. The dbDEMC 3.0 database [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was used to identify promising biomarkers for the expression levels of 13 candidate miRNAs in various cancers. Candidate miRNA expression correlation, prognosis, and enrichment analysis with SYT11 were conducted in pan-cancer using the Starbase database [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The criteria for miRNA interactomes were set such that the prediction program with miRanda\u0026ndash;PicTar\u0026ndash;TargetScan and suggested usage were selected.\u003c/p\u003e \u003cp\u003eExploring immune-related analysis in TME\u003c/p\u003e \u003cp\u003eThe Tumor Immune Estimation Resource database (TIMER 2.0) was employed to investigate the correlation between SYT11 and tumor-infiltrating immune cells such as CD8\u0026thinsp;+\u0026thinsp;T cells, macrophages, B cells, NK cells, MDSCs, and CAFs across diverse tumors from TCGA dataset [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The strength of the correlation heatmap with the purity-adjusted Spearman's ρ was statistically significant and automatically calculated online.\u003c/p\u003e \u003cp\u003eFunctional and pathway enrichment analysis of SYT11\u003c/p\u003e \u003cp\u003eThe 24 genes with the strongest correlation with SYT11 were selected using Pathway Commons [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and this result was used to explore mRNA expression in various cancers. To further elucidate the functions and pathways, pathway-enriched analysis (including Reactome_2022 and BioPlanet_2019) and ontological analysis (including GO Biological Processes 2023 and GO Molecular Function 2023) for the 25 selected genes forming a cluster with SYT11 were performed using Enrichr [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and the p-values were calculated online using Fisher\u0026rsquo;s exact test. Subsequently, the PPI network was evaluated using STRING to further understand the functions of interaction network for SYT11 protein. The strength and false discovery rates were automatically calculated online.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDifferential SYT11 expression in various cancers\u003c/p\u003e \u003cp\u003eAccording to the results obtained from the TIMER2 database, SYT11 was weakly expressed in most cancers, such as bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), glioblastoma multiforme (GBM), renal hepatocellular carcinoma (KICH), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), and uterine corpus endometrial carcinoma (UCEC) than in adjacent normal tissues, while being strongly expressed in cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (NHSC), liver hepatocellular carcinoma (LIHC), pheochromocytoma and paraganglioma (PCPG), and thyroid carcinoma (THCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Since TCGA database contains relatively insufficient information for normal tissues, we also included samples from the GTEx database for further analysis. SYT11 expression in the normal tissues of patients with adrenocortical cancer (ACC), BLCA, CESC, COAD, KICH, LUSC, READ, testicular germ cell tumors (TGCT), and UCEC is lower than the corresponding tumor tissues according to the GTEx database, while the pattern was opposite for the patients with CHOL, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), pancreatic ductal adenocarcinoma (PAAD), PCPG, and skin cutaneous melanoma (SKCM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). However, SYT11 expression in the normal and tumor tissues of the patients with BRCA, ESCA (esophageal carcinoma), GBM, NHSC, KIRC, KRIP, LIHC, LUAD, ovarian serous cystadenocarcinoma (OV), PRAD, sarcoma (SARC), stomach adenocarcinoma (STAD), thyroid carcinoma (THYM), and UCS (uterine carcinosarcoma) were not significantly different (Supplementary Fig.\u0026nbsp;1). To investigate the SYT11 expression in detail, we generated a pathological stage plot using the GEPIA2 module. SYT11 RNA expression levels were significantly and positively associated with the late clinical stages of BLCA and STAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrognostic analysis of SYT11 in pan-cancer\u003c/p\u003e \u003cp\u003eTo investigate the influence of SYT11 on the prognosis of various tumors, a heat map of the \u003cem\u003eSYT11\u003c/em\u003e gene with significant prognostic value was generated using the GEPIA2 database, and the samples were divided according to the median SYT11 expression. Aberrant SYT11 expression mainly affected the improved prognosis of overall survival (OS) for patients with KIRC (p\u0026thinsp;=\u0026thinsp;0.00088) and LUAD (p\u0026thinsp;=\u0026thinsp;0.0053), whereas high SYT11 expression was associated with poor prognosis for patients with ACC (p\u0026thinsp;=\u0026thinsp;0.054), BLCA (p\u0026thinsp;=\u0026thinsp;0.05), LAML (p\u0026thinsp;=\u0026thinsp;0.023), MESO (p\u0026thinsp;=\u0026thinsp;0.0036), and UVM (p\u0026thinsp;=\u0026thinsp;0.023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Moreover, high SYT11 expression displayed unfavorable disease-free survival (RFS) for patients with ACC (p\u0026thinsp;=\u0026thinsp;0.0083), BLCA (p\u0026thinsp;=\u0026thinsp;0.02), and COAD (p\u0026thinsp;=\u0026thinsp;0.031) tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), but not others (Supplementary Figs.\u0026nbsp;3 and 4). Subsequently, we assessed the K\u0026ndash;M plot to evaluate the relationship between SYT11 expression and prognosis of patients with different tumors. High SYT11 expression was associated with poor OS in patients with UCEC (p\u0026thinsp;=\u0026thinsp;0.043), whereas patients with KIRC (p\u0026thinsp;=\u0026thinsp;0.0098) and LUAD (p\u0026thinsp;=\u0026thinsp;0.004) showed better OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In addition, high SYT11 expression level was significantly associated with poor RFS in patients with OV (p\u0026thinsp;=\u0026thinsp;0.033).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGenetic alteration of SYT11 in various cancers\u003c/p\u003e \u003cp\u003eSince genetic alterations are closely associated with tumorigenesis, the genetic variation of SYT11 in various cancers were determined using the cBioPortal TCGA cohort. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, alteration frequencies were high in the patients with UCS, CHOL, and LIHC, and the highest alteration frequency (\u0026gt;\u0026thinsp;10%) with \u0026lsquo;amplification\u0026rsquo; in the patients with UCS. Accordingly, the most common alterations in SYT11 genes were missense (n\u0026thinsp;=\u0026thinsp;71), truncating (n\u0026thinsp;=\u0026thinsp;6), fusion (n\u0026thinsp;=\u0026thinsp;4), and splice (n\u0026thinsp;=\u0026thinsp;3) mutations, and the T68N mutation (Thr to Asn) was observed in the phosphorylation site. In addition, R342C was the main genetic alteration (one case in PRAD, two cases in UCEC, and one case in COAD) among the missense mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Based on the UCS showing the highest genetic alteration frequencies, we further analyzed the association between SYT11 and clinical attributes in UCS-TCGA. In the analysis of the putative copy number, SYT11 expression was the highest in the amplification group compared to that in the other groups, including shallow deletion, diploidy, and gain. Simultaneously, it was positively associated with copy number (Spearman r\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;=\u0026thinsp;0.0692; Pearson r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.016; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Regarding the association between SYT11 expression and copy number, we identified the molecular profiles of SYT11 genomic alterations. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, SNORA80E and UBQLN4 were significantly associated with SYT11 alteration as shown by volcano plots. Additionally, GON4L, RIT1, SCARNA4, SNORA80E, ARHGEF2, KHDC4, LAMTOR2, RAB25, RXFP4, SSR2, and UBQLN4 were significantly associated with SYT11 alterations.\u003c/p\u003e \u003cp\u003eDifference of SYT11 methylation level in pan-cancer\u003c/p\u003e \u003cp\u003eThe Shiny Methylation Analysis Resource Tool (SMART) database was used to analyze the difference in SYT11 methylation levels between normal and primary tumor tissues. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, CpG-aggregated SYT11 methylation was significantly lower in tumor tissues than that in corresponding normal tissues for patients with BLCA (p\u0026thinsp;\u0026le;\u0026thinsp;0.0001), BRCA (p\u0026thinsp;\u0026le;\u0026thinsp;0.01), COAD (p\u0026thinsp;\u0026le;\u0026thinsp;0.01), HNSC (p\u0026thinsp;\u0026le;\u0026thinsp;0.0001), KIRC (p\u0026thinsp;\u0026le;\u0026thinsp;0.0001), LIHC (p\u0026thinsp;\u0026le;\u0026thinsp;0.001), LUAD (p\u0026thinsp;\u0026le;\u0026thinsp;0.01), LUSC (p\u0026thinsp;\u0026le;\u0026thinsp;0.0001), PCPG (p\u0026thinsp;\u0026le;\u0026thinsp;0.05), PRAD (p\u0026thinsp;\u0026le;\u0026thinsp;0.0001), READ (p\u0026thinsp;\u0026le;\u0026thinsp;0.001), and UCEC (p\u0026thinsp;\u0026le;\u0026thinsp;0.0001), while being the opposite for patients with CHOL (p\u0026thinsp;\u0026le;\u0026thinsp;0.05). Since promoter methylation alters gene expression, we explored the promoter methylation level of SYT11 in tumor and normal tissues using the ULCAN database. The results showed that SYT11 promoter methylation was downregulated in patients with various tumors, including BLCA, BRCA, COAD, CESC, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, TGCT, and UCEC, but was lower in the primary tumor tissue then that in normal tissue only for the patients with CHOL. These results suggest that low SYT11 expression is less strongly associated with promoter methylation in most tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrediction of SYT11 upstream miRNA and differential expression\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) play crucial roles in post-transcriptional gene expression via base pairing within mRNAs. Since SYT11 expression is downregulated in various cancers, regulatory miRNAs are possibly highly expressed in cancer. To identify the target miRNAs of SYT11, we used miRNA prediction tools, including miRDB, TargetScan, and miRWalk, and then intersected 13 miRNAs by the Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These 13 miRNAs were further analyzed to explore their differential expression profiles using a meta-profile heatmap of tissue samples from various cancer patients and healthy participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Based on meta-profile heatmap, hsa-miR-19a-3p, hsa-let-7g-5p, hsa-let-7i-5p, and hsa-miR-98-5p showed significant differential expression in tissue samples of cancer patients and healthy participants, and presented binding sites with SYT11 3\u0026rsquo;-UTR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Simultaneously, biological network analysis showed that miRNA-mediated regulation was mostly enriched in intercellular signaling, environmental information processing, and cytoskeletal interactions, such as the MAPK signaling pathway, ECM receptor interaction, focal adhesion, and adherens junction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and Supplementary Table\u0026nbsp;1). To further assess the relationship between expression and clinical significance, correlation analyses and Kaplan\u0026ndash;Meier estimation were conducted between the four candidate miRNAs and SYT11 expression in pan-cancer samples. Among the miRNA/SYT11 pairs, hsa-let-7g-5p/SYT11, hsa-miR-19a-3p/SYT11, and hsa-miR-98-5p/SYT11 were negatively correlated with 11, 15, and 8 tumors, respectively. Conversely, the hsa-let-7i-5p/SYT11 pair was positively associated with most cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In terms of clinical survival prognosis, highly expressed hsa-let-7g-5p was linked to poor OS in the patients with THCA, COAD, SARC, and KIRC; hsa-miR-19a-3p in the patients with KIRC, THCA, SKCM, SARC, ACC, DLBC, BRCA, and LAML; hsa-miR-98-5p in the patients with TGCT, PRAD, ESCA, LGG, and HNSC; and hsa-let-7i-5p in the patients with KIRC, LGG, KIRP, and TGCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These results indicate that the candidate miRNAs may play an important role in reducing SYT11 expression and prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImmune infiltration analysis of SYT11\u003c/p\u003e \u003cp\u003eSince immune cell infiltration plays a crucial role in tumor progression, we investigated the relationship between SYT11 expression and immune cell infiltration in various tumors. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, SYT11 expression was significantly positively associated with CD8\u0026thinsp;+\u0026thinsp;T cell (in 14 types of cancer) and macrophage (in 13 types of cancer) infiltration. HNSC, LUSC, STAD, and THCA showed a positive tendency in B cells, but there was no clear trend in natural killer (NK) cells. Interestingly, SYT11 expression in myeloid-derived suppressor cells (MDSCs) showed a significant negative association with almost all cancer types, excluding ACC, MESO, OV, SKCM, and UCEC, while these negative correlations were associated with few CD8\u0026thinsp;+\u0026thinsp;T cells and macrophages. In addition, SYT11 expression positively correlated with cancer-associated fibroblasts (CAFs) in most cancer types, except for DLBC, GBM, SARC, and UCS. These results suggest that SYT11 plays an important role in immune cell infiltration and may serve as a novel biomarker of various tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSYT11-related gene enrichment analysis data\u003c/p\u003e \u003cp\u003eTo further explore the potential mechanism of SYT11 in various tumors and clinical outcomes, we attempted to obtain a SYT11-interacted gene network (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Twenty-four interacting genes and their expression profiles in various tumor and normal tissues are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB. Our results indicated that PDLIM7, SGIP1, DAB2, INPP5K, and PIP5K1B expression was higher in tumor tissues than that in the corresponding normal tissues, whereas the remaining interacting genes showed opposite tendencies. To assess the relationship between SYT11 and these genes, enriched pathway and ontological analyses were performed simultaneously. Pathway enrichment analysis revealed that SYT11 was significantly associated with clathrin-mediated endocytosis, phosphoinositide metabolism, and Rho GTPase activation in Reactom_2022 and phosphatidylinositol metabolism and cell motility signaling pathway in BioPlanet_2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC and Supplementary Table\u0026nbsp;2). In the ontological analysis, SYT11 was significantly linked with the cellular response to actin nucleation, phosphatidylinositol metabolism, and membrane ruffle formation in GO Biological Process 2023 and diverse phosphatidylinositol-based activities in GO Molecular Function 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD and Supplementary Table\u0026nbsp;2). We also assessed the STRING database to obtain the SYT11-interacting proteins to support gene set enrichment analysis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE, SYT11 interact with 10 proteins, and these PPIs were further analyzed to explore their biological and molecular processes. The biological process results showed that SYT11-correlated proteins were involved in neurotransmitter secretion, synaptic vesicle transport regulation, and SNARE complex assembly. The molecular process results suggest that SYT11-correlated proteins are linked to syntaxin-1 binding, SNAP receptor activity, SNARE binding, and clathrin binding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecently, Bajaj et al. discovered a novel role for SYT11 in epithelial\u0026ndash;mesenchymal transition (EMT)-mediated vesicular trafficking in the development of lung cancer invasion and metastasis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, SYT11 promoted the stem-like molecular subtype of diffuse gastric cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, whether SYT11 significantly impacts the pathogenesis of different tumors through common molecular mechanisms is not yet known. This study comprehensively explored the underlying molecular role of SYT11 in different tumor types and clinical prognoses using bioinformatics.\u003c/p\u003e \u003cp\u003eSYT11 mRNA expression analysis showed the possibility of predicting the diagnosis of certain tumors, such as COL, DLBC, LAML, LGG, PAAD, PCPG, and SKCM, in TCGA, which showed high SYT11 mRNA expression and decreased expression in other tumors. Meanwhile, SYT11 expression was linked to diverse OS and DFS outcomes and poor prognosis in most highly expressed cancers. Although SYT11 expression does not perfectly align with survival prognosis, no reported studies have focused on tumors other than some lung and gastric cancers; therefore, the differential SYT11 expression is considered to be closely related to the survival prognosis of most tumors in this study.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that multiple genetic and epigenetic events are highly involved in tumor initiation and progression [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Genetic alteration profiling of SYT11 revealed that amplification is the most common type of tumor, including UCS and missense mutations. In particular, GON4L, RIT1, SCARNA4, SNORA80E, ARHGEF2, KHDC4, LAMTOR2, RAB25, RXFP4, SSR2, and UBQLN4 were more frequently in the SYT11 altered group. Interestingly, these SYT11 co-occurring genes were enriched in pathways fundamental to cell function and metabolism, such as transcriptional regulation, cell survival, G protein-coupled signaling pathway, and ER function, which play an important role in tumor progression. In epigenetic analyses, such as promoter methylation profiling, SYT11 was hypomethylated in most tumor types than that in normal tissues, while the SYT11 expression was not consistent. Based on the inconsistencies between promoter methylation and mRNA expression, the association between miRNAs and gene expression was further investigated. Thus, hsa-miR-19a-3p, hsa-let-7g-5p, hsa-let-7i-5p, and hsa-miR-98-5p negatively regulates SYT11 and can interact with the SYT11 3\u0026rsquo;-UTR. Among the four miRNAs, hsa-let-7g-5p, hsa-miR-19a-3p, and hsa-miR-98-5p were negatively associated with SYT11 expression, whereas hsa-let-7i-5p was positively associated. Importantly, we found that COAD, THCA, SARC, and KIRC were significantly correlated with miRNA\u0026ndash;mRNA expression and OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). It was hypothesized that certain miRNAs other than the four predicted miRNAs, particularly hsa-let-7g-5p, may be related to epigenetic regulation and clinical prognosis in certain cancers. The reasons underlying the difference between epigenetic analysis and clinical outcomes in this study warrant further experimental investigation. Nevertheless, our findings provide useful information for further understanding the role of genetic and epigenetic SYT11 alterations.\u003c/p\u003e \u003cp\u003eNext, we visualized the immune infiltration landscape in various cancers, which are important TME components [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Particularly, tumor-infiltrated B-cell is a prominent feature of the immune response to human cancer, suggesting the importance of strong prediction and prognosis for cancer therapeutics [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. CAFs are activated fibroblasts with marked heterogeneity and plasticity in the TME and involved in tumor development, metastasis, and resistance to cancer immunotherapy. In addition, CAFs affect NK cell inactivation by inhibiting the cytolytic granule production signaling pathway, which causes cytotoxicity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our study closely associated SYT11 expression with immune components in various cancers and revealed that SYT11 was weakly correlated with B-cell and NK cell immunity, but highly correlated with CAFs. These findings suggest that abnormally expressed SYT11 plays a role in the relationship between CAFs and antitumor immunity.\u003c/p\u003e \u003cp\u003eIn our analysis of SYT11-interacted genes, we determined the potential roles of clathrin-mediated endocytosis, Rho GTPase signaling, cell motility, and phosphatidylinositol metabolism. In the PPI network analysis, SYT-related proteins were highly enriched in the regulation of neurotransmitter secretion and transport in biological processes and the binding of syntaxin and clathrin in molecular function. Since Syt11 is an essential component of neuronal vesicle trafficking and synaptic plasticity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and a reliable EMT regulator in lung cancer invasion and metastasis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], our study proves the above experimental results bioinformatically.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study is the first comprehensive pan-cancer analysis of SYT11 expression, including clinical prognosis, genetic alterations, epigenetic regulation, immune cell infiltration, gene enrichment analysis, and PPI network analysis, contributing to the clarification of the role of SYT11 from various perspectives in cancer. These results were mainly based on bioinformatic analyses, and further studies should be conducted to validate these results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;grants from the KRIBB Research Initiative Program (KGM5192423) in the Republic of Korea.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor\u0026rsquo;s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKN carried out the data acquisition, preparation, analysis, and drafting\u0026nbsp;the manuscript.\u0026nbsp;WY and KCP conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for critical intellectual content. All the authors\u0026nbsp;approved the final manuscript as submitted and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript is a not involving human or animal subjects were performed.\u0026nbsp;\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. doi:10.3322/caac.21763.\u003c/li\u003e\n\u003cli\u003eMohi-Ud-Din R, Chawla A, Sharma P, Mir PA, Potoo FH, Reiner Z et al. 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Cancer Gene Ther. 2021;28(9):984-99. doi:10.1038/s41417-021-00318-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SYT11, prognosis, genetic alteration, immune cell infiltration, enrichment analysis","lastPublishedDoi":"10.21203/rs.3.rs-3909545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3909545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSynaptotagmin 11 (SYT11) plays a pivotal role in neuronal vesicular trafficking and exocytosis. However, no independent prognostic studies have focused on various cancers. In this study, we aimed to summarize the clinical significance and molecular landscape of SYT11 in various tumor types.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing several available public databases, we investigated abnormal SYT11 expression in different tumor types and its potential clinical association with prognosis, methylation profiling, immune infiltration, gene enrichment analysis, and protein\u0026ndash;protein interaction analysis, and identified common pathways.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTCGA and Genotype-Tissue Expression (GTEx) showed that SYT11 was widely expressed across tumor and corresponding normal tissues. Survival analysis showed that SYT11 expression correlated with the prognosis of seven cancer types. Additionally, SYT11 mRNA expression was not affected by promoter methylation, but regulated by certain miRNAs and associated with cancer patient prognosis. Moreover, aberrant SYT11 expression was significantly associated with immune infiltration. Pathway enrichment analysis revealed that the biological and molecular processes of SYT11 were related to clathrin-mediated endocytosis, Rho GTPase signaling, and cell motility-related functions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results provide a clear understanding of the role of SYT11 in various cancer types and suggest that SYT11 may be of prognostic and clinical significance.\u003c/p\u003e","manuscriptTitle":"Role of SYT11 in human pan-cancer using comprehensive approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 15:17:04","doi":"10.21203/rs.3.rs-3909545/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-13T13:32:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-10T10:37:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-05T21:45:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-25T13:46:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20bb3ee3-c627-4578-bacf-f597759eaaf6","date":"2024-03-22T06:04:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"01c23f9d-695d-4938-8e88-df6a0095b36a","date":"2024-03-21T17:45:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5e10639f-baf8-4c5a-a4e8-661225963ce7","date":"2024-03-21T12:41:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4aa6d0da-7642-418f-9e91-faf4885e32e3","date":"2024-03-21T12:27:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-19T13:34:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-01T11:50:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-31T13:45:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2024-01-30T02:20:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41947ffe-71c1-4b67-9dd3-723453e322ff","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T15:34:07+00:00","versionOfRecord":{"articleIdentity":"rs-3909545","link":"https://doi.org/10.1186/s40001-024-01931-3","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2024-06-18 15:34:06","publishedOnDateReadable":"June 18th, 2024"},"versionCreatedAt":"2024-02-02 15:17:04","video":"","vorDoi":"10.1186/s40001-024-01931-3","vorDoiUrl":"https://doi.org/10.1186/s40001-024-01931-3","workflowStages":[]},"version":"v1","identity":"rs-3909545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3909545","identity":"rs-3909545","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}