{"paper_id":"3bfb1957-2c64-4b6b-b4e8-0c5271d693ac","body_text":"Integrated analysis unraveling the immunologic and clinical prognostic values of Synaptotagmin Like 4 in pan-cancer | 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 Integrated analysis unraveling the immunologic and clinical prognostic values of Synaptotagmin Like 4 in pan-cancer Yuehan Ren, Xiangbin Wu, Jinlei Li, Zhenhua Zhou, Shichang Ni, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4929307/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 SYTL4 (Synaptotagmin Like 4) encodes a protein of synaptotagmin like protein family, which participates in intracellular membrane trafficking. Currently, its role and mechanisms in cancer remain unveiled, necessitating additional comprehensive analysis across different types of cancer to assess its potential in diagnosis, prognosis, chemotherapy, and immunotherapy in cancer. In our study, the mRNA level, threshold for copy number alterations, segmentation of masked copy number alterations, and methylation of SYTL4 DNA were analyzed based on data from TCGA pan-cancer cohort. miRNA, TCPA, mutation and clinical data were analyzed to evaluate diagnostic and prognostic significances of SYTL4. Then the results were checked using cBioPortal and GEO database. The protein levels were analyzed and evaluated based on HPA database and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Biological roles of SYTL4 in pan-cancer were explored by GSEA. We use multiple immune infiltration algorithms in TIMER2.0 and TISCH database to cross-verify the associations between SYTL4 expression and tumor immune microenvironment. Additionally, we depicted a pan-cancer survival map and explored the differences of gene expressions among cancers with different molecular subtypes. Through chemotherapy data from CellMiner, GDSC, CTRP database, we clarified the relationship between SYTL4 and drug resistance. Finally, we explored the chemical substances that affect SYTL4 expression through CTD database. This study systematically and comprehensively reveals the functions of SYTL4 and potential clinical diagnostic and therapeutic predictive values of SYTL4 in pan-cancer. Synaptotagmin Like 4 Diagnosis Pan-cancer Prognosis TME Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 INTRODUCTION Due to the complexity and heterogeneity of carcinogenic process, analysis for the associations with genetic expression and prognosis of cancer patients from a pan-cancer perspective is of great importance for cancer research. TCGA dataset is a preferred tool for cancer research due to its large sample size, diverse data categories, and uniform data formats 1 , 2 . Here, we investigated the potential function and clinical value of SYTL4 (Synaptotagmin Like 4) based on TCGA and other public data. SYTL4 protein binds to certain small Rab GTPases and plays a role in intracellular membrane trafficking 3 . SYTL4 also functions in various molecular pathways, including synaptic vesicle cycle, insulin secretion and AMPK- signaling 4 , 5 . Thus, SYTL4 is associated with diseases related to defects in synaptic vesicle cycle and insulin secretion 5 – 7 . Overexpression of SYTL4 were observed in different tumorous tissues when compared with corresponding nontumorous tissues including gastric cancer 8 , 9 . Thus, SYTL4 may be crucial to the progression and treatment of cancer. The interaction of SYTL4 with microtubules can result in instability of microtubule and decreased responsiveness to paclitaxel in triple-negative breast cancer (TNBC) 10 . SYTL4-associated extracellular vesicle secretion is involved in proliferation and invasion of nasopharyngeal carcinoma cells 11 . However, the role and mechanism of SYTL4 in other cancers remains largely unknown. Whether SYTL4 have critical function and clinical value in cancer is still uncertain. A systematic study of its cross-cancer dysregulation will be helpful to unveil the role of SYTL4 in cancers. Therefore, a thorough evaluation of gene profiles in cancer to understand the intrinsic role of SYTL4 in immune cells of tumor immunology is necessary. This research involved a comprehensive analysis across various cancer types to explore the underlying mechanisms of genes involved in malignant transformation and clinical prognosis, and to depict the gene profiles, including expression levels, mutation status, relevance to target features, and contributions to survival of patients. Here, all data and analyses are based on public databases. We found that SYTL4 is related to different cancer characteristics, including drug resistance, immune microenvironment, and prognosis of patients. Above results highlight the key role of SYTL4 in cancer and facilitate further study for molecular mechanisms and therapeutic development of SYTL4. 2 MATERIALS AND METHODS 2.1 Data acquisition and softwares Raw data, processed data, clinical data were obtained at GDC ( https://portal.gdc.cancer.gov/ ). Clinical information of the Memorial Sloan Kettering Cancer Center were obtained from cBioPortal ( http://www.cbioportal.org ). RNA, DNA methylation, gene-level copy number (gistic2) are available in the PANCAN database. The transcriptional, TCPA data, ABSOLUTE purity/ploidy information were obtained from TCGA database. The molecular subtypes were defined according to TCGA Subtype.20170308.tsv. These public databases are freely available, and data extraction policies of the databases were strictly complied. Therefore, ethical review and approval from the ethical committee are not required. 2.2 Functional Pathways Analysis Gene effect score is from CRISPR knockout screening released by the Achilles project of Broad and the SCORE project of Sanger. A negative score means cellular growth inhibition and/or death after genetic knockout. The score is normalized, thus median score of non-essential genes is 0, and that of essential genes is -1. We showed the top 200 negative scoring cell lines and focused on colon cancer. It is worth noting that protein-protein interaction data usually contain biologically implausible interactions that cannot occur in living cells. We used the ComPPI database ( https://comppi.linkgroup.hu/ ) to filter out proteins that did not have subcellular colocalization in their interactions. ComPPI introduces two new quantitative metrics, the Localization Score and the Interaction Score, to assess the likelihood of the data accuracy. The final result was the proteins that may biologically interact with SYTL4. We first analyzed the differential expressed genes (DEGs) in pan-cancer groups with high and low levels of SYTL4. If a gene is highly expressed in more than six tumors, it was defined as a SYTL4-related gene and subjected to KEGG enrichment analysis. To determine the pathways related to SYTL4, we categorized tumor samples into two different groups according to expression levels of SYTL4, including the top 30% and the bottom 30%. Gene set enrichment analysis (GSEA) was conducted to assess the activation or suppression of top 50 hallmark gene sets and 85 metabolic gene sets in SYTL4 high-expressed group compared to the SYTL4 low-expressed group in different tumors. The CancerSEA website was used to handle single-cell data from Cyclebase, HCMDB, and StemMapper datasets, which redefined 14 functional states 1 . We implemented the z-score algorithm for 14 functional states using the R package GSVA, and z-scores were enumerated as values of each gene set based on previous alporithm 13 . In addition, we collected 14 classic tumor-related pathways from the KEGG database. Pearson correlation analysis were performed to calculate the statistical correlation between SYTL4 and z-scores of each gene set. 2.3 Identification of Chemical Substances Interacting With SYTL4 DEGs in samples from different cancer types with high and low expression of SYTL4 were identified. 150 genes with the most upregulated or downregulated expression were collected as SYTL4-related signatures. CMAP_gene_signatures. RData file of the database, containing 1288 feature-related compounds, ( https://www.pmgenomics.ca/bhklab/ ) were used to calculate matching scores according to the methods in previous publications 14 , 15 . The TOP5 for 32 types of cancer were summarized and graphically displayed using R language. In the analysis for immunotherapy, receiver operating characteristic curve (ROC) analysis were used to observe the predictive ability of SYTL4 for immunotherapy response. The correlations between SYTL4 expressions and chemotherapy drug sensitivity were analyzed using 4 different databases based on two versions of GDSC (GDSC1 and GDSC2). According to the official statement from database, when the results of the two versions differ, GDSC2 is taken as the standard. In the PRISM and CTRP databases, we calculated the associations between SYTL4 expression and the area under the curve (AUC) of drugs, while in GDSC, associations between SYTL4 expression and IC50 of drug was calculated. However, when both are negatively correlated, it means higher drug sensitivity. The cellminer database is just the opposite, we calculated the relationship between SYTL4 expression and z score of drug activity, so when both are positively correlated, it means higher drug sensitivity. 2.4 Multi-omics Analysis of DEGs in Tumors and Normal Tissues Dysregulated expression of SYTL4 in cancers was evaluated with a three-dimensional differential analysis. First, due to the limited normal tissues included in TCGA dataset, expression data of normal tissue from the GTEx data were integrated to increase the sample size and enhance confidence. Wilcox test were used to test differences. The expression distribution of SYTL4 in various organs were visualized using gganatogram package. Secondly, mRNA expressions of SYTL4 in tumors and adjacent tissues from TCGA were analyzed using Wilcoxon rank-sum test. In addition, we also conducted a Wilcoxon rank-sum test on the paired samples according to TCGA grouped cancer types. ROC was used to evaluate the importance of SYTL4 in pan-cancer diagnosis based on pROC package, with AUC values of 0.5 to 1. Higher the AUC value means better diagnostic performance. AUC values between 0.7 and 0.9 and above 0.9 indicate moderate and high levels of diagnostic accuracy, respectively. We validated the transcription level based on GEO data, and protein levels of SYTL4 based on CPTAC data. In addition, we calculated the proportion of 4 staining in different tumors using HPA pathological data. Moreover, we examined the SYTL4 expression patterns in various molecular subtypes through Wilcoxon rank-sum and Kruskal-Wallis Rank Sum Test. 2.5 Somatic Copy-number Alteration (SCNA), Mutation and DNA methylation Analysis cBioPortal ( http://www.cbioportal.org)offer s various analytical capabilities, including mutation-related analysis and visualization 16 . After logging into cBioPortal, \"TCGA Pan Cancer Atlas Study\" 17 , 18 were selected in \"Quick Selection\" section and inputted \"SYTL4\" to investigate SYTL4-related gene mutations. The changing frequencies, types of mutation, and copy-number alterations (CNA) data for various cancer types were acquired from \"Cancer Type Summary\" on TCGA. A \"mutation\" tool was used to display mutational sites of SYTL4 in a two-dimensional (2D) schematic diagram of a protein structure. SCNA and mutational analyses were performed by heterozygosity and homozygosity of amplifying and deleting, with more than 5% considered as SCNA with high frequency. The association between SCNA and SYTL4 levels were evaluated by analyzing spearman correlation. Bioconductor R package, \"IlluminaHumanMethylation-450kanno.ilmn12.hg19\" was introduced to annotate the methylation probes for the promoter. Methylation variations between tumorous and normal samples for each gene was tested using the Wilcoxon rank test. Spearman correlation between SYTL4 mRNA levels and β values for promoter methylation was computed. Furthermore, we calculated the Spearman correlations between SYTL4 and 10 types of genomic feature scores and examined the splicing patterns and scores of SYTL4 in pan-cancer based on TCGA SpliceSeq. 2.6 Survival and Clinical Outcome Analysis We performed Wilcox analysis to explore associations of SYTL4 expression with clinical outcomes. Survival data was acquired from TCGA, and associations between SYTL4 and prognostic indicators was analyzed with \"survival\" and \"survminer\" R packages. We combined Kaplan-Meier and single-factor Cox analysis methods to comprehensively determine whether SYTL4 is a risk factor or a protective factor, ultimately creating a highly confident survival map of SYTL4. Optimal cutoff value for high and low SYTL4 mRNA in Kaplan-Meier and single-factor Cox analysis were determined with \"survminer\" R package, using the survfit function of log-rank test to evaluate significance of SYTL4 expression. \"Forestplot\" package was used for visualization of Cox analysis outcomes for survival. 2.7 Tumor Microenvironment (TME) and Single-Cell Analysis Validation TIMER2.0 uses 7 advanced algorithms to provide a more confident estimate of immune infiltration in TCGA 19 . We obtained expression landscapes of SYTL4 in multiple single-cell datasets using the TISCH database, thereby verifying results of the TME at individual-cell resolution. Notably, we analyzed seven steps of cancer immune cycle in anti-tumor immune status: release and presentation of cancer cell antigen, initiation and activation of immune response, transport and infiltration of immunocytes to the tumor, recognition of cancer cells by T cells, and elimination of cancer cells, then scored per step and calculated the difference between the SYTL4 high and low expression groups. 2.8 Single-Cell Analysis in CRC We extracted the GSE166555 dataset from the TCGA to perform single-cell analysis. \"Seurat\" R software package was used for analysis and quality control (the minimum gene number is 500, the maximum gene number is 4000, the percentage of mitochondrial gene is below 5%, the percentage of red blood cell gene is below 1%) to avoid low-quality cells, cell fragments, or multi-cell capture. \"Normalize Data\" was used to standardize data and correct for sequencing depth. The “Find Variable Features” identified the top 2000 high-variable genes. \"RunPCA\" was used for principal component analysis (PCA) dimensionality reduction of top 3000 highly variable genes. The best PC was identified based on cumulative contribution of the main components being greater than 90%, the PC itself contributing less than 5% to the variance, and the difference between two consecutive PCs being less than 0.1%. The \"Run Harmony\" was used to integrate single samples with a resolution of 0.5. The \"Find Clusters\" was used to identify clusters, and the \"Run UMAP\" was used for visualization of the clusters. Subsequently, cell-specific lineage genes were manually annotated based on each cell cluster. Based on the \"Find All Markers\" function, highly expressed genes in each cell subpopulation were identified as markers for each cell cluster. 2.9 Microbiome analysis in Pan-cancer. We obtained the intra-tumoral microbiological information of 5 tumors from TCMA database( https://tcma.pratt.duke.edu ) 20 and divided patients into 2 groups by median expression of SYTL4 in each tumor. We used the Wilcoxon analysis to analyze the differences in microbial abundance. In addition, we also employed Spearman correlation analysis and transcriptome information for joint analysis to identify microbes related to SYTL4, filtering conditions were false discovery rate (FDR) < 0.05 and correlation coefficient > 0.15. 2.10 Statistical Analysis All data were analyzed via webtools and R software (V.4.3.0). Pearson correlation analysis was applied in normal distributed data, Spearman correlation analysis was used otherwise. Difference significance among multiple variates and two variates were tested using Signed Rank Test, Kruskal- Wilcoxon and Wallis Rank Sum and. The Cox and Kaplan-Meier survival analyses were performed with \"survival\" package. Kaplan-Meier analysis was visualized with \"survminer\" package, in which significances were analyzed by log-rank test. Relative risk were described with hazard ratio (HR) and 95% confidence interval (CI). \"pROC\" package was used for ROC analysis to evaluate the diagnostic performance of genes. All statistical tests are two-sided. Differences were defined as statistically significant for p < 0.05, and extremely significant for p < 0.0001. 3 Results 3.1 Association between SYTL4 and Pathways in pan-Cancer First, we investigated the essentiality of SYTL4 for growth and survival in cell lines based on the gene effect score from the CRISPR knockout screening. SYTL4 is negatively scored in most cell lines, but the vast majority do not exceed − 1 or even − 0.5, indicating that SYTL4 is not an essential gene for growth (Fig. 1 A), especially in colon cancer (Fig. 1 B). We showed the top 200 negative scoring cell lines and focused on colon cancer. What is biological function of SYTL4 involved in cancer? Then we identified proteins that interact with SYTL4, which may jointly perform biological functions (Fig. 1 C). Consistent with the transcriptome results, TCPA data shows that SYTL4 is statistically correlated with many functional proteins, thus SYTL4 may exert an important function in cancers (Fig. 1 D). High expression of SYTL4 significantly activates cell adhesion-related pathways and is associated with various signaling transduction and signaling molecules and interaction pathways, further confirming the above results (Fig. 1 E). Next, we analyzed the relationships between mRNA levels of SYTL4 and 14 cancer markers and 14 tumor-related pathways scores, of which the majority were positive correlations (Fig. 1 F). Based on the transcriptome data of SYTL4 in the two tumor subgroups, cancer-related cell signaling per cancer type was explored by GSEA analysis. It is observed that immunology and epithelial mesenchymal transition-related pathways often upregulated in tumors with high levels of SYTL4 (Fig. 1 G). SYTL4 may also engaged in the disorder of metabolism in tumors (Fig. 1 G). Thus, we systematically analyzed metabolism-related pathways and found a good consistency across cancer types, suggesting the functional conservation of SYTL4. Above all, SYTL4 might have an important role in promoting cancer development through immunology, epithelial mesenchymal transition (EMT), and metabolism disorder in pan-cancer. 3.2 Identification of Chemical Substances Interacting with SYTL4 We conducted CMap analysis to find out potential treatment regimens that could offset the tumor-enhancing effects facilitated by SYTL4. Firstly, we constructed a SYTL4-related gene signature, including top 150 upregulated and top 150 downregulated genes, which were determined by comparison between patients with SYTL4 high-expression and low-expression in each cancer type. The optimal feature matching method X Sum was used to compare SYTL4-related features with CMap gene features to obtain similarity scores for 1288 compounds. Scores of arachidonyltrifluoromethane, STOCK1N.35874 and X4.5.dianilinophthalimide are significantly lower in most cancer types, suggesting that they may have the potential to inhibit SYTL4-mediated oncogenic effects (Fig. 2 A). To evaluate the value of SYTL4 in cancer treatments, we examined how expressions of SYTL4 correlates with responses to systematic treatment in patients with different cancer types. In the analysis of SYTL4 expression in most immunotherapies, the AUC value is not generally ideal (Fig. 2 B). However, in a cutaneous malignant melanoma cohort, we observed higher expression of SYTL4 in group responding to treatment, and higher proportion of patients with high SYTL4 expression in response group. ROC curve analysis also shows that SYTL4 expression achieved good sensitivity and specificity in response to treatments (Fig. 2 C-E). As for chemotherapy, SYTL4 expression was correlated with drug sensitivity based on analyses from 3 different databases (CTRP, GDSC and PRISM). Obviously, SYTL4 is a potential drug-resistant gene (Fig. 2 F-J). Taken together, expressions of SYTL4 might be a predictive biomarker of response to cancer therapy. 3.3 Aberrant Expression of SYTL4 among Cancers We performed both solely differential analysis (Fig. 3 A) and paired difference analysis to determine the dysregulated patterns of SYTL4 in cancers (Fig. 3 B) based on TCGA cohorts. Subsequently, through combinationally mining the resources of TCGA and GTEx database, we revealed expression profiles of SYTL4 from a pan-cancer perspective (Fig. 3 C). The organ diagrams visualized expression distribution pattern of SYTL4 (Fig. 3 D). Protein level of SYTL4 was validated with CPTAC database (Fig. 3 E). We found that SYTL4 was dysregulated in the majority of cancer types and exhibited consistent significantly downregulated expression patterns across cancer types. The HPA results also supported that the staining level of SYTL4 in most tumors was extremely low (Fig. 3 F). The external validation at the mRNA level was performed in the GEO database (Figure S1 ). Based on TCGA, TCGA-GTEx, GEO and CPTAC database, the above results were fully validated by logistics regression analysis (Fig. 3 G). We observed a good consistency in the expression trends across different omics, databases, and multiple tumors. In fact, ROC curve analysis estimated that SYTL4 mRNA levels in various tumors were of adequate sensitivity and specificity in diagnosis (AUC > 0.7) (Figure S2). Combined with the expanded sample size of normal group, the results were still robust (Figure S3). This result was reproducible and consistent in multiple databases, multiple tumors, and multiple method, indicating that the dysregulated expression of SYTL4 may be functional in various cancers and is improbable to be a false discovery resulting from technical artifacts, opportunities, or sample qualification criteria biases. Interestingly, SYTL4 was also differentially expressed in many molecular subtypes (Figure S4). 3.4 Genetic Alterations of SYTL4 in Cancers To investigate why SYTL4 was dysregulated across cancers, we analyzed genomic information from the TCGA pan-cancer cohort. We investigated 2D structure of SYTL4 mutated sites, demonstrating the post-translational modification sites that may be affected (Fig. 4 A). The cBioPortal database indicates that SYTL4 presents a certain frequency of genetic alterations in most cancers, mutation and amplification are the most common types of genetic alterations of SYTL4 (Fig. 4 B). Further analysis showed missense mutations is the major type of mutations (Fig. 4 C). Then we also analyzed the spearman correlations between SYTL4 and 10 types of genomic signatures (Fig. 4 D), which showed significant associations with different preference in certain cancers such as BRCA, CESC, and COAD. To investigate genetic aberrations of SYTL4 in cancer, we examined SCNA on SYTL4. In general, high frequency of SCNA on SYTL4 was observed in most cancer types (more than 5% of all samples), but low in only few cancers (Fig. 4 E). Clearly, SCNA is key in gene expression regulation of SYTL4 in tumors (Fig. 4 F). Next we assessed how SCNA affects SYTL4 mRNA levels by computing spearman correlation between gene expression and masked copy-number segment in TCGA. It showed that mRNA level of SYTL4 negatively correlated with the SCNA in majority of tumors (Fig. 4 G). It suggests that copy-number aberrations of SYTL4 are frequent in cancers and may regulate gene expression. Besides SCNA, aberrant DNA methylations on promoter frequently occurred during tumorigenesis. In addition, SYTL4 displayed a relatively consistent methylation pattern across the pan-cancer cohort, and most tumorous tissues showed hypomethylation than normal tissues (Fig. 4 H). SYTL4 mRNA levels generally positively correlated with DNA methylation (Fig. 4 I). Alternative splicing is an important form of post-transcriptional regulation, which may regulate the expression of SYTL4. Our analysis showed SYTL4 is mainly spliced in three ways, AP, AT, and ES (Fig. 4 J). 3.5 Clinical Relevance of SYTL4 To elucidate the clinical significances of SYTL4 in cancer, association of SYTL4 expression with clinical stage and survival in cancer patients were examined. mRNA level of SYTL4 is associated with clinical staging (Fig. 5 A), which is important to select treatment strategies. The atlas of survival in pan-cancer shows that SYTL4 is associated with various survival types for multiple cancers (Fig. 5 B), and relatively homogeneous correlation was observed, as SYTL4 can often act as a protective factor in various types of cancer, also as risk factor in a few tumors, indicating that SYTL4 may play different roles in various cancers. Its functional roles in cancer survival need more exploration. To further supplement pan-cancer atlas, we used a forest plot to display the cox survival analysis results of 4 survival types (Fig. 5 C-F) and Kaplan-Meier analysis to exhibit results of KIRP, PAAD through log-rank test (Figure S5). Results suggest that SYTL4 expression associates with survival of cancer patients. 3.6 High SYTL4 Expression Correlates with Immune Infiltration in Cancer The ongoing interactions among tumor cells and immune cells in TME are determinate during development, advancement, metastasis, and reaction to therapies of tumors 21 – 23 . We investigated the involvement of SYTL4 in immune infiltration across cancers by examining correlations between SYTL4 expression and genes of immune activation/inhibition, chemokine, chemokine receptor, and major histocompatibility complex (MHC). We found a consistent positively corelated trend (Fig. 6 A). To clarify the particular types of cells affected by SYTL4 in TME, we investigated correlations of SYTL4 mRNA levels with immune infiltrations and stromal cells abundance using TIMER2.0 database. In most cancer types, Cancer associated fibroblast, Endothelial cell, macrophage neutrophil are positively related to SYTL4 expression in most cancer types, while CD4 + Th1 cell, CD8 + EPIC cell and activated NK cell are negatively related to SYTL4 expressions (Fig. 6 B). The results suggest that SYTL4 is involved to some extent in immune exclusion or immune cell infiltration and may function especially in immune evasion, interactions between tumors and immune system pathway. Notably, because of different proportions of infiltrated immunocytes and unique tumor microenvironments in different cancers, the trends of these correlation varies slightly in different tumors. However, the results of the 7 evaluation methods based on different software mutually corroborate, confirming the accuracy of our analysis. In addition, TISCH database describes expression landscape of SYTL4 in multiple datasets of single cell, showing that although SYTL4 is not highly expressed in most tumors, it mainly originates from malignant cells (Fig. 6 C), verifying the above immune infiltration results. In summary, we provide a thorough examination and depiction of SYTL4 in immune infiltration and the TME across various types of cancer. 3.7 Single-Cell Analysis in CRC To improve the resolution of the data, we analyzed the potential functions of SYTL4 involved in malignant cells at single-cell level. We used strict criteria for quality control, considering the potential influence of genes related to cell cycle on reduced dimensionality. Subsequently, cell cycle score was computed for each cell and regression correction was carried out during PCA (Figure S6). After the integration by Run Harmony, the cell distributed evenly across samples, indicating good integration effect (Figure S7). Then we identified 18 clusters and manually annotated them, ultimately annotating as 11 cell types (Fig. 7 A-B). As shown in Fig. 7 C, the manual annotation strictly adopted classic or well-established markers. Interestingly, we found that SYTL4 is mainly expressed in malignant cells, but there are still a large number of malignant cells that do not express SYTL4 (Fig. 7 D-E). We identified the DEGs between SYTL4-positive malignant cells and SYTL4-negative malignant cells and performed KEGG enrichment analysis to identify their functional differences. KEGG enrichment analysis suggests that SYTL4-positive malignant cells are mainly characterized by metabolic disorders, while SYTL4-negative malignant cells are characterized by proliferation (Fig. 7 F). 3.8 Association between SYTL4 and Microbiome in Pan-cancer. Microbes play complicated roles in cancer biology and immune response in cancer and be significant for development and therapy in cancers 24 – 26 . Among the 5 types of tumors, we detected correlations between SYTL4 and some microbes. The highest detection was in HNSC, while the lowest was in ESCA, showing mainly low correlations. Notably, in colorectal cancer, we found a moderate correlation of 0.401 between SYTL4 and Bifidobacteriales, which is colonized in the intestine and a key symbiotic bacteria. Bifidobacteriales can strengthen the intestinal barrier and benefit to inhibit tumor and inflammation 27 . In addition, Acidaminococcales, Dorea, Coprococcus, Phascolarctobacterium, Bifidobacterium, Acidaminococcaceae and Eubacteriaceae also significantly negatively correlated with SYTL4. However, Spirochaetales and Spirochaetes in HNSC, Selenomonadales and Selenomonadaceae in ESCA were positively correlated with SYTL4 (Fig. 8 ), suggesting that SYTL4 may modulate microbiota homeostasis in various cancers. 4 DISCUSSION SYTL4 is found to be a novel chemo resistant gene in TNBC and its high expression indicates worse prognosis in TNBC patients receiving taxane treatment 10 . SYTL4 and SDC2 can also be upregulated by Epstein-Barr virus-encoded latent membrane protein 1 to promote secretion of EV through NF-κB signaling, leading to enhanced cancer progression of nasopharyngeal carcinoma 11 . However, a comprehensive understanding to the role and mechanisms of SYTL4 in other cancer types is lacked. Our study firstly analyzed expression signatures and clinical significance of SYTL4 from a pan-cancer perspective and provided comprehensive information about the function and mechanism of SYTL4 in cancer based on multiple databases, which benefit further exploration of SYTL4 in cancer. SYTL4 is highly expressed in TNBC rather than other breast cancer subtypes 10 . In our study, SYTL4 is relatively high-staining in HPA pathology analysis, while mRNA level is slightly downregulated in BRCA, which might be due to the analysis based on a comprehensive BRCA rather than subtypes. It suggests the regulation of SYTL4 is cancer-specific and subtype-specific, and maybe related to regulation network by ER, PR and HER2. But what exactly leads to the dysregulation need further investigation. We found expressed differentially in various tumors, and SCNA and DNA methylation have led to its abnormal mRNA expression across cancers. In addition, alternative splicing of SYTL4 in ways of AP, AT and ES might contribute to post-transcriptional regulation of SYTL4, resulting to the different dysregulation of SYTL4 in pan cancer. Increasingly researches found that microbiome play significant and broad functions in cancers 24 – 26 . In our study, we found SYTL4 was correlated with bifidobacterial in rectum adenocarcinoma. As primary colonizers of the human infant gut, bifidobacterium genus and their by-products generated during carbohydrate metabolism are reported to produce beneficial health effects at both local and systemic levels, like influencing gut homeostasis, restricting pathogen colonization or invasion and regulating immune system via innate or adaptive immune responses 28 . Moreover, probiotic Bifidobacteria cocktail exhibits protective activity against CRC 29 . Thus, the protective role of SYTL4 might related to bifidobacterium in rectum adenocarcinoma. Supplement of bifidobacterium may provide potential benefit to rectum adenocarcinoma patients with low SYTL4 expression. mRNA level of SYTL4 is associated with clinical staging and showed excellent diagnostic and prognostic values in various cancers. For example, SYTL4 is highly expressed in ACC at the later stage, which is consistent with the result that SYTL4 is a risk factors in ACC. However, high SYTL4 expression associated with better prognosis in PRAD tumors. Thus, SYTL4 may serve as a protective factor in different types of cancer, also as risk factors in other tumors. In addition, high SYTL4 Expression correlates with Immune Infiltration in Cancer, thus SYTL4 might be a biomarker predicts survival and response to drugs, including immune therapies, which can help efficient therapy selection of patients and optimal utilization of medical resources. In conclusion, we systematically analyzed the expression, clinical values of SYTL4 and its association in tumor microenvironment in a pan-cancer perspective, providing knowledges for further functional and molecular mechanistic studies. The difference of the role of SYTL4 among various cancers need to be explored further. Abbreviations ACC: Adrenocortical carcinoma; AUC: Area under the curve; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; CCLE: Cancer cell line encyclopedia; COAD: Colon adenocarcinoma; COAD/READ: Colon adenocarcinoma/Rectum adenocarcinoma esophageal carcinoma; DSS: Disease-specific survival; DFI: Disease-free interval; DLBC: Lymphoid neoplasm diffuse large B-cell lymphoma; ESCA: Esophageal carcinoma; FAP: Fibroblast activation protein-α; GTEx: Genotype Tissue-Expression; GSEA: gene set enrichment analysis; GSVA: gene set variation analysis; GBM: Glioblastoma multiforme; GBMLGG: Glioma; HNSC: Head and Neck squamous cell carcinoma; ICIs: Immune checkpoint inhibitors; KICH: Kidney chromophobe; KM: Kaplan-Meier; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute myeloid leukemia; LGG: Brain lower grade glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; NCI: National Cancer Institute; OS: Overall survival; OV: Ovarian serous cystadenocarcinoma; PFI: Progress-free interval; PPI: Protein-protein interaction; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and paraganglioma; PRAD: Prostate adenocarcinoma; ROC: Receiver operating characteristic curve; SARC: Sarcoma; SKCM: Skin cutaneous melanoma; STAD: Stomach adenocarcinoma; STES: Stomach and esophageal carcinoma; TCGA: The Cancer Genome Atlas; TIICs: Tumor-infiltrating immune cells; TME: Tumor microenvironment; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine corpus endometrial carcinoma; UCS: Uterine carcinosarcoma; UVM: Uveal melanoma. Declarations CONFLICT OF INTEREST STATEMENT All authors declare that no conflict of interest exists. Author Contribution YH and XB were responsible for the conceptualization, data acquisition, and analysis. JL and ZH conducted the bioinformatics analyses and interpreted the results. SC assisted in the study's design and provided critical revisions to the manuscript. JH supervised the entire project, guided the research process, and critically revised the manuscript. All authors have read and approved the final manuscript. ACKNOWLEDGMENTS We thank the Colorectal and Anal Surgery department at The First Affiliated Hospital of Wenzhou Medical University for their support. We also acknowledge the data providers and curators of the public databases used in this study. Special thanks to our bioinformatics team for their assistance and to our colleagues for their constructive feedback. Data Availability Raw data, processed data, clinical data were obtained at GDC (https://portal.gdc.cancer.gov/). Clinical information of the Memorial Sloan Kettering Cancer Center were obtained from cBioPortal (http://www.cbioportal.org). RNA, DNA methylation, gene-level copy number (gistic2) are available in the PANCAN database.The transcriptional, TCPA data, ABSOLUTE purity/ploidy information were obtained from TCGA database. The molecular subtypes were defined according to TCGA Subtype.20170308.tsv. These public databases are freely available, and data extraction policies of the databases were strictly complied. Therefore, ethical review and approval from the ethical committee are not required. References Wang Z, Jensen MA, Zenklusen JC. A practical guide to The Cancer Genome Atlas (TCGA). Methods in Molecular Biology. Volume 1418. Humana Press Inc.; 2016. pp. 111–41. 10.1007/978-1-4939-3578-9_6 . Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Wspolczesna Onkologia. 2015;1A:A68–77. 10.5114/wo.2014.47136 . Fukuda M. Slp4-a/granuphilin-a inhibits dense-core vesicle exocytosis through interaction with the GDP-bound form of Rab27A in PC12 cells. J Biol Chem. 2003;278(17):15390–6. 10.1074/jbc.M213090200 . Fukuda M. Rab27 Effectors, Pleiotropic Regulators in Secretory Pathways. Traffic. 2013;14(9):949–63. 10.1111/tra.12083 . Rafi SK, Fernández-Jaén A, Álvarez S, Nadeau OW, Butler MG. High functioning autism with missense mutations in synaptotagmin-like protein 4 (SYTL4) and transmembrane protein 187 (TMEM187) genes: SYTL4- protein modeling, protein-protein interaction, expression profiling and microRNA studies. Int J Mol Sci. 2019;20(13):3358. 10.3390/ijms20133358 . Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(D1):D447–52. 10.1093/nar/gku1003 . Okada M, Itoh MI, Haraguchi M, et al. b-series Ganglioside deficiency exhibits no definite changes in the neurogenesis and the sensitivity to Fas-mediated apoptosis but impairs regeneration of the lesioned hypoglossal nerve. J Biol Chem. 2002;277(3):1633–6. 10.1074/jbc.C100395200 . Miller IV, Grunewald TGP. Tumour-derived exosomes: Tiny envelopes for big stories. Biol Cell. 2015;107(9):287–305. 10.1111/boc.201400095 . Wang Y, Guo Z, Tian Y, et al. MAPK1 promotes the metastasis and invasion of gastric cancer as a bidirectional transcription factor. BMC Cancer. 2023;23(1):959. 10.1186/s12885-023-11480-3 . Liu XY, Jiang W, Ma D, et al. SYTL4 downregulates microtubule stability and confers paclitaxel resistance in triple-negative breast cancer. Theranostics. 2020;10(24):10940–56. 10.7150/thno.45207 . Liao C, Zhou Q, Zhang Z, et al. Epstein-Barr virus-encoded latent membrane protein 1 promotes extracellular vesicle secretion through syndecan-2 and synaptotagmin-like-4 in nasopharyngeal carcinoma cells. Cancer Sci. 2020;111(3):857–68. 10.1111/cas.14305 . Yuan H, Yan M, Zhang G, et al. CancerSEA: A cancer single-cell state atlas. Nucleic Acids Res. 2019;47(D1):D900–8. 10.1093/nar/gky939 . Lee E, Chuang HY, Kim JW, Ideker T, Lee D. Inferring pathway activity toward precise disease classification. PLoS Comput Biol. 2008;4(11). 10.1371/journal.pcbi.1000217 . Malta TM, Sokolov A, Gentles AJ, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell. 2018;173(2):338–e35415. 10.1016/j.cell.2018.03.034 . Yang C, Zhang H, Chen M, et al. A Survey of Optimal Strategy for Signature-Based Drug Repositioning and 1 an Application to Liver Cancer 2. Elife. 2022;11:e71880. Cerami E, Gao J, Dogrusoz U, et al. The cBio Cancer Genomics Portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. 10.1158/2159-8290.CD-12-0095 . Li J, Li Z, Gao Y, et al. Integrating single-cell RNA sequencing and prognostic model revealed the carcinogenicity and clinical significance of FAM83D in ovarian cancer. Front Oncol. 2022;12:1055648. 10.3389/fonc.2022.1055648 . Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. 10.1126/scisignal.2004088 . Li T, Fu J, Zeng Z, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509–14. 10.1093/NAR/GKAA407 . Dohlman AB, Arguijo Mendoza D, Ding S, et al. The cancer microbiome atlas: a pan-cancer comparative analysis to distinguish tissue-resident microbiota from contaminants. Cell Host Microbe. 2021;29(2):281–e2985. 10.1016/j.chom.2020.12.001 . Elhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell. 2023;41(3):404–20. 10.1016/j.ccell.2023.01.010 . Wu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett. 2017;387:61–8. 10.1016/j.canlet.2016.01.043 . Hinshaw DC, Shevde LA. The tumor microenvironment innately modulates cancer progression. Cancer Res. 2019;79(18):4557–67. 10.1158/0008-5472.CAN-18-3962 . Helmink BA, Khan MAW, Hermann A, Gopalakrishnan V, Wargo JA. The microbiome, cancer, and cancer therapy. Nat Med. 2019;25(3):377–88. 10.1038/s41591-019-0377-7 . Sepich-Poore GD, Zitvogel L, Straussman R, Hasty J, Wargo JA, Knight R. The microbiome and human cancer. Science. 2021;371(6536):eabc4552. 10.1126/science.abc4552 . Cullin N, Azevedo Antunes C, Straussman R, Stein-Thoeringer CK, Elinav E. Microbiome and cancer. Cancer Cell. 2021;39(10):1317–41. 10.1016/j.ccell.2021.08.006 . Oloan Pardede S, Ayu Paramastri K, Hegar B, Rafli A. The Proportion of Bifidobacterium and Escherichia Coli in Colon of Children with Recurrent Urinary Tract Infection. Saudi J Kidney Dis Transpl. 2020;31(5):898–904. Alessandri G, Ossiprandi MC, MacSharry J, van Sinderen D, Ventura M. Bifidobacterial Dialogue With Its Human Host and Consequent Modulation of the Immune System. Front Immunol. 2019;10:2348. 10.3389/fimmu.2019.02348 . Parisa A, Roya G, Mahdi R, Shabnam R, Maryam E, Malihe T. Anti-cancer effects of Bifidobacterium species in colon cancer cells and a mouse model of carcinogenesis. PLoS ONE. 15(5), e0232930. 10.1371/journal.pone.0232930 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx 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-4929307\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":359215465,\"identity\":\"b24cb428-4617-4e4f-b069-4912927c7728\",\"order_by\":0,\"name\":\"Yuehan Ren\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuehan\",\"middleName\":\"\",\"lastName\":\"Ren\",\"suffix\":\"\"},{\"id\":359215466,\"identity\":\"48083213-6987-4066-8817-7349646b3093\",\"order_by\":1,\"name\":\"Xiangbin Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiangbin\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":359215467,\"identity\":\"20c4bfbb-1b0f-4536-96cf-c78b2d2f1f95\",\"order_by\":2,\"name\":\"Jinlei Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jinlei\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":359215468,\"identity\":\"f4cc00df-6715-468d-a861-93aea2ba1e0b\",\"order_by\":3,\"name\":\"Zhenhua Zhou\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhenhua\",\"middleName\":\"\",\"lastName\":\"Zhou\",\"suffix\":\"\"},{\"id\":359215469,\"identity\":\"e3915901-b648-4057-88f8-94e3d954aac5\",\"order_by\":4,\"name\":\"Shichang Ni\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shichang\",\"middleName\":\"\",\"lastName\":\"Ni\",\"suffix\":\"\"},{\"id\":359215470,\"identity\":\"14e867a2-3766-4c01-b299-4814f545c633\",\"order_by\":5,\"name\":\"Jianhui Cai\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACxvbGhsN/KmzkGNsbiNTC3HP44AGeM2nGzD0HiNTCPsMt+QBv2+HE9hkJRGrhncFjcECCjTmxd+bjjTcYamyiCWqRnN1jcMCAh8145uy0YguGY2m5DYS0GM45Y3AgQYJHduPsHDMJxobDhLXY38gxOHDAQIJx/80zRGphnJGWcLAhwUCxcQYPsVp6Dh84zHAgwZixB+iXBGL8AozK5s+M//4Do/LwxhsfamwIa0EGBhIJpCiHaCFVxygYBaNgFIwMAABhb0elY+KoVgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jianhui\",\"middleName\":\"\",\"lastName\":\"Cai\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-08-17 10:22:05\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4929307/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4929307/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":65721373,\"identity\":\"cc099ac4-8d45-4d4c-a7e5-85349eebd55e\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:11\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":40050274,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe function mediated by SYTL4 and its regulatory mechanism in Cancers\\u003c/strong\\u003e. (A) Gene Effect scores derived from CRISPR knockout screens. Nonessential genes have a median score of 0 and independently identified common essentials have a median score of -1. (B) The Gene Effect scores in COAD. (C) The interaction information of SYTL4, in which color of line corresponds to data source, the length of the line corresponds to the interaction score. (D) Sankey's picture showing the proteins associated with SYTL4 in the TCPA database. (E) KEGG enrichment analysis for differential-expression genes (UP). (F) The correlation between the mRNA expression of SYTL4 and 28 malignant features of all tumors. (G) Enrichment differences of SYTL4 in 50 HALLMARK and 85 metabolism gene sets. NES indicates normalized enrichment score.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/9a199ef56ddb4f3c065c6541.jpg\"},{\"id\":65721369,\"identity\":\"69c9d8b4-66d6-4929-b3ad-f0e32c4a324e\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:11\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":14345633,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDrug resistance analysis.\\u003c/strong\\u003e Drug sensitivity analysis based on SYTL4 expression using 3 databases Cellminer (A), CTRP (B), GDSC (C). \\u0026nbsp;(D)\\u003cstrong\\u003e \\u003c/strong\\u003ePrediction of potential compounds targeting SYTL4. Visualized the top5 candidate compounds that could potentially target SYTL4 based on connectivity map analysis of 32 cancer types. (E) Survival analysis of SYTL4 in melanoma. (F-J) The correlation analysis between SYTL and drug sensitivity to chemotherapy in cancers with different databases CTRP (F), GDSC1 (G), GDSC1 (H), PRISM(I) and Cellminer (J).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/e1a0157466970aa540e9785e.jpg\"},{\"id\":65721372,\"identity\":\"d7985fde-eaae-433a-9862-0a90a10cdadf\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:11\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":36430761,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe expression landscape of SYTL4 in pan-cancer. \\u003c/strong\\u003e\\u0026nbsp;(A) SYTL4 mRNA expression in TCGA. Boxplots show median, quartiles, min, and max, each point representing one sample. p-values are based on the Wilcox test. (B) SYTL4 mRNA levels in paired samples grouped by cancer from the TCGA. Each point representing one sample. (*P \\u0026lt; 0.05，**P \\u0026lt; 0.01，***P \\u0026lt; 0.001，****P \\u0026lt; 0.0001) (C) The levels of SYTL4 in tumorous tissues and corresponding nontumorous tissues from TCGA and GTEx datasets. (D) Expression and distribution of SYTL4 in various organs. (E) External validation of protein levels in CPTAC database. Here, we adopt the same cancer naming method as TCGA. The Y axis shows the expression amounts of genes. Note that we used a z score for the expression amounts of genes, thus correcting the expression amount to a dimensionless standardized data with an average of 0 and a variance of 1. (F) The proportions of four staining stages in different tumors in HPA pathology. (G) Logistic regression analysis of TCGA and TCGA-GTEx. Red means OR is greater than 1, blue represents an OR value between 0 and 1. A white circle means that there is no significance, and an empty value means that no relevant data set has been collected in the current database.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/d889f66a2485cc400719a675.jpg\"},{\"id\":65721375,\"identity\":\"9afc093b-707a-4254-9dcf-8fd110d273c0\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:11\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":24750418,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGenetic Alterations of SYTL4 in Cancers.\\u003c/strong\\u003e \\u0026nbsp;\\u0026nbsp;(A) Frequency of SYTL4 mutations in different tumor types. (B) The details of the mutation information (C) Sites and case numbers with SYTL4 genetic alterations in pan-cancer from cBioPortal. (D) Radar map visualization of spearman correlation coefficients of SYTL4 and 10 genomic features. *P\\u0026lt;0.05, **P\\u0026lt;0.01, ***P \\u0026lt;0.001, and ****P \\u0026lt; 0.0001. (E) Histogram for frequency of SCNA for SYTL4 in each cancer type. (F) Association between SYTL4 mRNA expression and genetic alterations. (G)) The Spearman’s correlation between SCNAs and SYTL4 expression. (H) Spearman’s correlation of SYTL4 expression and promoter methylation. Red and blue represent positive and negative correlations, respectively. (I) Heatmap for differential methylation of SYTL4 in cancers; hypermethylated and hypomethylated SYTL4 are marked in red and blue, respectively (Wilcoxon rank-sum test).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/404807d9f7da2ce2e2ac4f55.jpg\"},{\"id\":65721376,\"identity\":\"bdd9482d-1e7a-4408-a29f-e6441eb4c90b\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:12\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":27429679,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe survival landscape of pan-cancer.\\u003c/strong\\u003e (A) Wilcoxon rank-sum test examining differences of SYTL4 expression in different stages. (B）Summary of correlation between expression of SYTL4 with overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI) and progression-free interval (PFI) based on the univariate Cox regression and Kaplan-Meier models. Red indicates that SYTL4 is a risk factor for prognosis of cancer patients, and green represents a protective factor. Data with P values \\u0026lt; 0.05 are shown. (C-F) The forest plot for prognostic role of SYTL4 in cancers by univariate Cox regression method.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/3b68623e095b052e3e953f41.jpg\"},{\"id\":65721374,\"identity\":\"68794572-afb1-4a56-9fde-4655c4efdc48\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:11\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":48788147,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAssociation of SYTL4 expression with immune infiltration.\\u003c/strong\\u003e (A) The heatmap of correlations between SYTL4 mRNA expressions and expressions of chemokine, chemokine receptor, immune-inhibitor, immune-stimulatory, and MHC genes. (B) The correlation between SYTL4 expression and cancer immune infiltration with Seven softwares. (C) Cell sources of SYTL4 in pan-cancer at single-cell level.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/82d1408c335ec1bc49d87f21.jpg\"},{\"id\":65721377,\"identity\":\"4f1e0422-6911-4fbb-a5e5-b579d329e2fb\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:12\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":13058432,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSingle cell analysis in CRC.\\u003c/strong\\u003e (A) UMAP of the cluster landscape. (B) Manually annotated and visualized cell clusters. (C) Reliable expression of specific markers in different cell clusters. (D) Expression localization of SYTL4 in colon cancer microenvironment. (E) Kruskal test of SYTL4 expressions in patients with colon cancer (F) KEGG enrichment analysis of SYTL4.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/0f8f45987cf0dfe79df4cfb4.jpg\"},{\"id\":65721371,\"identity\":\"778a5cd0-f5f8-4862-bbf6-3ae683bf7bbd\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:11\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":522785,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe lollipop plot shows the microbes significantly associated with SYTL4 in each tumor, with the length of lines and the size of balls representing the correlation coefficient, and color representing microbes significantly changed in each tumor.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure8.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/dda15be0e14f424a8b220184.jpg\"},{\"id\":66039123,\"identity\":\"b8abf872-ade4-462c-a4e1-f081dbca9474\",\"added_by\":\"auto\",\"created_at\":\"2024-10-07 05:25:25\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":76876256,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/9cb3a82f-052c-4c20-bcdb-3b5d8106c247.pdf\"},{\"id\":65721370,\"identity\":\"c9573323-98df-42ed-a6f3-2cf333a97998\",\"added_by\":\"auto\",\"created_at\":\"2024-10-01 16:54:11\",\"extension\":\"docx\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2439715,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFile.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4929307/v1/22503e23739e944447445663.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Integrated analysis unraveling the immunologic and clinical prognostic values of Synaptotagmin Like 4 in pan-cancer\",\"fulltext\":[{\"header\":\"1 INTRODUCTION\",\"content\":\"\\u003cp\\u003eDue to the complexity and heterogeneity of carcinogenic process, analysis for the associations with genetic expression and prognosis of cancer patients from a pan-cancer perspective is of great importance for cancer research. TCGA dataset is a preferred tool for cancer research due to its large sample size, diverse data categories, and uniform data formats\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. Here, we investigated the potential function and clinical value of SYTL4 (Synaptotagmin Like 4) based on TCGA and other public data.\\u003c/p\\u003e \\u003cp\\u003eSYTL4 protein binds to certain small Rab GTPases and plays a role in intracellular membrane trafficking\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. SYTL4 also functions in various molecular pathways, including synaptic vesicle cycle, insulin secretion and AMPK- signaling\\u003csup\\u003e\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e. Thus, SYTL4 is associated with diseases related to defects in synaptic vesicle cycle and insulin secretion\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e. Overexpression of SYTL4 were observed in different tumorous tissues when compared with corresponding nontumorous tissues including gastric cancer\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e. Thus, SYTL4 may be crucial to the progression and treatment of cancer. The interaction of SYTL4 with microtubules can result in instability of microtubule and decreased responsiveness to paclitaxel in triple-negative breast cancer (TNBC)\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. SYTL4-associated extracellular vesicle secretion is involved in proliferation and invasion of nasopharyngeal carcinoma cells\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. However, the role and mechanism of SYTL4 in other cancers remains largely unknown. Whether SYTL4 have critical function and clinical value in cancer is still uncertain. A systematic study of its cross-cancer dysregulation will be helpful to unveil the role of SYTL4 in cancers. Therefore, a thorough evaluation of gene profiles in cancer to understand the intrinsic role of SYTL4 in immune cells of tumor immunology is necessary.\\u003c/p\\u003e \\u003cp\\u003eThis research involved a comprehensive analysis across various cancer types to explore the underlying mechanisms of genes involved in malignant transformation and clinical prognosis, and to depict the gene profiles, including expression levels, mutation status, relevance to target features, and contributions to survival of patients. Here, all data and analyses are based on public databases. We found that SYTL4 is related to different cancer characteristics, including drug resistance, immune microenvironment, and prognosis of patients. Above results highlight the key role of SYTL4 in cancer and facilitate further study for molecular mechanisms and therapeutic development of SYTL4.\\u003c/p\\u003e\"},{\"header\":\"2 MATERIALS AND METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Data acquisition and softwares\\u003c/h2\\u003e \\u003cp\\u003eRaw data, processed data, clinical data were obtained at GDC (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://portal.gdc.cancer.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://portal.gdc.cancer.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). Clinical information of the Memorial Sloan Kettering Cancer Center were obtained from cBioPortal (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.cbioportal.org\\u003c/span\\u003e\\u003cspan address=\\\"http://www.cbioportal.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). RNA, DNA methylation, gene-level copy number (gistic2) are available in the PANCAN database.\\u003c/p\\u003e \\u003cp\\u003eThe transcriptional, TCPA data, ABSOLUTE purity/ploidy information were obtained from TCGA database. The molecular subtypes were defined according to TCGA Subtype.20170308.tsv. These public databases are freely available, and data extraction policies of the databases were strictly complied. Therefore, ethical review and approval from the ethical committee are not required.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Functional Pathways Analysis\\u003c/h2\\u003e \\u003cp\\u003eGene effect score is from CRISPR knockout screening released by the Achilles project of Broad and the SCORE project of Sanger. A negative score means cellular growth inhibition and/or death after genetic knockout. The score is normalized, thus median score of non-essential genes is 0, and that of essential genes is -1. We showed the top 200 negative scoring cell lines and focused on colon cancer. It is worth noting that protein-protein interaction data usually contain biologically implausible interactions that cannot occur in living cells. We used the ComPPI database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://comppi.linkgroup.hu/\\u003c/span\\u003e\\u003cspan address=\\\"https://comppi.linkgroup.hu/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) to filter out proteins that did not have subcellular colocalization in their interactions. ComPPI introduces two new quantitative metrics, the Localization Score and the Interaction Score, to assess the likelihood of the data accuracy. The final result was the proteins that may biologically interact with SYTL4. We first analyzed the differential expressed genes (DEGs) in pan-cancer groups with high and low levels of SYTL4. If a gene is highly expressed in more than six tumors, it was defined as a SYTL4-related gene and subjected to KEGG enrichment analysis. To determine the pathways related to SYTL4, we categorized tumor samples into two different groups according to expression levels of SYTL4, including the top 30% and the bottom 30%. Gene set enrichment analysis (GSEA) was conducted to assess the activation or suppression of top 50 hallmark gene sets and 85 metabolic gene sets in SYTL4 high-expressed group compared to the SYTL4 low-expressed group in different tumors. The CancerSEA website was used to handle single-cell data from Cyclebase, HCMDB, and StemMapper datasets, which redefined 14 functional states\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. We implemented the z-score algorithm for 14 functional states using the R package GSVA, and z-scores were enumerated as values of each gene set based on previous alporithm\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e. In addition, we collected 14 classic tumor-related pathways from the KEGG database. Pearson correlation analysis were performed to calculate the statistical correlation between SYTL4 and z-scores of each gene set.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e\\u003cb\\u003e2.3 Identification of Chemical Substances Interacting With SYTL4\\u003c/b\\u003e\\u003c/h2\\u003e \\u003cp\\u003eDEGs in samples from different cancer types with high and low expression of SYTL4 were identified. 150 genes with the most upregulated or downregulated expression were collected as SYTL4-related signatures. CMAP_gene_signatures. RData file of the database, containing 1288 feature-related compounds, (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.pmgenomics.ca/bhklab/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.pmgenomics.ca/bhklab/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) were used to calculate matching scores according to the methods in previous publications \\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e. The TOP5 for 32 types of cancer were summarized and graphically displayed using R language. In the analysis for immunotherapy, receiver operating characteristic curve (ROC) analysis were used to observe the predictive ability of SYTL4 for immunotherapy response. The correlations between SYTL4 expressions and chemotherapy drug sensitivity were analyzed using 4 different databases based on two versions of GDSC (GDSC1 and GDSC2). According to the official statement from database, when the results of the two versions differ, GDSC2 is taken as the standard. In the PRISM and CTRP databases, we calculated the associations between SYTL4 expression and the area under the curve (AUC) of drugs, while in GDSC, associations between SYTL4 expression and IC50 of drug was calculated. However, when both are negatively correlated, it means higher drug sensitivity. The cellminer database is just the opposite, we calculated the relationship between SYTL4 expression and z score of drug activity, so when both are positively correlated, it means higher drug sensitivity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Multi-omics Analysis of DEGs in Tumors and Normal Tissues\\u003c/h2\\u003e \\u003cp\\u003eDysregulated expression of SYTL4 in cancers was evaluated with a three-dimensional differential analysis. First, due to the limited normal tissues included in TCGA dataset, expression data of normal tissue from the GTEx data were integrated to increase the sample size and enhance confidence. Wilcox test were used to test differences. The expression distribution of SYTL4 in various organs were visualized using gganatogram package. Secondly, mRNA expressions of SYTL4 in tumors and adjacent tissues from TCGA were analyzed using Wilcoxon rank-sum test. In addition, we also conducted a Wilcoxon rank-sum test on the paired samples according to TCGA grouped cancer types. ROC was used to evaluate the importance of SYTL4 in pan-cancer diagnosis based on pROC package, with AUC values of 0.5 to 1. Higher the AUC value means better diagnostic performance. AUC values between 0.7 and 0.9 and above 0.9 indicate moderate and high levels of diagnostic accuracy, respectively. We validated the transcription level based on GEO data, and protein levels of SYTL4 based on CPTAC data. In addition, we calculated the proportion of 4 staining in different tumors using HPA pathological data. Moreover, we examined the SYTL4 expression patterns in various molecular subtypes through Wilcoxon rank-sum and Kruskal-Wallis Rank Sum Test.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Somatic Copy-number Alteration (SCNA), Mutation and DNA methylation Analysis\\u003c/h2\\u003e \\u003cp\\u003ecBioPortal (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.cbioportal.org)offer\\u003c/span\\u003e\\u003cspan address=\\\"http://www.cbioportal.org)offer\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003es various analytical capabilities, including mutation-related analysis and visualization\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e. After logging into cBioPortal, \\\"TCGA Pan Cancer Atlas Study\\\"\\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u003c/sup\\u003e were selected in \\\"Quick Selection\\\" section and inputted \\\"SYTL4\\\" to investigate SYTL4-related gene mutations. The changing frequencies, types of mutation, and copy-number alterations (CNA) data for various cancer types were acquired from \\\"Cancer Type Summary\\\" on TCGA. A \\\"mutation\\\" tool was used to display mutational sites of SYTL4 in a two-dimensional (2D) schematic diagram of a protein structure. SCNA and mutational analyses were performed by heterozygosity and homozygosity of amplifying and deleting, with more than 5% considered as SCNA with high frequency. The association between SCNA and SYTL4 levels were evaluated by analyzing spearman correlation. Bioconductor R package, \\\"IlluminaHumanMethylation-450kanno.ilmn12.hg19\\\" was introduced to annotate the methylation probes for the promoter. Methylation variations between tumorous and normal samples for each gene was tested using the Wilcoxon rank test. Spearman correlation between SYTL4 mRNA levels and β values for promoter methylation was computed. Furthermore, we calculated the Spearman correlations between SYTL4 and 10 types of genomic feature scores and examined the splicing patterns and scores of SYTL4 in pan-cancer based on TCGA SpliceSeq.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Survival and Clinical Outcome Analysis\\u003c/h2\\u003e \\u003cp\\u003eWe performed Wilcox analysis to explore associations of SYTL4 expression with clinical outcomes. Survival data was acquired from TCGA, and associations between SYTL4 and prognostic indicators was analyzed with \\\"survival\\\" and \\\"survminer\\\" R packages. We combined Kaplan-Meier and single-factor Cox analysis methods to comprehensively determine whether SYTL4 is a risk factor or a protective factor, ultimately creating a highly confident survival map of SYTL4. Optimal cutoff value for high and low SYTL4 mRNA in Kaplan-Meier and single-factor Cox analysis were determined with \\\"survminer\\\" R package, using the survfit function of log-rank test to evaluate significance of SYTL4 expression. \\\"Forestplot\\\" package was used for visualization of Cox analysis outcomes for survival.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7 Tumor Microenvironment (TME) and Single-Cell Analysis Validation\\u003c/h2\\u003e \\u003cp\\u003eTIMER2.0 uses 7 advanced algorithms to provide a more confident estimate of immune infiltration in TCGA \\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e. We obtained expression landscapes of SYTL4 in multiple single-cell datasets using the TISCH database, thereby verifying results of the TME at individual-cell resolution. Notably, we analyzed seven steps of cancer immune cycle in anti-tumor immune status: release and presentation of cancer cell antigen, initiation and activation of immune response, transport and infiltration of immunocytes to the tumor, recognition of cancer cells by T cells, and elimination of cancer cells, then scored per step and calculated the difference between the SYTL4 high and low expression groups.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.8 Single-Cell Analysis in CRC\\u003c/h2\\u003e \\u003cp\\u003eWe extracted the GSE166555 dataset from the TCGA to perform single-cell analysis. \\\"Seurat\\\" R software package was used for analysis and quality control (the minimum gene number is 500, the maximum gene number is 4000, the percentage of mitochondrial gene is below 5%, the percentage of red blood cell gene is below 1%) to avoid low-quality cells, cell fragments, or multi-cell capture. \\\"Normalize Data\\\" was used to standardize data and correct for sequencing depth. The \\u0026ldquo;Find Variable Features\\u0026rdquo; identified the top 2000 high-variable genes. \\\"RunPCA\\\" was used for principal component analysis (PCA) dimensionality reduction of top 3000 highly variable genes. The best PC was identified based on cumulative contribution of the main components being greater than 90%, the PC itself contributing less than 5% to the variance, and the difference between two consecutive PCs being less than 0.1%. The \\\"Run Harmony\\\" was used to integrate single samples with a resolution of 0.5. The \\\"Find Clusters\\\" was used to identify clusters, and the \\\"Run UMAP\\\" was used for visualization of the clusters. Subsequently, cell-specific lineage genes were manually annotated based on each cell cluster. Based on the \\\"Find All Markers\\\" function, highly expressed genes in each cell subpopulation were identified as markers for each cell cluster.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.9 Microbiome analysis in Pan-cancer.\\u003c/h2\\u003e \\u003cp\\u003eWe obtained the intra-tumoral microbiological information of 5 tumors from TCMA database(\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://tcma.pratt.duke.edu\\u003c/span\\u003e\\u003cspan address=\\\"https://tcma.pratt.duke.edu\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e)\\u003csup\\u003e20\\u003c/sup\\u003e and divided patients into 2 groups by median expression of SYTL4 in each tumor. We used the Wilcoxon analysis to analyze the differences in microbial abundance. In addition, we also employed Spearman correlation analysis and transcriptome information for joint analysis to identify microbes related to SYTL4, filtering conditions were false discovery rate (FDR)\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 and correlation coefficient\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.15.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.10 Statistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eAll data were analyzed via webtools and R software (V.4.3.0). Pearson correlation analysis was applied in normal distributed data, Spearman correlation analysis was used otherwise. Difference significance among multiple variates and two variates were tested using Signed Rank Test, Kruskal- Wilcoxon and Wallis Rank Sum and. The Cox and Kaplan-Meier survival analyses were performed with \\\"survival\\\" package. Kaplan-Meier analysis was visualized with \\\"survminer\\\" package, in which significances were analyzed by log-rank test. Relative risk were described with hazard ratio (HR) and 95% confidence interval (CI). \\\"pROC\\\" package was used for ROC analysis to evaluate the diagnostic performance of genes. All statistical tests are two-sided. Differences were defined as statistically significant for p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, and extremely significant for p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3 Results\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Association between SYTL4 and Pathways in pan-Cancer\\u003c/h2\\u003e \\u003cp\\u003eFirst, we investigated the essentiality of SYTL4 for growth and survival in cell lines based on the gene effect score from the CRISPR knockout screening. SYTL4 is negatively scored in most cell lines, but the vast majority do not exceed \\u0026minus;\\u0026thinsp;1 or even \\u0026minus;\\u0026thinsp;0.5, indicating that SYTL4 is not an essential gene for growth (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA), especially in colon cancer (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). We showed the top 200 negative scoring cell lines and focused on colon cancer. What is biological function of SYTL4 involved in cancer? Then we identified proteins that interact with SYTL4, which may jointly perform biological functions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). Consistent with the transcriptome results, TCPA data shows that SYTL4 is statistically correlated with many functional proteins, thus SYTL4 may exert an important function in cancers (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD). High expression of SYTL4 significantly activates cell adhesion-related pathways and is associated with various signaling transduction and signaling molecules and interaction pathways, further confirming the above results (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE). Next, we analyzed the relationships between mRNA levels of SYTL4 and 14 cancer markers and 14 tumor-related pathways scores, of which the majority were positive correlations (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eF). Based on the transcriptome data of SYTL4 in the two tumor subgroups, cancer-related cell signaling per cancer type was explored by GSEA analysis. It is observed that immunology and epithelial mesenchymal transition-related pathways often upregulated in tumors with high levels of SYTL4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eG). SYTL4 may also engaged in the disorder of metabolism in tumors (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eG). Thus, we systematically analyzed metabolism-related pathways and found a good consistency across cancer types, suggesting the functional conservation of SYTL4. Above all, SYTL4 might have an important role in promoting cancer development through immunology, epithelial mesenchymal transition (EMT), and metabolism disorder in pan-cancer.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Identification of Chemical Substances Interacting with SYTL4\\u003c/h2\\u003e \\u003cp\\u003eWe conducted CMap analysis to find out potential treatment regimens that could offset the tumor-enhancing effects facilitated by SYTL4. Firstly, we constructed a SYTL4-related gene signature, including top 150 upregulated and top 150 downregulated genes, which were determined by comparison between patients with SYTL4 high-expression and low-expression in each cancer type. The optimal feature matching method X Sum was used to compare SYTL4-related features with CMap gene features to obtain similarity scores for 1288 compounds. Scores of arachidonyltrifluoromethane, STOCK1N.35874 and X4.5.dianilinophthalimide are significantly lower in most cancer types, suggesting that they may have the potential to inhibit SYTL4-mediated oncogenic effects (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). To evaluate the value of SYTL4 in cancer treatments, we examined how expressions of SYTL4 correlates with responses to systematic treatment in patients with different cancer types. In the analysis of SYTL4 expression in most immunotherapies, the AUC value is not generally ideal (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). However, in a cutaneous malignant melanoma cohort, we observed higher expression of SYTL4 in group responding to treatment, and higher proportion of patients with high SYTL4 expression in response group. ROC curve analysis also shows that SYTL4 expression achieved good sensitivity and specificity in response to treatments (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC-E). As for chemotherapy, SYTL4 expression was correlated with drug sensitivity based on analyses from 3 different databases (CTRP, GDSC and PRISM). Obviously, SYTL4 is a potential drug-resistant gene (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eF-J). Taken together, expressions of SYTL4 might be a predictive biomarker of response to cancer therapy.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Aberrant Expression of SYTL4 among Cancers\\u003c/h2\\u003e \\u003cp\\u003eWe performed both solely differential analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA) and paired difference analysis to determine the dysregulated patterns of SYTL4 in cancers (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB) based on TCGA cohorts. Subsequently, through combinationally mining the resources of TCGA and GTEx database, we revealed expression profiles of SYTL4 from a pan-cancer perspective (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). The organ diagrams visualized expression distribution pattern of SYTL4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). Protein level of SYTL4 was validated with CPTAC database (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE). We found that SYTL4 was dysregulated in the majority of cancer types and exhibited consistent significantly downregulated expression patterns across cancer types. The HPA results also supported that the staining level of SYTL4 in most tumors was extremely low (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF). The external validation at the mRNA level was performed in the GEO database (Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Based on TCGA, TCGA-GTEx, GEO and CPTAC database, the above results were fully validated by logistics regression analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eG). We observed a good consistency in the expression trends across different omics, databases, and multiple tumors. In fact, ROC curve analysis estimated that SYTL4 mRNA levels in various tumors were of adequate sensitivity and specificity in diagnosis (AUC\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.7) (Figure S2). Combined with the expanded sample size of normal group, the results were still robust (Figure S3). This result was reproducible and consistent in multiple databases, multiple tumors, and multiple method, indicating that the dysregulated expression of SYTL4 may be functional in various cancers and is improbable to be a false discovery resulting from technical artifacts, opportunities, or sample qualification criteria biases. Interestingly, SYTL4 was also differentially expressed in many molecular subtypes (Figure S4).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Genetic Alterations of SYTL4 in Cancers\\u003c/h2\\u003e \\u003cp\\u003eTo investigate why SYTL4 was dysregulated across cancers, we analyzed genomic information from the TCGA pan-cancer cohort. We investigated 2D structure of SYTL4 mutated sites, demonstrating the post-translational modification sites that may be affected (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). The cBioPortal database indicates that SYTL4 presents a certain frequency of genetic alterations in most cancers, mutation and amplification are the most common types of genetic alterations of SYTL4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). Further analysis showed missense mutations is the major type of mutations (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC). Then we also analyzed the spearman correlations between SYTL4 and 10 types of genomic signatures (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD), which showed significant associations with different preference in certain cancers such as BRCA, CESC, and COAD. To investigate genetic aberrations of SYTL4 in cancer, we examined SCNA on SYTL4. In general, high frequency of SCNA on SYTL4 was observed in most cancer types (more than 5% of all samples), but low in only few cancers (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE). Clearly, SCNA is key in gene expression regulation of SYTL4 in tumors (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eF). Next we assessed how SCNA affects SYTL4 mRNA levels by computing spearman correlation between gene expression and masked copy-number segment in TCGA. It showed that mRNA level of SYTL4 negatively correlated with the SCNA in majority of tumors (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eG). It suggests that copy-number aberrations of SYTL4 are frequent in cancers and may regulate gene expression. Besides SCNA, aberrant DNA methylations on promoter frequently occurred during tumorigenesis. In addition, SYTL4 displayed a relatively consistent methylation pattern across the pan-cancer cohort, and most tumorous tissues showed hypomethylation than normal tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eH). SYTL4 mRNA levels generally positively correlated with DNA methylation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eI). Alternative splicing is an important form of post-transcriptional regulation, which may regulate the expression of SYTL4. Our analysis showed SYTL4 is mainly spliced in three ways, AP, AT, and ES (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eJ).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Clinical Relevance of SYTL4\\u003c/h2\\u003e \\u003cp\\u003eTo elucidate the clinical significances of SYTL4 in cancer, association of SYTL4 expression with clinical stage and survival in cancer patients were examined. mRNA level of SYTL4 is associated with clinical staging (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA), which is important to select treatment strategies. The atlas of survival in pan-cancer shows that SYTL4 is associated with various survival types for multiple cancers (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB), and relatively homogeneous correlation was observed, as SYTL4 can often act as a protective factor in various types of cancer, also as risk factor in a few tumors, indicating that SYTL4 may play different roles in various cancers. Its functional roles in cancer survival need more exploration. To further supplement pan-cancer atlas, we used a forest plot to display the cox survival analysis results of 4 survival types (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC-F) and Kaplan-Meier analysis to exhibit results of KIRP, PAAD through log-rank test (Figure S5). Results suggest that SYTL4 expression associates with survival of cancer patients.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6 High SYTL4 Expression Correlates with Immune Infiltration in Cancer\\u003c/h2\\u003e \\u003cp\\u003eThe ongoing interactions among tumor cells and immune cells in TME are determinate during development, advancement, metastasis, and reaction to therapies of tumors\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e. We investigated the involvement of SYTL4 in immune infiltration across cancers by examining correlations between SYTL4 expression and genes of immune activation/inhibition, chemokine, chemokine receptor, and major histocompatibility complex (MHC). We found a consistent positively corelated trend (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). To clarify the particular types of cells affected by SYTL4 in TME, we investigated correlations of SYTL4 mRNA levels with immune infiltrations and stromal cells abundance using TIMER2.0 database. In most cancer types, Cancer associated fibroblast, Endothelial cell, macrophage neutrophil are positively related to SYTL4 expression in most cancer types, while CD4\\u0026thinsp;+\\u0026thinsp;Th1 cell, CD8\\u0026thinsp;+\\u0026thinsp;EPIC cell and activated NK cell are negatively related to SYTL4 expressions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). The results suggest that SYTL4 is involved to some extent in immune exclusion or immune cell infiltration and may function especially in immune evasion, interactions between tumors and immune system pathway. Notably, because of different proportions of infiltrated immunocytes and unique tumor microenvironments in different cancers, the trends of these correlation varies slightly in different tumors. However, the results of the 7 evaluation methods based on different software mutually corroborate, confirming the accuracy of our analysis. In addition, TISCH database describes expression landscape of SYTL4 in multiple datasets of single cell, showing that although SYTL4 is not highly expressed in most tumors, it mainly originates from malignant cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC), verifying the above immune infiltration results. In summary, we provide a thorough examination and depiction of SYTL4 in immune infiltration and the TME across various types of cancer.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.7 Single-Cell Analysis in CRC\\u003c/h2\\u003e \\u003cp\\u003eTo improve the resolution of the data, we analyzed the potential functions of SYTL4 involved in malignant cells at single-cell level. We used strict criteria for quality control, considering the potential influence of genes related to cell cycle on reduced dimensionality. Subsequently, cell cycle score was computed for each cell and regression correction was carried out during PCA (Figure S6). After the integration by Run Harmony, the cell distributed evenly across samples, indicating good integration effect (Figure S7). Then we identified 18 clusters and manually annotated them, ultimately annotating as 11 cell types (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA-B). As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eC, the manual annotation strictly adopted classic or well-established markers. Interestingly, we found that SYTL4 is mainly expressed in malignant cells, but there are still a large number of malignant cells that do not express SYTL4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eD-E). We identified the DEGs between SYTL4-positive malignant cells and SYTL4-negative malignant cells and performed KEGG enrichment analysis to identify their functional differences. KEGG enrichment analysis suggests that SYTL4-positive malignant cells are mainly characterized by metabolic disorders, while SYTL4-negative malignant cells are characterized by proliferation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eF).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.8 Association between SYTL4 and Microbiome in Pan-cancer.\\u003c/h2\\u003e \\u003cp\\u003eMicrobes play complicated roles in cancer biology and immune response in cancer and be significant for development and therapy in cancers\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR25\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e. Among the 5 types of tumors, we detected correlations between SYTL4 and some microbes. The highest detection was in HNSC, while the lowest was in ESCA, showing mainly low correlations. Notably, in colorectal cancer, we found a moderate correlation of 0.401 between SYTL4 and Bifidobacteriales, which is colonized in the intestine and a key symbiotic bacteria. Bifidobacteriales can strengthen the intestinal barrier and benefit to inhibit tumor and inflammation\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e. In addition, Acidaminococcales, Dorea, Coprococcus, Phascolarctobacterium, Bifidobacterium, Acidaminococcaceae and Eubacteriaceae also significantly negatively correlated with SYTL4. However, Spirochaetales and Spirochaetes in HNSC, Selenomonadales and Selenomonadaceae in ESCA were positively correlated with SYTL4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e), suggesting that SYTL4 may modulate microbiota homeostasis in various cancers.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4 DISCUSSION\",\"content\":\"\\u003cp\\u003eSYTL4 is found to be a novel chemo resistant gene in TNBC and its high expression indicates worse prognosis in TNBC patients receiving taxane treatment \\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. SYTL4 and SDC2 can also be upregulated by Epstein-Barr virus-encoded latent membrane protein 1 to promote secretion of EV through NF-κB signaling, leading to enhanced cancer progression of nasopharyngeal carcinoma\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. However, a comprehensive understanding to the role and mechanisms of SYTL4 in other cancer types is lacked. Our study firstly analyzed expression signatures and clinical significance of SYTL4 from a pan-cancer perspective and provided comprehensive information about the function and mechanism of SYTL4 in cancer based on multiple databases, which benefit further exploration of SYTL4 in cancer.\\u003c/p\\u003e \\u003cp\\u003eSYTL4 is highly expressed in TNBC rather than other breast cancer subtypes\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. In our study, SYTL4 is relatively high-staining in HPA pathology analysis, while mRNA level is slightly downregulated in BRCA, which might be due to the analysis based on a comprehensive BRCA rather than subtypes. It suggests the regulation of SYTL4 is cancer-specific and subtype-specific, and maybe related to regulation network by ER, PR and HER2. But what exactly leads to the dysregulation need further investigation. We found expressed differentially in various tumors, and SCNA and DNA methylation have led to its abnormal mRNA expression across cancers. In addition, alternative splicing of SYTL4 in ways of AP, AT and ES might contribute to post-transcriptional regulation of SYTL4, resulting to the different dysregulation of SYTL4 in pan cancer.\\u003c/p\\u003e \\u003cp\\u003eIncreasingly researches found that microbiome play significant and broad functions in cancers\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR25\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e. In our study, we found SYTL4 was correlated with bifidobacterial in rectum adenocarcinoma. As primary colonizers of the human infant gut, bifidobacterium genus and their by-products generated during carbohydrate metabolism are reported to produce beneficial health effects at both local and systemic levels, like influencing gut homeostasis, restricting pathogen colonization or invasion and regulating immune system via innate or adaptive immune responses\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e. Moreover, probiotic Bifidobacteria cocktail exhibits protective activity against CRC\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e. Thus, the protective role of SYTL4 might related to bifidobacterium in rectum adenocarcinoma. Supplement of bifidobacterium may provide potential benefit to rectum adenocarcinoma patients with low SYTL4 expression.\\u003c/p\\u003e \\u003cp\\u003emRNA level of SYTL4 is associated with clinical staging and showed excellent diagnostic and prognostic values in various cancers. For example, SYTL4 is highly expressed in ACC at the later stage, which is consistent with the result that SYTL4 is a risk factors in ACC. However, high SYTL4 expression associated with better prognosis in PRAD tumors. Thus, SYTL4 may serve as a protective factor in different types of cancer, also as risk factors in other tumors. In addition, high SYTL4 Expression correlates with Immune Infiltration in Cancer, thus SYTL4 might be a biomarker predicts survival and response to drugs, including immune therapies, which can help efficient therapy selection of patients and optimal utilization of medical resources.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, we systematically analyzed the expression, clinical values of SYTL4 and its association in tumor microenvironment in a pan-cancer perspective, providing knowledges for further functional and molecular mechanistic studies. The difference of the role of SYTL4 among various cancers need to be explored further.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eACC: Adrenocortical carcinoma; AUC: Area under the curve; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: Cholangiocarcinoma; CCLE: Cancer cell line encyclopedia; COAD: Colon adenocarcinoma; COAD/READ: Colon adenocarcinoma/Rectum adenocarcinoma esophageal carcinoma; DSS: Disease-specific survival; DFI: Disease-free interval; DLBC: Lymphoid neoplasm diffuse large B-cell lymphoma; ESCA: Esophageal carcinoma; FAP: Fibroblast activation protein-\\u0026alpha;; GTEx: Genotype Tissue-Expression; GSEA: gene set enrichment analysis; GSVA: gene set variation analysis; GBM: Glioblastoma multiforme; GBMLGG: Glioma; HNSC: Head and Neck squamous cell carcinoma; ICIs: Immune checkpoint inhibitors; KICH: Kidney chromophobe; KM: Kaplan-Meier; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute myeloid leukemia; LGG: Brain lower grade glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; NCI: National Cancer Institute; OS: Overall survival; OV: Ovarian serous cystadenocarcinoma; PFI: Progress-free interval; PPI: Protein-protein interaction; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and paraganglioma; PRAD: Prostate adenocarcinoma; ROC: Receiver operating characteristic curve; SARC: Sarcoma; SKCM: Skin cutaneous melanoma; STAD: Stomach adenocarcinoma; STES: Stomach and esophageal carcinoma; TCGA: The Cancer Genome Atlas; TIICs: Tumor-infiltrating immune cells; TME: Tumor microenvironment; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine corpus endometrial carcinoma; UCS: Uterine carcinosarcoma; UVM: Uveal melanoma.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCONFLICT OF INTEREST STATEMENT\\u003c/h2\\u003e \\u003cp\\u003eAll authors declare that no conflict of interest exists.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eYH and XB were responsible for the conceptualization, data acquisition, and analysis. JL and ZH conducted the bioinformatics analyses and interpreted the results. SC assisted in the study's design and provided critical revisions to the manuscript. JH supervised the entire project, guided the research process, and critically revised the manuscript. All authors have read and approved the final manuscript.\\u003c/p\\u003e\\u003ch2\\u003eACKNOWLEDGMENTS\\u003c/h2\\u003e \\u003cp\\u003eWe thank the Colorectal and Anal Surgery department at The First Affiliated Hospital of Wenzhou Medical University for their support. We also acknowledge the data providers and curators of the public databases used in this study. Special thanks to our bioinformatics team for their assistance and to our colleagues for their constructive feedback.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eRaw data, processed data, clinical data were obtained at GDC (https://portal.gdc.cancer.gov/). Clinical information of the Memorial Sloan Kettering Cancer Center were obtained from cBioPortal (http://www.cbioportal.org). RNA, DNA methylation, gene-level copy number (gistic2) are available in the PANCAN database.The transcriptional, TCPA data, ABSOLUTE purity/ploidy information were obtained from TCGA database. The molecular subtypes were defined according to TCGA Subtype.20170308.tsv. These public databases are freely available, and data extraction policies of the databases were strictly complied. Therefore, ethical review and approval from the ethical committee are not required.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eWang Z, Jensen MA, Zenklusen JC. A practical guide to The Cancer Genome Atlas (TCGA). Methods in Molecular Biology. Volume 1418. Humana Press Inc.; 2016. pp. 111\\u0026ndash;41. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/978-1-4939-3578-9_6\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/978-1-4939-3578-9_6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Wspolczesna Onkologia. 2015;1A:A68\\u0026ndash;77. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.5114/wo.2014.47136\\u003c/span\\u003e\\u003cspan address=\\\"10.5114/wo.2014.47136\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFukuda M. Slp4-a/granuphilin-a inhibits dense-core vesicle exocytosis through interaction with the GDP-bound form of Rab27A in PC12 cells. J Biol Chem. 2003;278(17):15390\\u0026ndash;6. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1074/jbc.M213090200\\u003c/span\\u003e\\u003cspan address=\\\"10.1074/jbc.M213090200\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFukuda M. Rab27 Effectors, Pleiotropic Regulators in Secretory Pathways. Traffic. 2013;14(9):949\\u0026ndash;63. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1111/tra.12083\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/tra.12083\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRafi SK, Fern\\u0026aacute;ndez-Ja\\u0026eacute;n A, \\u0026Aacute;lvarez S, Nadeau OW, Butler MG. High functioning autism with missense mutations in synaptotagmin-like protein 4 (SYTL4) and transmembrane protein 187 (TMEM187) genes: SYTL4- protein modeling, protein-protein interaction, expression profiling and microRNA studies. Int J Mol Sci. 2019;20(13):3358. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/ijms20133358\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijms20133358\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSzklarczyk D, Franceschini A, Wyder S, et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(D1):D447\\u0026ndash;52. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/nar/gku1003\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gku1003\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOkada M, Itoh MI, Haraguchi M, et al. b-series Ganglioside deficiency exhibits no definite changes in the neurogenesis and the sensitivity to Fas-mediated apoptosis but impairs regeneration of the lesioned hypoglossal nerve. J Biol Chem. 2002;277(3):1633\\u0026ndash;6. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1074/jbc.C100395200\\u003c/span\\u003e\\u003cspan address=\\\"10.1074/jbc.C100395200\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMiller IV, Grunewald TGP. Tumour-derived exosomes: Tiny envelopes for big stories. Biol Cell. 2015;107(9):287\\u0026ndash;305. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1111/boc.201400095\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/boc.201400095\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang Y, Guo Z, Tian Y, et al. MAPK1 promotes the metastasis and invasion of gastric cancer as a bidirectional transcription factor. BMC Cancer. 2023;23(1):959. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12885-023-11480-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12885-023-11480-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu XY, Jiang W, Ma D, et al. SYTL4 downregulates microtubule stability and confers paclitaxel resistance in triple-negative breast cancer. Theranostics. 2020;10(24):10940\\u0026ndash;56. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.7150/thno.45207\\u003c/span\\u003e\\u003cspan address=\\\"10.7150/thno.45207\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiao C, Zhou Q, Zhang Z, et al. Epstein-Barr virus-encoded latent membrane protein 1 promotes extracellular vesicle secretion through syndecan-2 and synaptotagmin-like-4 in nasopharyngeal carcinoma cells. Cancer Sci. 2020;111(3):857\\u0026ndash;68. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1111/cas.14305\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/cas.14305\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYuan H, Yan M, Zhang G, et al. CancerSEA: A cancer single-cell state atlas. Nucleic Acids Res. 2019;47(D1):D900\\u0026ndash;8. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/nar/gky939\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gky939\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLee E, Chuang HY, Kim JW, Ideker T, Lee D. Inferring pathway activity toward precise disease classification. PLoS Comput Biol. 2008;4(11). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1371/journal.pcbi.1000217\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pcbi.1000217\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMalta TM, Sokolov A, Gentles AJ, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell. 2018;173(2):338\\u0026ndash;e35415. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.cell.2018.03.034\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2018.03.034\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang C, Zhang H, Chen M, et al. A Survey of Optimal Strategy for Signature-Based Drug Repositioning and 1 an Application to Liver Cancer 2. Elife. 2022;11:e71880.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCerami E, Gao J, Dogrusoz U, et al. The cBio Cancer Genomics Portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401\\u0026ndash;4. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1158/2159-8290.CD-12-0095\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/2159-8290.CD-12-0095\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi J, Li Z, Gao Y, et al. Integrating single-cell RNA sequencing and prognostic model revealed the carcinogenicity and clinical significance of FAM83D in ovarian cancer. Front Oncol. 2022;12:1055648. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fonc.2022.1055648\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fonc.2022.1055648\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1126/scisignal.2004088\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/scisignal.2004088\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi T, Fu J, Zeng Z, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509\\u0026ndash;14. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/NAR/GKAA407\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/NAR/GKAA407\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDohlman AB, Arguijo Mendoza D, Ding S, et al. The cancer microbiome atlas: a pan-cancer comparative analysis to distinguish tissue-resident microbiota from contaminants. Cell Host Microbe. 2021;29(2):281\\u0026ndash;e2985. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.chom.2020.12.001\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.chom.2020.12.001\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eElhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell. 2023;41(3):404\\u0026ndash;20. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ccell.2023.01.010\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ccell.2023.01.010\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett. 2017;387:61\\u0026ndash;8. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.canlet.2016.01.043\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.canlet.2016.01.043\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHinshaw DC, Shevde LA. The tumor microenvironment innately modulates cancer progression. Cancer Res. 2019;79(18):4557\\u0026ndash;67. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1158/0008-5472.CAN-18-3962\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/0008-5472.CAN-18-3962\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHelmink BA, Khan MAW, Hermann A, Gopalakrishnan V, Wargo JA. The microbiome, cancer, and cancer therapy. Nat Med. 2019;25(3):377\\u0026ndash;88. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41591-019-0377-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41591-019-0377-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSepich-Poore GD, Zitvogel L, Straussman R, Hasty J, Wargo JA, Knight R. The microbiome and human cancer. Science. 2021;371(6536):eabc4552. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1126/science.abc4552\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/science.abc4552\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCullin N, Azevedo Antunes C, Straussman R, Stein-Thoeringer CK, Elinav E. Microbiome and cancer. Cancer Cell. 2021;39(10):1317\\u0026ndash;41. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ccell.2021.08.006\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ccell.2021.08.006\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOloan Pardede S, Ayu Paramastri K, Hegar B, Rafli A. The Proportion of Bifidobacterium and Escherichia Coli in Colon of Children with Recurrent Urinary Tract Infection. Saudi J Kidney Dis Transpl. 2020;31(5):898\\u0026ndash;904.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlessandri G, Ossiprandi MC, MacSharry J, van Sinderen D, Ventura M. Bifidobacterial Dialogue With Its Human Host and Consequent Modulation of the Immune System. Front Immunol. 2019;10:2348. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fimmu.2019.02348\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fimmu.2019.02348\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eParisa A, Roya G, Mahdi R, Shabnam R, Maryam E, Malihe T. Anti-cancer effects of Bifidobacterium species in colon cancer cells and a mouse model of carcinogenesis. PLoS ONE. 15(5), e0232930. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1371/journal.pone.0232930\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0232930\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\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\":\"info@researchsquare.com\",\"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\":\"Synaptotagmin Like 4, Diagnosis, Pan-cancer, Prognosis, TME\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4929307/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4929307/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eSYTL4 (Synaptotagmin Like 4) encodes a protein of synaptotagmin like protein family, which participates in intracellular membrane trafficking. Currently, its role and mechanisms in cancer remain unveiled, necessitating additional comprehensive analysis across different types of cancer to assess its potential in diagnosis, prognosis, chemotherapy, and immunotherapy in cancer. In our study, the mRNA level, threshold for copy number alterations, segmentation of masked copy number alterations, and methylation of SYTL4 DNA were analyzed based on data from TCGA pan-cancer cohort. miRNA, TCPA, mutation and clinical data were analyzed to evaluate diagnostic and prognostic significances of SYTL4. Then the results were checked using cBioPortal and GEO database. The protein levels were analyzed and evaluated based on HPA database and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Biological roles of SYTL4 in pan-cancer were explored by GSEA. We use multiple immune infiltration algorithms in TIMER2.0 and TISCH database to cross-verify the associations between SYTL4 expression and tumor immune microenvironment. Additionally, we depicted a pan-cancer survival map and explored the differences of gene expressions among cancers with different molecular subtypes. Through chemotherapy data from CellMiner, GDSC, CTRP database, we clarified the relationship between SYTL4 and drug resistance. Finally, we explored the chemical substances that affect SYTL4 expression through CTD database. This study systematically and comprehensively reveals the functions of SYTL4 and potential clinical diagnostic and therapeutic predictive values of SYTL4 in pan-cancer.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Integrated analysis unraveling the immunologic and clinical prognostic values of Synaptotagmin Like 4 in pan-cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-01 16:54:06\",\"doi\":\"10.21203/rs.3.rs-4929307/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"ce17f924-c632-4e8c-87db-0e1a147630ae\",\"owner\":[],\"postedDate\":\"October 1st, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-10-07T05:08:42+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-10-01 16:54:06\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4929307\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4929307\",\"identity\":\"rs-4929307\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}