Differential SYT11 expression in various cancers
According to the results obtained from the TIMER2 database, SYT11 was weakly expressed in most cancers, such as bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), glioblastoma multiforme (GBM), renal hepatocellular carcinoma (KICH), kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), and uterine corpus endometrial carcinoma (UCEC) than in adjacent normal tissues, while being strongly expressed in cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (NHSC), liver hepatocellular carcinoma (LIHC), pheochromocytoma and paraganglioma (PCPG), and thyroid carcinoma (THCA) (Fig. 1A). Since TCGA database contains relatively insufficient information for normal tissues, we also included samples from the GTEx database for further analysis. SYT11 expression in the normal tissues of patients with adrenocortical cancer (ACC), BLCA, CESC, COAD, KICH, LUSC, READ, testicular germ cell tumors (TGCT), and UCEC is lower than the corresponding tumor tissues according to the GTEx database, while the pattern was opposite for the patients with CHOL, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), pancreatic ductal adenocarcinoma (PAAD), PCPG, and skin cutaneous melanoma (SKCM) (Fig. 1B). However, SYT11 expression in the normal and tumor tissues of the patients with BRCA, ESCA (esophageal carcinoma), GBM, NHSC, KIRC, KRIP, LIHC, LUAD, ovarian serous cystadenocarcinoma (OV), PRAD, sarcoma (SARC), stomach adenocarcinoma (STAD), thyroid carcinoma (THYM), and UCS (uterine carcinosarcoma) were not significantly different (Supplementary Fig. 1). To investigate the SYT11 expression in detail, we generated a pathological stage plot using the GEPIA2 module. SYT11 RNA expression levels were significantly and positively associated with the late clinical stages of BLCA and STAD (Fig. 1C and Supplementary Fig. 2).
Prognostic analysis of SYT11 in pan-cancer
To investigate the influence of SYT11 on the prognosis of various tumors, a heat map of the SYT11 gene with significant prognostic value was generated using the GEPIA2 database, and the samples were divided according to the median SYT11 expression. Aberrant SYT11 expression mainly affected the improved prognosis of overall survival (OS) for patients with KIRC (p = 0.00088) and LUAD (p = 0.0053), whereas high SYT11 expression was associated with poor prognosis for patients with ACC (p = 0.054), BLCA (p = 0.05), LAML (p = 0.023), MESO (p = 0.0036), and UVM (p = 0.023) (Fig. 2A). Moreover, high SYT11 expression displayed unfavorable disease-free survival (RFS) for patients with ACC (p = 0.0083), BLCA (p = 0.02), and COAD (p = 0.031) tumors (Fig. 2B), but not others (Supplementary Figs. 3 and 4). Subsequently, we assessed the K–M plot to evaluate the relationship between SYT11 expression and prognosis of patients with different tumors. High SYT11 expression was associated with poor OS in patients with UCEC (p = 0.043), whereas patients with KIRC (p = 0.0098) and LUAD (p = 0.004) showed better OS (Fig. 3C). In addition, high SYT11 expression level was significantly associated with poor RFS in patients with OV (p = 0.033).
Genetic alteration of SYT11 in various cancers
Since genetic alterations are closely associated with tumorigenesis, the genetic variation of SYT11 in various cancers were determined using the cBioPortal TCGA cohort. As shown in Fig. 3A, alteration frequencies were high in the patients with UCS, CHOL, and LIHC, and the highest alteration frequency (> 10%) with ‘amplification’ in the patients with UCS. Accordingly, the most common alterations in SYT11 genes were missense (n = 71), truncating (n = 6), fusion (n = 4), and splice (n = 3) mutations, and the T68N mutation (Thr to Asn) was observed in the phosphorylation site. In addition, R342C was the main genetic alteration (one case in PRAD, two cases in UCEC, and one case in COAD) among the missense mutations (Fig. 3B). Based on the UCS showing the highest genetic alteration frequencies, we further analyzed the association between SYT11 and clinical attributes in UCS-TCGA. In the analysis of the putative copy number, SYT11 expression was the highest in the amplification group compared to that in the other groups, including shallow deletion, diploidy, and gain. Simultaneously, it was positively associated with copy number (Spearman r = 0.24, p = 0.0692; Pearson r = 0.32, p = 0.016; Fig. 3C). Regarding the association between SYT11 expression and copy number, we identified the molecular profiles of SYT11 genomic alterations. As shown in Fig. 3D, SNORA80E and UBQLN4 were significantly associated with SYT11 alteration as shown by volcano plots. Additionally, GON4L, RIT1, SCARNA4, SNORA80E, ARHGEF2, KHDC4, LAMTOR2, RAB25, RXFP4, SSR2, and UBQLN4 were significantly associated with SYT11 alterations.
Difference of SYT11 methylation level in pan-cancer
The Shiny Methylation Analysis Resource Tool (SMART) database was used to analyze the difference in SYT11 methylation levels between normal and primary tumor tissues. As shown in Fig. 4A, CpG-aggregated SYT11 methylation was significantly lower in tumor tissues than that in corresponding normal tissues for patients with BLCA (p ≤ 0.0001), BRCA (p ≤ 0.01), COAD (p ≤ 0.01), HNSC (p ≤ 0.0001), KIRC (p ≤ 0.0001), LIHC (p ≤ 0.001), LUAD (p ≤ 0.01), LUSC (p ≤ 0.0001), PCPG (p ≤ 0.05), PRAD (p ≤ 0.0001), READ (p ≤ 0.001), and UCEC (p ≤ 0.0001), while being the opposite for patients with CHOL (p ≤ 0.05). Since promoter methylation alters gene expression, we explored the promoter methylation level of SYT11 in tumor and normal tissues using the ULCAN database. The results showed that SYT11 promoter methylation was downregulated in patients with various tumors, including BLCA, BRCA, COAD, CESC, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, TGCT, and UCEC, but was lower in the primary tumor tissue then that in normal tissue only for the patients with CHOL. These results suggest that low SYT11 expression is less strongly associated with promoter methylation in most tumors.
Prediction of SYT11 upstream miRNA and differential expression
MicroRNAs (miRNAs) play crucial roles in post-transcriptional gene expression via base pairing within mRNAs. Since SYT11 expression is downregulated in various cancers, regulatory miRNAs are possibly highly expressed in cancer. To identify the target miRNAs of SYT11, we used miRNA prediction tools, including miRDB, TargetScan, and miRWalk, and then intersected 13 miRNAs by the Venn diagram (Fig. 5A). These 13 miRNAs were further analyzed to explore their differential expression profiles using a meta-profile heatmap of tissue samples from various cancer patients and healthy participants (Fig. 5B). Based on meta-profile heatmap, hsa-miR-19a-3p, hsa-let-7g-5p, hsa-let-7i-5p, and hsa-miR-98-5p showed significant differential expression in tissue samples of cancer patients and healthy participants, and presented binding sites with SYT11 3’-UTR (Fig. 5C). Simultaneously, biological network analysis showed that miRNA-mediated regulation was mostly enriched in intercellular signaling, environmental information processing, and cytoskeletal interactions, such as the MAPK signaling pathway, ECM receptor interaction, focal adhesion, and adherens junction (Fig. 5D and Supplementary Table 1). To further assess the relationship between expression and clinical significance, correlation analyses and Kaplan–Meier estimation were conducted between the four candidate miRNAs and SYT11 expression in pan-cancer samples. Among the miRNA/SYT11 pairs, hsa-let-7g-5p/SYT11, hsa-miR-19a-3p/SYT11, and hsa-miR-98-5p/SYT11 were negatively correlated with 11, 15, and 8 tumors, respectively. Conversely, the hsa-let-7i-5p/SYT11 pair was positively associated with most cancers (Fig. 6A). In terms of clinical survival prognosis, highly expressed hsa-let-7g-5p was linked to poor OS in the patients with THCA, COAD, SARC, and KIRC; hsa-miR-19a-3p in the patients with KIRC, THCA, SKCM, SARC, ACC, DLBC, BRCA, and LAML; hsa-miR-98-5p in the patients with TGCT, PRAD, ESCA, LGG, and HNSC; and hsa-let-7i-5p in the patients with KIRC, LGG, KIRP, and TGCT (Fig. 6B). These results indicate that the candidate miRNAs may play an important role in reducing SYT11 expression and prognosis.
Immune infiltration analysis of SYT11
Since immune cell infiltration plays a crucial role in tumor progression, we investigated the relationship between SYT11 expression and immune cell infiltration in various tumors. As shown in Fig. 7, SYT11 expression was significantly positively associated with CD8 + T cell (in 14 types of cancer) and macrophage (in 13 types of cancer) infiltration. HNSC, LUSC, STAD, and THCA showed a positive tendency in B cells, but there was no clear trend in natural killer (NK) cells. Interestingly, SYT11 expression in myeloid-derived suppressor cells (MDSCs) showed a significant negative association with almost all cancer types, excluding ACC, MESO, OV, SKCM, and UCEC, while these negative correlations were associated with few CD8 + T cells and macrophages. In addition, SYT11 expression positively correlated with cancer-associated fibroblasts (CAFs) in most cancer types, except for DLBC, GBM, SARC, and UCS. These results suggest that SYT11 plays an important role in immune cell infiltration and may serve as a novel biomarker of various tumors.
SYT11-related gene enrichment analysis data
To further explore the potential mechanism of SYT11 in various tumors and clinical outcomes, we attempted to obtain a SYT11-interacted gene network (Fig. 8A). Twenty-four interacting genes and their expression profiles in various tumor and normal tissues are presented in Fig. 8B. Our results indicated that PDLIM7, SGIP1, DAB2, INPP5K, and PIP5K1B expression was higher in tumor tissues than that in the corresponding normal tissues, whereas the remaining interacting genes showed opposite tendencies. To assess the relationship between SYT11 and these genes, enriched pathway and ontological analyses were performed simultaneously. Pathway enrichment analysis revealed that SYT11 was significantly associated with clathrin-mediated endocytosis, phosphoinositide metabolism, and Rho GTPase activation in Reactom_2022 and phosphatidylinositol metabolism and cell motility signaling pathway in BioPlanet_2019 (Fig. 8C and Supplementary Table 2). In the ontological analysis, SYT11 was significantly linked with the cellular response to actin nucleation, phosphatidylinositol metabolism, and membrane ruffle formation in GO Biological Process 2023 and diverse phosphatidylinositol-based activities in GO Molecular Function 2023 (Fig. 8D and Supplementary Table 2). We also assessed the STRING database to obtain the SYT11-interacting proteins to support gene set enrichment analysis. As shown in Fig. 8E, SYT11 interact with 10 proteins, and these PPIs were further analyzed to explore their biological and molecular processes. The biological process results showed that SYT11-correlated proteins were involved in neurotransmitter secretion, synaptic vesicle transport regulation, and SNARE complex assembly. The molecular process results suggest that SYT11-correlated proteins are linked to syntaxin-1 binding, SNAP receptor activity, SNARE binding, and clathrin binding.