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. 1A), especially in colon cancer (Fig. 1B). 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. 1C). 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. 1D). 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. 1E). 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. 1F). 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. 1G). SYTL4 may also engaged in the disorder of metabolism in tumors (Fig. 1G). 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. 2A). 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. 2B). 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. 2C-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. 2F-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. 3A) and paired difference analysis to determine the dysregulated patterns of SYTL4 in cancers (Fig. 3B) 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. 3C). The organ diagrams visualized expression distribution pattern of SYTL4 (Fig. 3D). Protein level of SYTL4 was validated with CPTAC database (Fig. 3E). 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. 3F). 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. 3G). 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. 4A). 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. 4B). Further analysis showed missense mutations is the major type of mutations (Fig. 4C). Then we also analyzed the spearman correlations between SYTL4 and 10 types of genomic signatures (Fig. 4D), 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. 4E). Clearly, SCNA is key in gene expression regulation of SYTL4 in tumors (Fig. 4F). 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. 4G). 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. 4H). SYTL4 mRNA levels generally positively correlated with DNA methylation (Fig. 4I). 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. 4J).
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. 5A), 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. 5B), 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. 5C-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 tumors21–23. 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. 6A). 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. 6B). 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. 6C), 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. 7A-B). As shown in Fig. 7C, 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. 7D-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. 7F).
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 cancers24–26. 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 inflammation27. 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.