Gene Expression Analysis Data
TIMER2 was used to analyze the differential expression of YKT6 between tumor and normal tissues in TCGA tumors. As shown in Fig. 1A, the expression of YKT6 in Bladder Urothelial Carcinoma (BLCA), Breast invasive carcinoma (BRCA), Cholangiocarcinoma (CHOL), Esophageal carcinoma (ESCA), Head and Neck squamous cell carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney renal papillary cell carcinoma (KIRP), Liver hepatocellular carcinoma (LIHC), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), Prostate adenocarcinoma (PRAD), Stomach adenocarcinoma (STAD), Uterine Corpus Endometrial Carcinoma (UCEC) (P<0.001), Colon adenocarcinoma (COAD), Glioblastoma multiforme (GBM) (P<0.01), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC)(P<0.05) was significantly overexpressed compared with the corresponding control tissues. However, the expression of YKT6 was significantly downregulated in Kidney renal clear cell carcinoma (KIRC) and Thyroid carcinoma (THCA)(P<0.001).
We went further to analyzing the expression of YKT6 by using tumor tissues and normal tissues in TCGA and GTEx data to get detailed statistical calculations. As shown in Fig. 1B, YKT6 was significantly upregualted in Adrenocortical carcinoma (ACC) (P<0.05), BLCA, BRCA, CESC, CHOL, COAD, Lymphoid Neoplasm Diffuse Large B-cell (DLBC), ESCA, GBM, HNSC, KICH, KIRP, Brain Lower Grade Glioma (LGG), LIHC, LUAD, LUSC, Ovarian serous cystadenocarcinoma (OV), Pancreatic adenocarcinoma (PAAD), PRAD, Rectum adenocarcinoma (READ), Sarcoma (SARC), Skin Cutaneous Melanoma (SKCM), STAD, Testicular Germ Cell Tumors (TGCT), Thymoma (THYM), UCEC and Uterine Carcinosarcoma (UCS) (P<0.001). By contrast, the expression of YKT6 was low expressed in Acute Myeloid Leukemia (LAML) and THCA(P<0.001). There were no statistical differences in Kidney renal clear cell carcinoma (KIRC) and Pheochromocytoma and Paraganglioma (PCPG).
We then analyzed the YKT6 protein level in different tumors of TCGA by using CPTAC dataset. As shown in Fig. 2A, protein expression of YKT was significantly overexpressed in BRCA, COAD, GBM, HNSC, KIRC, LIHC, UCEC and PAAD(P<0.001) compared with corresponding normal tissues.
Furthermore, we use GEPIA2 online tool to detect expression of YKT6 in different tumor stages. The data of Fig. 2B showed that there was a significant relation between level of YKT6 and the pathological stages of several tumors, including ACC (P = 0.0213), BLCA (P = 0.0354), COAD (P = 0.0000102), KICH (P = 0.000329), KIRC (P = 0.0355), LIHC (P = 0.00387), LUAD (P = 0.00954), OV (P = 0.0199), SKCM (P = 0.00912), THCA (P = 0.00562), UCS (P = 0.0226).
Survival Prognosis Analysis
To evaluate the relationship between YKT6 and the prognosis of patients with diverse kinds of cancer, the tumors were dichotomized into two groups (high-expression and low-expression groups) based on the level of YKT6 in TCGA and GEO datasets. As data displayed in Fig. 3A, we found that high-expression of YKT6 was positively correlated with poor prognosis of overall survival (OS) in different types of tumors including ACC (P = 0.0029), BLCA (P = 0.022), CESE (P = 0.04), Head and Neck squamous cell carcinoma (HNSC, P = 0.00034), LGG (P = 0.00025), LIHC (P = 0.0013), LUAD (P = 0.017), MESO (P = 0.025) and UVM (P = 0.00094). As shown in Fig. 3B, high-expression of YKT6 was associated with poor disease-free survival (DFS) prognosis for ACC (P = 0.015), BLCA (P = 0.0058), HNSC (P = 0.022), LGG (P = 0.00012), LIHC (P = 0.019), LUAD (P = 0.019), MESO (P = 0.013), PAAD (P = 0.0014), PRAD (P = 0.019) and UVM (P = 0.005).
Genetic Alteration Analysis
The genetic alteration of YKT6 in different types of tumors from TCGA was analyzed. As data described in Fig. 4A, YKT6 alteration frequency (> 4%) is the highest in ACC, with the primary alteration type being “Amplification”. We obtained that the second-most frequency of YKT6 alteration (> 1.5%) in cases with ESCA with “Amplification” as the main type. “Amplification” was the sole form of variation in DLBC, UCS, GBM and PCPG. The additional mutations and location of YKT6 were shown on Fig. 4B. No predominant genetic alterations were obtained. Locations of genetic alterations appeared to be sporadic. For example, a truncating mutation, R163* alteration, within the Synaptobrevin domain, was only found in two patients with UCEC (Fig. 4C). In UCEC patients, we detected whether YKT6 genetic alterations affect clinical survival prognosis. We found that prognosis in terms of OS (P = 0.495), DFS (P = 0.304), progression-free survival (PFS) (P = 0.125), and disease-specific (DS) (P = 0.268) were no significant difference between YKT6 altered group and unaltered group (Fig. 4D).
Moreover, we detected the relationship between YKT6 and tumor mutational burden (TMB) and microsatellite instability (MSI) with tumors by using TCGA. As described in Fig. 5A, we obtained that the expression of YKT6 was positively associated with TMB in LUAD (P = 4.92e-8), SARC (P = 0.00062), KIRC (P = 0.043) and ACC (P = 0.046). While, the expression of YKT6 was negative association with TMB in HNSC (P = 0.013) and THCA (P = 0.046). As described in Fig. 5B, we also explored that YKT6 expression was positively correlated with MSI in GBM (P = 0.022), CESC (P = 0.0017), LUAD (P = 0.043), SARC (P = 0.000016), KIPAN (P = 3.69e-9), KIRC (P = 0.015), LUSC (P = 0.0082) and LIHC (P = 0.012). By contrast, the expression of YKT6 was negatively correlated with MSI in GBMLGG (P = 0.0044), COAD (P = 0.00017), COADREAD (P = 0.00010), STES (P = 0.031), THCA (P = 0.0033) and DLBC (P = 0.016). These results suggested the genetic alteration of YKT6 may be considered as potential novel drivers of some tumors.
Dna Methylation Analysis
DNA methylation, as an important epigenetic regulator of postreplication, played a significant role in tumorigenesis19. Figure 6 showed that there was a hypermethylation status in promoter region of YKT6 in KIRC, LUSC and PAAD. While, there was a hypomethylation level in the promoter region of YKT6 in BLCA, HNSC, KIRP, LUAD, PRAD, TGCT, THCA and UCEC. The occurrence and development of tumors was affected by up-regulated or down-regulated DNA methylation state of target gene.
We also used “R packages” to explore the relationship between YKT6 DNA methylation and etiopathogenesis of diverse types of tumors in TCGA database. As the data shown in Fig. 7, we obtained a significant positive association of YKT6 DNA methylation and gene expression at the probe of the non-promoter region as cg14549774 in several tumors. We also explored that YKT6 DNA methylation was positively related with cg15972849 in READ, SKCM and THCA. Meanwhile, YKT6 DNA methylation was negatively correlated with cg15972849 in RRCA, GBMLGG and LUAD. Moreover, we detected that YKT6 DNA methylation was positively associated with cg1841261 in STAD.
Immune Infiltration Analysis
Tumor-infiltrating immune cells, as an integral part of tumor microenvironment (TME), played a crucial role in tumor progression and development20, 21. We then used the TIMER, CIBERSORT, CIBERSORT-ABS, TIDE, XCEL, MCPCOUNTER, QUANTISEQ and EPIC algorithms to detect the possible association between the different immune infiltration and immune cells and YKT6 level in different types of tumors in TCGA. We detected that YKT6 level was positively associated with cancer-associated fibroblasts (CAFs) for TCGA tumors of COAD and LGG (Fig. 8A and Fig. 8B).
Furthermore, as data shown in Fig. 8C and Fig. 8D, we discovered a significantly positively association between YKT6 level and endothelial cell in tumors of COAD, HNSC-HPV+, OV, READ and THCA. While a negative relationship was obtained between YKT6 expression and endothelial cell in KIRC.
Enrichment Analysis Of Ykt6-related Gene
To explore the mechanism of YKT6 in tumorigenesis, we then sought to identify YKT6-binding proteins and YKT6 level-correlated genes for a variety of pathway enrichment analyses. In Fig. 9A, we got 50 YKT6-binding proteins by using STRING online tool. We obtained the top 100 genes which associated with YKT6 expression by using GEPIA2 online tool. As data shown in Fig. 9B, we found that the expression of YKT6 was positively related with FTSJ2 (R = 0.61), RALA (R = 0.61), ABCF2 (R = 0.63), POLD2 (R = 0.54) and EIF3B (R = 0.58) genes (all P < 0.001). Heatmap data showed that YKT6 gene had a significant positive association with the above five genes in the majority of tumors (Fig. 9C).
And then, we used KEGG pathway and GO enrichment analyses to explore the functions of YKT6. We found that “Syntaxin binding”, “SNARE complex”, “vesicle fusion” and “DNA replication” may be involved in the influence of YKT6 on tumor pathogenesis (Fig. 9D and Fig. 9E).