Medicine OPEN
A pan-cancer analysis of the oncogenic role of YKT6 in human tumors
Xuezhong Zhang, MDa,b,* [D, Mark Lloyd G. Dapar, PhDa,c,d, Xin Zhang, MDb, Yingjun Chen, MDe
Abstract
YKT6, as a Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) protein with vesicle trafficking, plays an essential role in the development and progression of tumor. However, the gene of YKT6 has not been fully assessed in pan- cancer studies. We aim to investigate the gene of YKT6 across 33 different types of tumor by using the Cancer Genome Atlas, Gene Expression Omnibus database, and other several kinds of bioinformatic tools. YKT6 is significantly up-regulated in most tumors, and we found that overexpression of YKT6 is positively associated with poor prognosis of overall survival and poor disease-free survival prognosis in several tumors, such as Adrenocortical carcinoma, Bladder Urothelial Carcinoma, Head and Neck squamous cell carcinoma. We also detected distinct associations exist between YKT6 and tumor mutational burden or microsatellite instability with tumors. YKT6 expression was positively related to cancer-associated fibroblasts for TCGA tumors of colon adenocarcinoma and LGG. Furthermore, we discovered a significantly positively correlation between YKT6 expression and endothelial cell in tumors of colon adenocarcinoma, HNSC-HPV+, OV, READ and THCA. While a negative relationship was obtained between YKT6 expression and endothelial cell in KIRC. Moreover, “Syntaxin binding,” “SNARE complex,” “vesicle fusion” and “DNA replication” are involved in the influence of YKT6 on tumor pathogenesis. Our pan-cancer analysis offers a deep comprehending the gene of YKT6 in tumoeigenesis from viewpoint of clinical tumor samples.
Abbreviations: ACC = adrenocortical cancer, BLCA = bladder urothelial carcinoma, BRCA = breast invasive carcinoma, CESC = cervical squamous cell carcinoma and endocervical adenocarcinoma, CHOL = cholangiocarcinoma, COAD = colon adenocarcinoma, DFS = disease-free survival, DLBC = Lymphoid Neoplasm Diffuse Large B-cell, ESCA = esophageal carcinoma, GBM = glioblastoma multiforme, GEO = gene expression omnibus, GO = gene ontology, HCC = hepatocellular carcinoma, HNSC = head and neck squamous cell carcinoma, KEGG = Kyoto Encyclopedia of Genes and Genomes, KICH = kidney chromophobe, KIRC = kidney renal clear cell carcinoma, KIRP = kidney papillary cell carcinoma, LAML = acute myeloid leukemia, LIHC = liver hepatocellular carcinoma, LGG = lower grade glioma, LUAD = lung adenocarcinoma, LUSC = lung squamous cell carcinoma, MESO = mesothelioma, MSI = microsatellite instability, NSCLC = non-small-cell lung cancer, OS = overall survival, OSCC = oral squamous cell carcinoma, OV = ovarian serous cystadenocarcinoma, PAAD = pancreatic adenocarcinoma, PCPG = pheochromocytoma & paraganglioma, PRAD = prostate adenocarcinoma, READ = rectum adenocarcinoma, SARC = sarcoma, SKCM = skin cutaneous melanoma, SNAREs = soluble N-ethylmaleimide-sensitive factor attachment protein receptors, STAD = stomach adenocarcinoma, TCGA = the Cancer Genome Atlas, TGCT = testicular germ cell tumors, THYM = thymoma, THCA = thyroid carcinoma, TMB = tumor mutational burden, UCEC = uterine corpus endometrial carcinoma, UCS = uterine carcinosarcoma, UVM = ocular melanomas.
Keywords: analysis, pan-cancer, prognosis, tumor, YKT6
1. Introduction
Due to the complexity of tumorigenesis, it is significant to perform pan-cancer gene expression analyses and explore the association between clinical prognosis and possible molecu- lar mechanisms.[1] Both publicly funded the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database
provide functional genomics data about a wide variety of tumors and enable us to conduct pan-cancer analyses.
Soluble N-ethylmaleimide-sensitive factor attachment pro- tein receptors (SNAREs) proteins are commonly responsible for helping vesicle trafficking between membranes.[2,3] It is the main fusion of SNAREs to mediate membrane fusion.[4,5] SNAREs possess membrane specificity, and they undergo
The authors have no funding and conflicts of interest to disclose.
All data generated or analyzed during this study are included in this published article [and its supplementary information files].
The datasets used in this investigation are available through public repositories.
a Department of Biology, College of Arts and Sciences, Central Mindanao University, Musuan, Philippines, b Department of Laboratory Medicine, Zibo Central Hospital, Zibo, China, ” Center for Biodiversity Research and Extension in Mindanao, Central Mindanao University, Musuan, Philippines, d Microtechnique and Systematics Laboratory, Natural Science Research Center, Musuan, Philippines, ” Department of Infectious Diseases, Binzhou Medical University Hospital, Binzhou, China.
*Correspondence: Xuezhong Zhang, Department of Biology, College of Arts and Sciences, Central Mindanao University, Musuan, Bukidnon 8714, Philippines (e-mail: zhangxuezhong2021phu@gmail.com).
Copyright @ 2023 the Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite this article: Zhang X, G Dapar ML, Zhang X, Chen Y. A pan- cancer analysis of the oncogenic role of YKT6 in human tumors. Medicine 2023;102:15(e33546).
Received: 21 January 2023 / Received in final form: 24 March 2023 / Accepted: 27 March 2023
http://dx.doi.org/10.1097/MD.0000000000033546
multiple posttranscriptional modifications that might regulate their function. Our research is focused on YKT6, as a SNARE protein with vesicle trafficking, was initially discovered in yeast.[6] It has been demonstrated that YKT6 is involved in trafficking of membrane vesicles both inside and outside of the Golgi.[7] Recently researches showed that YKT6 played an essential role in the development and progression of tumor. It has been found that YKT6 was required for exosome secretion and adversely impacted prognosis of non-small-cell lung cancer (NSCLC) patients.[8,9] Researchers also found that YKT6 was upregulated in breast cancer samples with an invasive pheno- type, promoting cell proliferation and conferring drug resis- tance.[10] Hepatocellular carcinoma (HCC) study has revealed that overexpressed expression of YKT6 was closely associated with prognosis of HCC patients.[11] Recent research about oral squamous cell carcinoma (OSCC) suggested that overexpression of YKT6 was related to cell invasion and metastasis, and low level of YKT6 was associated with CD8+ T cell infiltration.[12] Pancreatic cancer studies also found that YKT6 was significantly upregulated in pancreatic cancer cells.[13-15] However, studies of YKT6 have been confined to a few types of tumors, and its role in other tumors remains elusive.
In the study, we used TGCA project and GEO databases to investigate expression profiles of YKT6 across different types of cancer in a pan-cancer analysis. A number of factors, includ- ing gene expression, survival prognosis, genetic alteration, DNA methylation, immune infiltration, and gene enrichment analysis, were considered to explore the potential molecular mechanism by which YKT6 was involved in the pathogenesis or clinical prognosis of cancer.
2. Materials and methods
2.1. Gene expression analysis
The tumor immune estimation resource, vision 2 (TIMER2, web: http://timer.cistrome.org/) was used to analyze the differ- ential expression of YKT6 between tumor and normal tissues in TCGA tumors. Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2, web: http://gepia2.cancer-pku.cn/#analysis) was used to obtain box plots of Genotype-Tissue Expression (GTEx) database. The log2FC (fold change) cutoff was set 1, and a P value cutoff was .01. GEPIA2 tool was used to acquire the violin plots of YKT6 expression in different types of patholog- ical stages of TCGA tumors. The UALCAN online toll (https:// ualcan.path.uab.edu/analysis.html) was used to analyze cancer Omics data.[16] The protein expression analysis dataset was got from clinical proteomic tumor analysis consortium (CPTAC) dataset by using UCLCAN.
2.2. Survival prognosis analysis
Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2, web: http://gepia2.cancer-pku.cn/#analysis) was used to analyze overall survival (OS) and disease-free survival (DFS) significance map data (setting Group cutoff = median) of YKT6 gene in all TCGA tumors. Log-Rank method was used for sta- tistical hypothesis test.
2.3. Genetic alteration analysis
The cBioPortal tool (http://www.cbioportal.org/) was used to obtain genetic alteration characteristics of YKT6.[17] Genetic alteration characteristics including alteration frequency, muta- tion type, mutated site information, copy number alteration and three-dimensional (3D) structure of protein were collected. Survival data including overall, progression-free, disease-free and disease-free survival were obtained in TCGA tumors with or without YKT6 genetic alteration with “Comparison.” We
went further detecting the relationship between YKT6 and tumor mutational burden (TMB) and microsatellite instability (MSI) with tumors in TCGA by using R package and R lan- guage software.
2.4. YKT6 methylation analysis
The level of methylation of YKT6 in different tumors was obtained from TCGA dataset by using UCLCAN.[16] And mul- tiple probes of YKT6 associated with DNA methylation in diverse types of tumor of TCGA were detected by the method of R package and R language software.
2.5. Immune infiltration analysis
We then used TIMER2 online tool to acquire the data of association between YKT6 level and immune infiltration. The gene of YKT6 was entered into “gene name field” for analy- sis. The results of the selected immune cells were performed by the method of a heatmap and scatter plots. The TIMER algo- rithms, EPIC, XCELL, MCPCOUNTER, CIBERSORT-ABS, QUANTISEQ and CIBERSORT were used to evaluate the degree of immune infiltrating situation.[18]
2.6. YKT6-related gene enrichment analysis
The STRING online tool (https://cn.string-db.org/) was used to detect the proteins which bind to YKT6. The following main parameters were set: score for minimum interaction required [“low confidence (0.150)”], maximum number of interactors shown (“no more than 50”), examples of network edges (“evi- dence”) and active interactions (“experiments”). Finally, the available experimentally determined YKT6-binding proteins were detected.
Based on the datasets of all TCGA tumors and normal tissues, we identified the top 100 YKT6-correlated targeting genes by using GEPIA2 platform. Analysis of gene Pearson correlation pairwise was conducted between YKT6 and genes which were selected. The mean of log2 TPM was used to calculate the dot plot. The heatmap data was presented using TIMER2’s “Gene_ Corr” module. Spearman’s rank correlation was calculated using purity-adjusted test.
The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed by using DAVID website and the “ggplot2” R packages. The results of KEGG were shown as bubble chart. Finally, the gene ontology (GO) enrichment was analyzed by the method of “cluster Profiler” R package and R language software. As a results of GO, bar plots were produced.
3. Results
3.1. 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 Figure 1A, the expression of YKT6 in Bladder Urothelial Carcinoma (BLCA), Breast invasive carcinoma (BRCA), Cholangiocarcinoma (CHOL), Esophageal carci- noma (ESCA), Head and Neck squamous cell carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney renal papillary cell carcinoma (KIRP), Liver hepatocellular car- cinoma (LIHC), Lung adenocarcinoma (LUAD), Lung squa- mous cell carcinoma (LUSC), Prostate adenocarcinoma (PRAD), Stomach adenocarcinoma (STAD), Uterine Corpus Endometrial Carcinoma (UCEC) (P < . 001), colon adenocar- cinoma (COAD), glioblastoma multiforme (GBM) (P < . 01), Cervical squamous cell carcinoma and endocervical adeno- carcinoma (CESC) (P < . 05) was significantly overexpressed compared with the corresponding control tissues. However,
A
YKT6 Expression Level (log2 TPM)
*
**
**
**
8-
..
6-
-
4
1
2
ACC. Tumor (n=79)
BLCA. Tumor (n=408)
BLCA. Normal (n=19)
BRCA. Tumor (n=1093)
BRCA.Normal (n=112)
BRCA-Basal. Tumor (n=190)
BRCA-Her2. Tumor (n=82
BRCA-LumA. Tumor (n=564)
BRCA-LumB. Tumor (n=217)
CESC. Tumor (n=304)
CESC.Normal (n=3)
CHOL. Tumor (n=36)
CHOL.Normal (n=9)
COAD. Tumor (n=457)
COAD.Normal (n=41)
DLBC. Tumor (n=48)
ESCA. Tumor (n=184)
ESCA. Normal (n=11)
GBM. Tumor (n=153)
GBM.Normal (n=5)
HNSC. Tumor (n=520)
HNSC.Normal (n=44)
HNSC-HPV+. Tumor (n=97)
HNSC-HPV -. Tumor (n=421)
KICH. Tumor (n=66)
KICH.Normal (n=25)
KIRC. Tumor (n=533)
KIRC. Normal (n=72)
KIRP.Tumor (n=290)
KIRP.Normal (n=32
LAML. Tumor (n=173)
LGG. Tumor (n=516)
LIHC. Tumor (n=371)
LIHC. Normal (n=50)
LUAD. Tumor (n=515)
LUAD.Normal (n=59)
LUSC. Tumor (n=501
LUSC.Normal (n=51)
MESO. Tumor (n=87
OV.Tumor (n=303)
PAAD. Tumor (n=178)
PAAD. Normal (n=4)
PCPG. Tumor (n=179
PCPG. Normal (n=3)
PRAD. Tumor (n=497
PRAD.Normal (n=52)
READ. Tumor (n=166)
READ. Normal (n=10
SARC. Tumor (n=259)
SKCM. Tumor (n=103)
SKCM.Metastasis (n=368)
STAD. Tumor (n=415)
STAD.Normal (n=35)
TGCT.Tumor (n=150)
THCA. Tumor (n=501
THCA.Normal (n=59)
THYM. Tumor (n=120)
UCEC. Tumor (n=545)
UCEC.Normal (n=35)
UCS. Tumor (n=57)
UVM. Tumor (n=80).
B
The expression of YKT6
ns
ns
(TPM+1)
8
O
—.
+
…
1
Normal
-
1
-
. …
…
…
-
Tumor
-
-
-
…
. .
Log2
S
2
2
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
the expression of YKT6 was significantly downregulated in Kidney renal clear cell carcinoma (KIRC) and Thyroid carci- noma (THCA) (P <. 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 Figure 1B, YKT6 was significantly upregulated in Adrenocortical carci- noma (ACC) (P < . 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 cystadenocar- cinoma (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 < . 001). By contrast, the expres- sion of YKT6 was low expressed in Acute Myeloid Leukemia (LAML) and THCA (P < . 001). There were no statistical dif- ferences in 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 Figure 2A,
protein expression of YKT was significantly overexpressed in BRCA, COAD, GBM, HNSC, KIRC, LIHC, UCEC, and PAAD (P <. 001) compared with corresponding normal tissues.
Furthermore, we use GEPIA2 online tool to detect expres- sion of YKT6 in different tumor stages. The data of Figure 2B showed that there was a significant relation between level of YKT6 and the pathological stages of several tumors, including ACC (P = . 0213), BLCA (P = . 0354), COAD (P = . 0000102), KICH (P = . 000329), KIRC (P = . 0355), LIHC (P = . 00387), LUAD (P = . 00954), OV (P = . 0199), SKCM (P = . 00912), THCA (P =. 00562), UCS (P =. 0226).
3.2. Survival prognosis analysis
To evaluate the relationship between YKT6 and the progno- sis of patients with diverse kinds of cancer, the tumors were dichotomized into 2 groups (high-expression and low-ex- pression groups) based on the level of YKT6 in TCGA and GEO datasets. As data displayed in Figure 3A, we found that high-expression of YKT6 was positively correlated with poor prognosis of OS in different types of tumors including ACC (P = . 0029), BLCA (P = . 022), CESE (P = . 04), Head and
A
Protein expression of YKT6 (Z-value)
Breast cancer
Colon cancer
Clear cell RCC
UCEC
3
3.
3-
3.
2
2.
2.
2.
1.
1-
1.
1.
0.
0-
0-
0-
1
.1.
.1.
.1.
2
2.
2.
2.
3
Normal (n=18)
3.
Primary tumor (n=125)
Normal (n=100)
Primary tumor (n=97)
3
Normal (n=84)
Primary tumor (n=110)
3
Normal (n=31)
Primary tumor (n=100)
Protein expression of YKT6 (Z-value)
Head and neck squamous carcinoma
Pancreatic adencarcinoma
Glioblastoma multiforme
Hepatocellular carcinoma
3-
3-
4-
3-
2-
2.
3.
2.
1.
1-
2-
0-
1.
0-
1.
-1.
CON- 3
-1.
0-
0-
-1.
-1-
2.
2.
2.
4
Normal (n=71)
Primary tumor (n=108)
-3
Normal (n=74)
Primary tumor (n=137)
3
Normal (n= 10)
3.
Primary tumor (n=99)
Normal (n=165)
Primary tumor (n= 165)
B
8
YKT6 expression (TPM+1)
ACC
F value = 3.44 Pr(>F) = 0.0213
3 4 5 6 7 8 9
BLCA
F value = 3.37 Pr(>F) = 0.0354
8
COAD
F value = 7.3 Pr(>F) = 0.000102
4.5 5.05.56.06.57.0
KICH
F value = 5.08 Pr(>F) = 0.00329
6
~
5
6
log2
+
0
5
2
Stage | Stage II Stage III Stage IV
Stage II Stage III Stage IV
Stage | Stage II Stage III Stage IV
Stage | Stage II Stage III Stage IV
8
YKT6 expression log2 (TPM+1)
KIRC
F value = 2.88 Pr(>F) = 0.0355
2 3 4 5 6 7 8
LIHC
F value = 4.54 Pr(>F) = 0.00387
8
LUAD
F value = 3.86 Pr(>F) = 0.00954
F value = 3.95 Pr(>F) = 0.0199
8
OV
1
7
6
6
6
+
5
5
2
+
0
Stage | Stage II Stage IIIStage IV
Stage | Stage II Stage IIIStage IV
Stage | Stage II Stage IIIStage IV
Stage II Stage III Stage IV
8
YKT6 expression log2 (TPM+1)
9 8 5 6 7 89
SKCM
F value = 3.42 Pr(>F) = 0.00912
THCA
F value = 4.25 Pr(>F) = 0.00562
UCS
F value = 3.46
~
Pr(>F) = 0.0226
7
6
6
5
+
5
Stage | Stage II Stage IIIStage IV
Stage IStage IIStage IIIStage IV
Stage IStage II Stage III Stage IV
Neck squamous cell carcinoma (HNSC, P = . 00034), LGG (P = . 00025), LIHC (P = . 0013), LUAD (P = . 017), MESO (P = . 025), and UVM (P = . 00094). As shown in Figure 3B, high-expression of YKT6 was associated with poor DFS
prognosis for ACC (P = . 015), BLCA (P = . 0058), HNSC (P = .022), LGG (P = . 00012), LIHC (P = . 019), LUAD (P = . 019), MESO (P = . 013), PAAD (P = . 0014), PRAD (P = . 019), and UVM (P = . 005).
A
Overall Survival
log10(HR)
YKT6
☐
☐
☐
☐
☐
HNSC
KIRP LAML
MESO
PCPG
SARC
SKCM
TGCT
THYM
UCEC
☐
0.4
0.0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
KICH
KIRC
LGG
LIHC
LUAD
LUSC
OV
PAAD
PRAD
READ
STAD
THCA
UCS
UVM
-0.4
1.0
0.
1.0
1.0
1.0
Low YKTS Group
High /NTB Group
Y
Low YKTS Group
Low YKTG Group
Logrank p=0.0029
8
High YKT6 Group
1
LOW YKT6 Group
High YKT6 Group
High
LOW YKT6 Group
Logrank p=0.022
8
00
Legrank p=0.00034
00
High YKT6 Group
Logrank p=0.00094
Percent survival
.8
HR(high)=3.4
P(HR)=0.005
0
HR(high)=1.4
Logrank p=0.04 HR(high)=1.6
0
nihichi=38
0.6
P(HR)=0.023 n(high)=201
P(HR)-0.042 a(high)=146
HR(high)=1.6
P(HR)=0.00038
m(low)-201
n(high)=259 n(ow)=259
0
HR(high)=5.2
p(HR)=0.003
0.6
nglow)-30
6
0
n(low)=146
C
10
n(high)=39
n(ow)=39
0.4
4
V
-
O
0
0.2
~
N
~
ACC
BLCA
O
S
HNSC
0
0.0
0
CESC
0.0
UVM
3.
O
50
100
150
50
100
150
200
50
100
150
200
0
0
50
100
0
Months
150
0
Months
0
Months
Months
0
20
40
Months
60
80
1.0
1.0
0
1.0
Low YKT6 Group
Low YKT6 Group
High YKT6 Group
Low YKT6 Group
High YKT6
C
Low YKT6 Group
High YKT6 Group
i
High YKT6 Group
Percent survival
0º
Logrank p=0.00025
00
0
HR(high)=1.9
8
Logrank p=0.0013
HR(high)=1.8
8
Logrank p=0.017
Logrank p=0.025
0
0
HR(high)=1.4
0
HR(high)=1.7
p(HR)=0.00032
p(HR)=0.0015
P(HR)=0.017
p(HR)=0.026
0.6
n(high)=257
n[high)=182
n(low)=257
n(low)=182
00
n(high)=239
n(low)=239
0.6
n(high)=41
n(low)=41
0
0
”
+
+
0
0
0
0.2
N
0
~
2
LGG
LIHC
0
LUAD
0
MESO
0.0
0
0
0
0
0
0
50
100
150
200
0
20
40
60
80
100 120
0
50
100 150 200 250
0
20
40
60
80
Months
Months
Months
Months
B
Disease Free Survival
log10(HR)
YKT6
☐
☐
☐
☐
☐
0.3
0.0
ACC
BLCA
BRCA
CESC CHOL
COAD
DLBC
ESCA
GBM HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
-0.3
1.0
LOW YKT6 GrOUD HIgh YKTO Group
1.0
1.0
S
1.0
Low YKT6 Group
Low YKT6 Group
High YKT6 Group
Low YKT6
High YKTG Group
Low YKTG Group
Percent survival
Logrank p=0.015
HEY P(HR)=0.018
CO 0
High YKTO Group
Logrank p=0.0058
Logrank DRO 00012
Horses me0 010
HR(high)=1.8
HR(high)=1.8
Logrank p=0,019
HR(high)=1.6
HRchiche1
Loprank 000.00012
n(high)=38
P(HR)=0.0062 n(high)=201
0.8
0.8
P(HR)=0.00015
P(HR)=0.00015
HR(high)=1.4
n(high)=257 n@low)=257
n(high)=257 n(low)=257
P(HR)=0.019
n(ow)=38
00
(flow)“201
0.6
O
0.6
n(high)=182
n(low)=182
1
1.4
4
0.4
0.2
0.2
0.2
O
-
0.2
0
ACC
0.0
BLCA
0.0
HNSC
LGG
0.0
LIHC
0
0
50
100
Months
150
0
50
100
150
0
50
100
150
Months
Months
0
50
100
150
Months
0
20
40
60
80
100 120
Months
Percent survival
.0
Disease Free Survival
1.0
1.0
High YKT6 Group
Low YKTG
Low YKTG Group High YKTO
1.0
1.0
Low YKT6 Group
High YKT6 Group
Low YKT6 Group
Low YKTB Group
0.8
Logrank p=0,019
Logrank p=0.013
Logrank p=0.013
High YKT6 Group
Logrank p=0 019
.
High YKT6 Group
Logrank p=0.005
HR(high)=1.4
8
P(HR)=0.02
0
Hit(high)=2.1
8
8
Đ(HR)=0.014
0
HR(high)=2.1
P(HR)=0.014
0
HR(high)=1.7
0
HR(high)=3.9
0.6
n(high)=239
n(high)=41 n(ow)=41
6
n(high)=41 n(low)=41
P(HR)=0.021 which)#246
P(HR)=0.0092
6
m(w)#245
10
(high)=39
nílow)=239
0
0
0
0
n(ow)=39
V 1
0.4
V
V
0
0
0
0.2
0.2
0.2
N
2
LUAD
0
0
0.0
0
0.0
PAAD
0
PRAD
0
UVM
D
MESO
60
0
0
0
50
100
50
200
250
0
20
40
80
Months
0
20
40
60
80
0
50
100
150
0
20
40
60
80
Months
Months
Months
Months
A
Mutation
Structural Variant
☒ Amplification
☒ Deep Deletion
B
Alteration Frequency
4%-
3%-
R163*
2%-
1%-
Structural variant data
Mutation data
CNA data
Adrenocortical Carcinoma
Diffuse Large B-Cell Lymphoma Esophageal Adenocarcinoma
Uterine Corpus Endometrial Carcinoma
Stomach Adenocarcinoma
Sarcoma
Lung Adenocarcinoma
Lung Squamous Cell Carcinoma
Uterine Carcinosarcoma
Skin Cutaneous Melanoma
Testicular Germ Cell Tumors
Head and Neck Squamous Cell Carcinoma
Bladder Urothelial Carcinoma
Glioblastoma Multiforme
Pheochromocytoma and Paraganglioma
Breast Invasive Carcinoma
Cervical Squamous Cell Carcinoma
Brain Lower Grade Glioma Colorectal Adenocarcinoma
Pancreatic Adenocarcinoma
Ovarian Serous Cystadenocarcinoma
Prostate Adenocarcinoma
Liver Hepatocellular Carcinoma
C
19
Missense
# YKT6 Mutations
5
10
Truncating
0
Inframe
2
Splice
1
SV/Fusion
R163*
0
Longin
Synaptobrevin
0
100
198aa
D
Overall Survival
Disease-specific Survival
Progress Free Survival
Disease Free Survival
100%
Altered group
Unaltered group
100%
Altered group
100%
Altered group
Altered group
Percent Survival
90%
90%
Unaltered group
90%
Unaltered group
100%
80%
80%
80%
90%
Unaltered group
70%
70%
70%
80%
70%
60%
60%
Logrank Test P-Value: 0.658
60%
60%
Logrank Test P-Value: 0.972
50%
50%
50%
Logrank Test P-Value: 0.914
50%
40%
30%
Logrank Test P-Value: 0.649
40%
40%
30%
30%
40%
30%
Q 20%
UCEC
20%
20%
10%
UCEC
10%
UCEC
20%
10%
0%
10%
UCEC
0%
0%
0
40
80
120
160
200
0
40
80
120
160
200
0
40
80
120
160
200
0%
0
40
80
160
Months
200
Months
Months
120
Months
3.3. Genetic alteration analysis
The genetic alteration of YKT6 in different types of tumors from TCGA was analyzed. As data described in Figure 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 Figure 4B. No predominant genetic
alterations were obtained. Locations of genetic alterations appeared to be sporadic. For example, a truncating muta- tion, R163* alteration, within the Synaptobrevin domain, was only found in 2 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 = . 495), DFS (P = . 304), progression-free survival (PFS) (P = . 125), and disease-specific (DS) (P = . 268) were no significant difference between YKT6 altered group and unal- tered group (Fig. 4D).
A MSI
DLBC(N=47)
pValue
COAD(N=285)
0.0
COADREAD(N=374)
0.2
UCS(N=57)
THCA(N=493)
0.4
GBMLGG(N=657)
STES(N=592)
- 0.6
PRAD(N=495)
0.8
HNSC(N=500)
STAD(N=412)
1.0
UCEC(N=180)
ESCA(N=180)
LGG(N=506)
BRCA(N=1039)
THYM(N=118)
READ(N=89)
PCPG(N=177)
OV(N=303)
BLCA(N=407)
LAML(N=129)
LUAD(N=511)
KIRP(N=285)
PAAD(N=176)
KICH(N=66)
ACC(N=77)
LUSC(N=490)
LIHC(N=367)
KIRC(N=337)
TGCT(N=148)
UVM(N=79)
CESC(N=302)
GBM(N=151)
SKCM(N=102)
MESO(N=83)
CHOL(N=36)
KIPAN(N=688)
SARC(N=252)
-0.2
0.0
0.2
B
Correlation coefficient(pearson)
TMB
CHOL(N=36)
pValue
UVM(N=79)
0.0
HNSC(N=498)
0.2
UCS(N=57)
THCA(N=489)
0.4
COAD(N=282)
LAML(N=126)
0.6
COADREAD(N=372)
0.8
GBM(N=149)
STES(N=589)
1.0
DLBC(N=37)
LUSC(N=486)
THYM(N=118)
KIRP(N=279)
SKCM(N=102)
STAD(N=409)
BLCA(N=407)
ESCA(N=180)
PAAD(N=171)
GBMLGG(N=650)
PRAD(N=492)
BRCA(N=981)
TGCT(N=143)
KIPAN(N=679)
LGG(N=501)
PCPG(N=177)
OV(N=303)
LIHC(N=357)
READ(N=90)
CESC(N=286)
KICH(N=66)
KIRC(N=334)
UCEC(N=175)
MESO(N=82)
SARC(N=234)
ACC(N=77)
LUAD(N=509)
-0.2
-0.1
0.0
0.1
0.2
Correlation coefficient(pearson)
Moreover, we detected the relationship between YKT6 and TMB and MSI with tumors by using TCGA. As described in Figure 5A, we obtained that the expression of YKT6 was pos- itively associated with TMB in LUAD (P = 4.92e-8), SARC (P = . 00062), KIRC (P = . 043) and ACC (P = . 046). While, the expression of YKT6 was negative association with TMB in HNSC (P = . 013) and THCA (P = . 046). As described in Figure 5B, we also explored that YKT6 expression was pos- itively correlated with MSI in GBM (P = . 022), CESC (P = .0017), LUAD (P = . 043), SARC (P = . 000016), KIPAN (P = 3.69e-9), KIRC (P = . 015), LUSC (P = . 0082), and LIHC (P = . 012). By contrast, the expression of YKT6 was negatively correlated with MSI in GBMLGG (P = . 0044), COAD (P = .00017), COADREAD (P = . 00010), STES (P = . 031), THCA (P = . 0033), and DLBC (P = . 016). These results suggested the genetic alteration of YKT6 may be considered as potential novel drivers of some tumors.
3.4. DNA methylation analysis
DNA methylation, as an important epigenetic regulator of postreplication, played a significant role in tumorigenesis.[19] 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 Figure 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
Promoter methylation level of YKT6 (Beta value)
BLCA
HNSC
KIRC
KIRP
0.1
0.1
0.09-
p<0.001
0.09.
p<0.001
0.09
p<0.001
0.09
p<0.001
0.08-
0.08.
0.08
0.08
0.07-
0.07.
0.07
0.07
0.06-
0.06
0.06
0.05-
0.06.
0.04-
0.05.
0.05
0.05
0.03-
0.04.
0.04
0.04-
0.02
0.03
Normal (n=21)
Primary tumor (n=418)
Normal (n=50)
Primary tumor (n=528)
0.03
Normal (n=160)
Primary tumor (n=324)
0.03
Normal (n=45)
Primary tumor (n=275)
Promoter methylation level of YKT6 (Beta value)
LUAD
LUSC
PAAD
PRAD
0.147
p<0.001
0.10-
p<0.001
0.1
0.1
0.09
p<0.001
0.09
p<0.001
0.12
0.08
0.08
0.08
0.07
0.10
0.06
0.07-
0.06-
0.06
0.05
0.08
0.04
0.05
0.04-
0.04
0.06
0.02
0.03
0.03
Normal (n=32)
Primary tumor (n=473)
Normal (n=42)
Primary tumor (n=370)
Normal (n=10)
Primary tumor (n=184)
0.02
0.06
Normal (n=50)
Primary tumor (n=502)
Promoter methylation level of YKT6 (Beta value)
TGCT
THCA
UCEC
0.08-
p<0.001
0.1
p<0.001
0.09
p<0.001
0.07
0.09
0.08
0.08
0.06
0.07
0.07
0.06-
0.05
0.06
0.05
0.05-
0.04
0.04
0.03
0.04-
0.03
Seminoma
Non- seminoma (n=69)
0.02
0.03
Normal (n=56)
Primary tumor (n= 507)
Normal (n=46)
Primary tumor (n=438)
(n=63)
HNSC
KICH
LIHC
LUAD
Methylation correlation of YKT6 (Betavalue)
0.18
Spearman
0.09-
Spearman
r = 0.103
q = 0.275
0.10-
Spearman
0.16
r = 0.129
Spearman
P = 0.013
r =- 0.130
0.14-
P = 0.021
P = 0.027
0.14
P= 0.006
cg14549774
cg14549774
0.08-
cg14549774
0.08-
cg15972949
0.12
0.10-
0.07-
0.06-
0.10-
0.06-
0.06-
0.08
0.02
0.04-
0.06
6
7
8
0.05
5
6
7
3
4
5
6
7
8
5
6
7
YKT6(Log2(TPM+1))
8
YKT6 (Log2(TPM+1))
YKT6(Log2(TPM+1))
YKT6(Log2(TPM+1))
LUSC
READ
READ
SKCM
Methylation correlation of YKT6 (Betavalue)
0.10
Spearman
0.09
r=0.232
Spearman
T= 0.213
0.08-
Spearman
r = 0.243
0.08
Spearman
P < 0.001
0.08-
P= 0.129
+0.08
P = 0.036
0.07-
P = 0.016
0.07-
P = 0.005
cg14549774
cg 14549774
₾0.07-
cg15972949
cg15972949
20.06-
20.06
¥0.06-
40.06-
0.05-
0.05
0.04-
0.05-
0.04-
0.04
0.03
YKT6(Log2(TPM+1))
7
8
9
0.04
5
YKT6(Log2(TPM+1))
6
7
8
0.03-
5
6
7
YKT6(Log2(TPM+1))
7
8
6
8
YKT6(Log2(TPM+1))
9
Methylation correlation of YKT6 (Betavalue)
SKCM
STAD
STAD
THCA
UCEC
0.10
Spearman
DE 0.262
0.4.
Spearman
0.08
I= 0.139
Spearman
I’= 0.196
0.18
Spearman
Spearmar
¥0.08
P< 0.001
P < 0.001
r= 0.140
0.09
₹ 0.134
cg14549774
cg14549774
P = 0.011
cg18412613
0.07-
0.3
৳0.06
90.14
g15972949
P = 0.002
cg 14549774
0.08
P = 0.005
৳0.07
420.06
0.2
0.05
0.04
90.10
0.06
0.1
0.03
80.06
90.05-
0.04
0.0
0.02
0.04
6
7
8
9
5
6
7
5
6
7
0.02
4
5
6
7
0.03-
4
5
6
7
8
9
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.
3.5. Immune infiltration analysis
Tumor-infiltrating immune cells, as an integral part of tumor microenvironment (TME), played a crucial role in tumor progression and development.[20,21] We then used the TIMER, CIBERSORT, CIBERSORT-ABS, TIDE, XCEL, MCPCOUNTER, QUANTISEQ, and EPIC algorithms to detect the possible association between the different immune infil- tration 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 B).
Furthermore, as data shown in Figure 8C and D, we discov- ered 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.
3.6. 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 Figure 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 Figure 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 < . 001). Heatmap data showed that YKT6 gene had a significant positive association with the above 5 genes in the majority of tumors (Fig. 9C).
And then, we used KEGG pathway and GO enrichment anal- yses to explore the functions of YKT6. We found that “Syntaxin binding,” “SNARE complex,” “vesicle fusion” and “DNA rep- lication” may be involved in the influence of YKT6 on tumor pathogenesis (Fig. 9D and E).
A
Cancer associated fibroblast
UVM (n=80)
UCS (n=57)
UCEC (n=545)
THYM (n=120)
THCA (n=509)
TGCT (n=150)
STAD (n=415)
SKCM-Primary (n=103)
SKCM-Metastasis (n=368)
SKCM (n=471)
SARC (n=260)
READ (n=166)
PRAD (n=498)
PCPG (n=181)
PAAD (n=179)
OV (n=303)
MESO (n=87)
LUSC (n=501)
LUAD (n=515)
LIHC (n=371)
GG (n=516)
KIRP (n=290)
KIRC (n=533)
KICH (n=66)
HNSC-HPV+ (n=98)
HNSC-HPV- (n=422)
HNSC (n=522)
GBM (n=153)
ESCA (n=185)
DLBC (n=48)
[COAD (n=458)
CHOL (n=36)
CESC (n=306)
BRCA-LumB (n=219)
BRCA-LumA (n=568)
BRCA-Her2 (n=82)
BRCA-Basal (n=191)
BRCA (n=1100)
BLCA (n=408)
ACC (n=79)
Partial_Cor
☒
p >0.05
1
p … 0.05
0
-1
Cancer associated fibroblast_EPIC
Cancer associated fibroblast_MCPCOUNTER
Cancer associated fibroblast XCELL
3
Cancer associated fibroblast_TIDE
YKT6 Expression Level (log2 TPM)
YKT6 Expression Level (log2 TPM)
Purity
ancer associated fibroblast_MCPCC
Purity
ancer associated fibroblast_MCPCC
Rho = - 0.106
p = 3.30e-02
.
Rho = 0.232
p = 1.04e-04
Rho --- 0.068
p = 1.380-01
.
Rho = 0.235
p = 2.11e-07
N
?
COAD
4
LGG
6
0
0.25
0.50
0.75
1.000
10000 20000 30000 40000
0.25
0.50
0.75
1.000
2000
4000
Purity
Infiltration Level
Purity
Infiltration Level
C
SKCM-Metastasis (n=368)
Endothelial cell
UVM (n=80) UCS (n=57)
UCEC (n=545)
THYM (n=120)
THCA (n=509)
TGCT (n=150)
STAD (n=415)
SKCM-Primary (n=103)
SKCM (n=471)
SARC (n=260)
READ (n=166)
PRAD (n=498)
PCPG (n=181)
PAAD (n=179)
Partial_Cor
OV (n=303)
MESO (n=87)
LUSC (n=501)
LUAD (n=515)
LIHC (n=371)
LGG (n=516)
KIRP (n=290)
KIRC (n=533)
KICH (n=66)
HNSC-HPV+ (n=98)
HNSC-HPV- (n=422)
HNSC (n=522)
GBM (n=153)
ESCA (n=185)
DLBC (n=48)
COAD (n=458)
CHOL (n=36)
CESC (n=306)
BRCA-LumB (n=219)
BRCA-LumA (n=568)
BRCA-Her2 (n=82)
BRCA-Basal (n=191)
BRCA (n=1100)
BLCA (n=408)
ACC (n=79)
☒
p>0.05
1
p … 0.05
0
-1
Endothelial cell_EPIC
D
X
[X]
X
Endothelial cell_MCPCOUNTER Endothelial cell_XCELL
X
YKT6 Expression Level (log2 TPM)
YKT6 Expression Level (log2 TPM)
Purity
Endothelial cell_EPIC
Purity
Endothelial cell_EPIC
Rho = - 0.106
p = 3.30e-02
Rho = 0.266
p = 7.59e-06
Rho =- 0.037
Rho = 0.254
N
00
COAD
HNSC-HPV+
p = 7.298-01
p = 1.658-02
40
6-
a
0.25
0.50
1.00
0.000 0.025 0.050 0.075 0.100
0.00 0.02 0.04 0.06 0.08
Purity
0.75
Infiltration Level
0.25
0.50
Purity
0.75
Infiltration Level
YKT6 Expression Level (log2 TPM)
YKT6 Expression Level (log2 TPM)
Purity
Endothelial cell_EPIC
Purity
Endothelial cell_EPIC
1
Rho - - 0.142
p = 2.18e-03
Rno = - 0.198
p = 1.80€-05
Rho .: - 0.059
Rho - 0.318
1
p = 3.51e-01
p = 2.84e-07
00
KIRC
0
5
Si
4-
0:25
0.50
0.75
1.000.0
0.2
0.4
0.6
0.4
0.6
0.8
1.00.00
0.02
0.04
0.06
Purity
Infiltration Level
Purity
Infiltration Level
YKT6 Expression Level (log2 TPM)
YKT6 Expression Level (log2 TPM)
Purity
Endothelial cell_EPIC
Purity
Endothelial cell_EPIC
Rho = 0.009
Rh98 P.268
Rho - 0.02
Rho = 0.342
p = 9.14e-01
p = 6.53e-01
p = 7.10e-15
7-
READ
6
THCA
CO
5
:
4-
0:25
0.50
0.75
10.00
0.05
0.10
0.00
0.25
0.50
0.75
1
0.0
0.1
0.2
0.3
0.4
Purity
Infiltration Level
Purity
Infiltration Level
A
B
HSPA9
p-value = 0
-
p-value = 0
R = 0.61
R = 0.6
NSF
PTART
0
KRAS
S& VTIJA
log2(FTSJ2 TPM)
UFC1
log2(RALA TPM)
.
STX4
- RAB64
VPS45
៛
.
USOTRAB6C
TSNARESTX3
STX1B
USE1
SP000
SEUPU BESTXE
STX11
SIX
RFK
N
N
SEC SEGZZA
V RAB6B
GOSR2
NAPA
RAB4
1
PCMT1
STX12
STX10
VPS334
RABGGTB
0
-
0
¥
NRAS
VPS39
YKT6
STX8
0
2
4
6
8
0
2
4
6
8
log2(YKT6 TPM)
log2(YKT6 TPM)
STX16
-
p-value = 0
우
STX17 SEC22C
NARE VPS33B
HNRNPH1
R = 0.63
p-value = 0
R = 0.54
p=value = 0
R = 0.58
-
.
GFBRAR18
log2(ABCF2 TPM)
.
·
BET
log2(POLD2 TPM)
log2(EIF3B TPM)
+
-
·
VTI1B
-
.
A
-
.
-
%
0
.
”
·
0
%
.
.
8
0
2
4
4
*
·
2
4
.
&
C
log2(YKT6 TPM)
log2(YKT6 TPM)
log2(YKT6 TPM)
SKCM-Metastasis (n=368)
Spearman_Cor
SKCM-Primary (n=103)
p > 0.05
1
p … 0.05
0
1
UVM (n=80)
UCS (n=57)
UCEC (n=545)
THYM (n=120)
THCA (n=509)
TGCT (n=150)
STAD (n=415)
SKCM (n=471)
SARC (n=260)
READ (n=166)
PRAD (n=498)
PCPG (n=181)
PAAD (n=179)
OV (n=303)
MESO (n=87)
LUSC (n=501)
LUAD (n=515)
LIHC (n=371)
LGG (n=516)
KIRP (n=290)
KIRC (n=533)
KICH (n=66)
HNSC-HPV+ (n=98)
HNSC-HPV- (n=422)
HNSC (n=522)
GBM (n=153)
ESCA (n=185)
DLBC (n=48)
COAD (n=458)
CHOL (n=36)
CESC (n=306)
BRCA-LumB (n=219)
BRCA-LumA (n=568)
BRCA-Her2 (n=82)
BRCA-Basal (n=191)
BRCA (n=1100)
BLCA (n=408)
ACC (n=79)
ABCF2
EIF3B
FTSJ2
POLD2
X
RALA
D
E
SNARE interactions in vesicular transport
Proteasome
DNA replication-
exocytic vesicle vesicle docking
SNARE binding-
p.adjust
0.015
syntaxin binding
DNA replication
transport vesicle
Counts
SNAP receptor activity-
0.010
SNARE binding
4
0.005
SNAP receptor activity
17
syntaxin binding-
vesicle fusi@NARE complex
30
SNARE complex-
Counts
membrane fusion
4
SNARE interactions in vesicular transport
transport vesicle
17
exocytic vesicle
30
membrane fusion
vesicle fusion-
Proteasome
vesicle docking
T
KEGG
0.05
10
0. .15
0.20
T
0.25
GO
GeneRatio
4. Discussion
Previous researches have shown that YKT6 has emerged as a critical protein implicated in multitudes of trafficking events.[2,22] Recently studies have also revealed that YKT6 has been involved in the progression of several cancers, including NSCLC, HCC, PAAD, BRCA, and OSCC.[8-13] It remains to be answered that whether YKT6 can play an important role in the progression of different types of cancers via certain common molecular mechanisms.
In our study, YKT6 was significantly elevated in majority of tumors through TCGA and GEO databases, which is not in accordance with the altered protein levels in KIRC in the CPTAC dataset. This suggested that over-expressed RNA expressions of YKT6 may be usual, but it could not reflect actual protein level or response to certain types of cancer. In addition, we detected that upregulation of YKT6 commonly predicted poor OS and DFS for patients with tumors expressing over-expression of YKT6, such as ACC, BLCA, HNSC, LGG, LIHC, and so on. These findings reveal that YKT6 is a potential biomarker for the prognosis of patients with tumors. Yang et al[9] recently found that the high level of YKT6 related with worse prognoses in patients with OSCC which is the most common subtype of Head and Neck squamous cell carcinoma (HNSC). Similarly, we found that high YKT6 expression was related to poor OS (P = . 00034) and DFS (P = . 022) in HNSC, which is in agreement with Yang’s recently report. YKT6 was also reported to have a detrimental effect on survival for NSCLC patients. Consistent with the finding, our results revealed a relation between high level of YKT6 and poor OS (P = . 017) and DFS (P = . 019) in patients with LUAD. Xu et al[11] reported that over-expression of YTK6 was correlated with worse prognosis in patients with hepatocellular carcinoma. Similarly, we observed that high level of YKT6 was positive association with poor prognosis OS (P = . 0013) and DFS (P = . 019) in LIHC, which is in accordance with Xu’s report. Consequently, KT6 can play an important role in clinical prognosis of patients with tumors based on the clin- ical big data evidence. Many studies have shown that clinical survival prognosis was often associated with genetic alterations related to cancer progression.[23,24] While, there is no potential relationship between YKT6 genetic alteration and clinical sur- vival prognosis including OS (P = . 495), DFS (P = . 304), PFS (P = . 125), and DS (P = . 268) in UCEC patients.
Recently, researchers have become increasingly interested in exploring how YKT6 played a role in tumor. Throughout the genome, microsatellites are short stretches of DNA that are repeated, and MSI occurs when one or more repeats are added or removed.[25] TMB is the total amount of mutations per DNA megabase.[26]Both TMB and MSI are 2 new biomarkers correlated with immunotherapy responses.[27] In our present study, we firstly demonstrated the potential relationship between the level of YKT6 and TMB or MSI. We detected the significant relation- ship between YKT6 of several tumors and TMB and MSI with tumors in TCGA. Furthermore, we conducted a series of enrich- ment analyses using information on YKT6-binding components and YKT6 level-related genes through all tumors. We obtained the possible impact of “Syntaxin binding,” “SNARE complex,” “vesicle fusion” and “DNA replication” in the tumorigensis. Our study showed that YKT6 expression was positively related to cancer-associated fibroblasts for TCGA tumors of COAD and LGG. Furthermore, our findings demonstrated a significantly pos- itively association between expression of YKT6 and endothelial cell in tumors of COAD, HNSC-HPV+, OV, READ and THCA. Meanwhile, a statistically negative relationship was obtained between YKT6 expression and endothelial cell in KIRC.
Methylation of DNA plays a crucial role in tumor develop- ment. Up-or down-regulation of DNA methylation levels can influence the expression of tumor gene, which then affects tum- origenesis and development.[28,29] We found a potential associa- tion between DNA methylation and YKT6 gene. The difference
of YKT6 methylation between tumor tissues and matched nor- mal tissues has distinct results at different methylation sites. There remains a need for further evidence of how YKT6 DNA methylation might contribute to tumorigenesis.
In summary, our pan-cancer analysis of YKT6 revealed statis- tical association of YKT6 level with survival prognosis, genetic alteration, DNA methylation and immune infiltration across most tumors, which can contribute to understanding the role of YKT6 in tumoeigenesis from perspective of clinical tumor samples.
Author contributions
Conceptualization: Xuezhong Zhang.
Data curation: Xuezhong Zhang, Mark Lloyd G. Dapar, Xin Zhang, Yingjun Chen.
Methodology: Xuezhong Zhang.
Resources: Xuezhong Zhang, Yingjun Chen.
Software: Xuezhong Zhang, Xin Zhang.
Validation: Mark Lloyd G. Dapar.
Writing - original draft: Xuezhong Zhang.
Writing - review & editing: Mark Lloyd G. Dapar.
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