The role of YTH domain containing 2 in epigenetic modification and immune infiltration of pan-cancer
Chiyuan Zhang1 | Cuishan Guo1 | Yan Li1 | Ling Ouyang1 | Qi Zhao2 | Kuiran Liu1
1Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
2School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China
Correspondence
Qi Zhao, School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China.
Email: zhaoqi@lnu.edu.cn
Kuiran Liu, Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110004, China.
Email: liukr@sj-hospital.org
Funding information
This work was supported by the Scientific research funding project of Liaoning Provincial Department of Science and Technology under Grant No. 2020JH2/10300050
Abstract
YTH domain containing 2 (YTHDC2) is the largest N6-Methyladenosine (mºA) binding protein of the YTH protein family and the only member containing ATP-dependent RNA helicase activity. For further analysing its biological role in epigenetic modifica- tion, we comprehensively explored YTHDC2 from gene expression, genetic alteration, protein-protein interaction (PPI) network, immune infiltration, diagnostic value and prognostic value in pan-cancer, using a series of databases and bioinformatic tools. We found that YTHDC2 with Missense mutation could cause a different prognosis in uterine corpus endometrial carcinoma (UCEC), and its different methylation level could lead to a totally various prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), lung squamous cell carcinoma (LUSC) and UCEC. The main molecular mechanisms of YTHDC2 fo- cused on catalytic activity, helicase activity, snRNA binding, spliceosome and mRNA surveillance. Additionally, YTHDC2 was notably correlated with tumour immune infil- tration. Moreover, YTHDC2 had a high diagnostic value for seven cancer types and a prognostic value for brain lower grade glioma (LGG), rectum adenocarcinoma (READ) and skin cutaneous melanoma (SKCM). Collectively, YTHDC2 plays a significant role in epigenetic modification and immune infiltration and maybe a potential biomarker for diagnosis and prognosis in certain cancers.
KEYWORDS
diagnosis, immune infiltration, m6A, prognosis, YTHDC2
|
1 INTRODUCTION
In recent years, as the most abundant mRNA modification in eu- karyotic cells,1 m6A RNA modification is widely recognized as an es- sential epigenetic modification in many biological processes by the dynamic adjustment of gene expression and receives considerable attention. It is well established that mºA modification is crucial for various pathological processes, including cancer initiation and pro- gression.2-6 Although the dedicated methyltransferases (writers)
and demethylases (erasers) regulate the reversibility of mºA modifi- cation, which does not alter the matching and coding of bases, m6A binding proteins (readers) are vital to the fate of mRNAs through specific recognizing mºA and binding function during various bio- processes, such as RNA splicing, export, translation and decay,1 widely affecting gene expression at different levels. Among diverse mºA binding proteins, the YTH protein family has been identified to play a critical role in mºA modification, including YTHDF1, YTHDF2, YTHDF3, YTHDC1 and YTHDC2. YTHDF1 contributes to enhance
@ 2021 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.
the initiation of translating and promote protein synthesis through interacting with elF3.7 YTHDF2 can mediate mºA-dependent mRNA decay.8 YTHDF3 takes an important part in the translation process of m6A-containing mRNAs through the sequential recruitment of other effectors.9 YTHDC1 regulates gene splicing and modulate the exportation of m6A-modified mRNA.2 YTHDC2 serves as the largest protein member (approximately 160 kD) in the YTH protein family. YTHDC2 is distinct from other YTH proteins because of its special helicase domains. YTHDC2 prefers to bind to the conserved m&A-modified motifs and executives the function of mºA reader by enhancing the translation efficiency and decreasing the mRNA abundance.10-13 Previous studies have shown that YTHDC2 has a crucial effect on cancer metastasis through increased translation efficiency of HIF-1« in colon cancer patients14 and contributes sig- nificantly in the proliferation of hepatocellular carcinoma cells.15 YTHDC2 expression has been identified to be associated with prog- nosis, apoptosis activation and ubiquitin-mediated proteolysis in Head and Neck squamous cell carcinoma (HNSCC).16
However, although certain studies have been carried out on YTHDC2, no single study exists which could overall evaluate its ef- fects on considerable types of cancers. In light of increasing concern of pan-cancer genetic analysis, which will be beneficial to assess diag- nostic and clinical prognostic values of genes on the whole level, we explored YTHDC2 expression in pan-cancer to further identify the influence of YTHDC2 on tumour promotion and suppression. As a re- sult, we observed that YTHDC2 expressed significantly differently in cancers compared with normal tissues and was down-regulated in most tumours. We also found that YTHDC2 not only showed a high diagnos- tic value in predicting cancers containing cholangiocarcinoma (CHOL), LUSC, thyroid carcinoma (THCA), ovarian serous cystadenocarcinoma (OV), SKCM, testicular germ cell tumours (TGCT) and uterine carcino- sarcoma (UCS), but also displayed its prognostic value in LGG, READ and SKCM. Furthermore, YTHDC2 with genetic alteration was signifi- cantly correlated with the prognosis of UCEC, ACC, LUSC and CESC. In addition, YTHDC2 was identified to play an important role in tumour cell immune infiltration. The pan-cancer analysis may contribute to un- covering the distinct roles of YTHDC2 in the varying cancer promotion or suppression and provide evidence for more directional clinical and experimental research in individual cancers.
2 MATERIALS AND METHODS |
2.1 Gene expression analysis |
The RNA-seq data and relevant clinical data in level 3 transcripts per million reads (TPM) of 15,776 samples across 33 tumour types from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/; tcga_RSEM_gene_tpm) and the Genotype-Tissue Expression (GTEX) database (dataset ID: gtex_RSEM_gene_tpm) were downloaded by UCSC XENA (https://xenabrowser.net/datapages/). Then, the RNA- seq data in TPM format were converted into log2 format for expres- sion comparison between samples. R software v3.6.3 was used for
statistical analysis, and ggplot2 package was for visualization. The Wilcoxon rank sum test detected two sets of data, and p < 0.05 was considered statistically significant. (ns, p ≥ 0.05; * , p < 0.05; ** , p < 0.01; *** , p < 0.001).17 We also displayed the differential ex- pression of YTHDC2 between tumours and adjacent normal tissues using Tumor Immune Estimation Resource (TIMER).
2.2 Genetic alteration analysis |
Genomic profiles, including the alteration frequency, mutation type and Copy number alterations (CNA) across all TCGA tumours, were calculated by using the cBioPortal web (https://www.cbioportal. org/). Kaplan-Meier plots with log-rank p-value was generated by obtaining the data on the overall survival (OS), progression-free sur- vival (PFS), disease-free survival (DFS) and disease-specific survival (DSS) of UCEC cases with or without YTHDC2 genetic alteration.18,19 Additionally, we used Gene Set Cancer Analysis (GSCA; http://bioin fo.life.hust.edu.cn/),2º which is an integrated genomic and immunog- enomic web-based platform for gene set cancer research to assess the correlation between YTHDC2 methylation and prognosis in cancers.
2.3 PPI network analysis |
The online STRING (https://string-db.org/) tool provides investi- gators with systematic and comprehensive functional annotation tools for identifying the biological significance of an extensive list of genes. We obtained 50 YTHDC2-binding proteins by setting the fol- lowing main parameters: minimum required interaction score (‘me- dium confidence [0.400]’), meaning of network edges (‘evidence’), max number of interactors to show (‘no more than 50 interactors’ in 1st shell) and active interaction sources (‘Experiments, Text mining, Databases’). Then, Cytoscape (version 3.7.2) was applied for visuali- zation of PPI networks.
2.4 Functional and pathway enrichment analysis |
In our study, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted for YTHDC2-binding proteins using ggplot2 package for visualization and cluster Profiler package for statistical analysis.21,22
2.5 Distribution of YTHDC2 expression in molecular subtypes and immune subtypes of cancers |
TISIDB (cis.hku.hk/TISIDB/)23 is a web portal for tumour and im- mune system interaction, which integrates multiple heterogeneous data types. We explored the association between YTHDC2 expres- sion and molecular subtypes or immune subtypes across TCGA tu- mours from TISIDB database.
YTHDC2 Expression Level (log2 TPM)
5
A
N
0
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.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)
2
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)
A
log2(TPM+1)
6
N
A
0
Adipose Tissue(N=515)
Adrenal Gland(N=128)
Bladder(N=9)
I
I
Blood Vessel(N=606)
Blood(N=444)
Bone Marrow(N=70)
Brain(N=1152)
Breast(N=179)
Cervix Uteri(N=10)
Colon(N=308)
I
Esophagus(N=653)
L
Fallopian Tube(N=5)
Heart(N=377)
Kidney(N=28)
Liver(N=110)
Lung(N=288)
L
*
Muscle(N=396)
Nerve(N=278)
L
Ovary(N=88)
Pancreas(N=167)
Pituitary(N=107)
Prostate(N=100)
I
O
Salivary Gland(N=55)
Skin(N=812)
Small Intestine(N=92)
**
Spleen(N=100)
Stomach(N=174)
Testis(N=165)
Thyroid(N=279)
I
Uterus(N=78)
Vagina(N=85)
GTEX database as controls. * p < 0.05, ** p < 0.01, *** p < 0.001, the asterisk represents the degree of importance (*p). The significance of the expression in TCGA tumours and adjacent normal tissues. (C) YTHDC2 expression in TCGA tumours and normal tissues with the data of the
The expression of YTHDC2 Log2 (TPM+1)
two groups of samples passed the Wilcox test
0
FIGURE 1 Expression level of YTHDC2 gene in tumours and normal tissues. (A) YTHDC2 expression in normal tissues. (B) YTHDC2
5
9
00
A
0
>
2
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
ns
GBM
ns
HNSC
KICH
KIRC
ns
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
ns
PCPG
PRAD
READ
SARC
SKCM
ns
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
Tumor Normal
**
*
* *
KICH.Tumor (n=66)
|
WILEY
A
0
8%
47/531
Alteration Frequency
% of Samples with YTHDC2 Mutation
0.09
6%
22/406
4%
0.06
21/468
4/92
2/57
9/291
12/411
12/485
10/439
2%
0.03
3/150
9/509
9/517
3/185
1/66
5/370
5/408
1/82
4/365
1/92
2/211
2/239
1/140
2/282
1/178
2/400
5/1026
2/498
2/519
1/525
Mutation data
0.00
CNA data
Endometrial Carcinoma
Colorectal Adenocarcinoma
ewoutjory
Cervical Adenocarcinoma
Bladder Urothelial Carcinoma
Esophagogastric Adenocarcinoma
Adrenocortical Carcinoma
Cervical Squamous Cell Carcinoma
Sarcoma
Renal Clear Cell Carcinoma
Ovarian Epithelial Tumor
Non-Small Cell Lung Cancer
Prostate Adenocarcinoma
Pleural Mesothelioma
Head and Neck Squamous Cell Carcinoma
Mature 8-Cell Neoplasms
Hepatocellular Carcinoma
Renal Non-Clear Cell Carcinoma
Esophageal Squamous Cell Carcinoma
guayos
Invasive Breast Carcinoma
Pancreatic Adenocarcinoma
Glioblastoma
Dittuse Glioma
Cholangiocarcinoma
Fibrolamellar Carcinoma
Phaochromocytoma
Miscellaneous Neuroepithelial Tumor
Undifferentiated Stomach Adenocarcinoma
Seminoma
Non-Seminomatous Germ Cell Tumor
Well-Differentiated Thyroid Cancer
Thymic Epithelial Tumor
Ocular Melanoma
Encapsulated Glioma
UCEC
COAD
SKCM
HNSC-HPV+
UCs
CESC
BLCA
LUSC
STAD
READ
HNSC
LUAD
ESCA
KICH-
KIRC
HNSC-HPV-
MESO
LIHC
ACC
BRCA-LumB
SARC
LAML
KIRP
PAAD
GBM
BRCA
PRAD
BRCA-LumA
LGG
C
Mutation
Amplification
Deep Deletion
Multiple Alterations
E634K
# YTHDC2 Mutations
7
0
R3H
DEAD
Ank_2
Heli ..
HA2
OB_NT ..
YTH
0
200
400
600
800
1000
1200
1430aa
. Missense Mutations
Truncating Mutations: Nonsense, Nonstop. Frameshift deletion, Frameshift insertion, Splice site
. Inframe Mutations: Inframe deletion, Inframe insertion
210
Missense
58 Truncating
. Fusion Mutations
0
Inframe
0
Fusion
. Other Mutations: All other types of mutations
0
Other
D
Logrank Test P-Value: 8.7060-3
100%
90%
80%
70%
60%
Overall
50%
40%
30%
20%
10%
0%
0
20
40
60
80
100
120
140
160
180
200
220
Overall Survival (Months)
Overall
Altered group
Unaltered group
F
100%
Logrank Test P-Value: 0.0475
90%
80%
70%
Disease Free
60%
50%
40%
30%
20%
10%
0%
0
20
40
60
80
100
120
140
160
180
200
220
Disease Froo [Months)
Disease Free
Altered group
Unatered group
E
100%
Logrank Test P-Value: 2.990e-3
90%
80%
70%
Progression Free
60%
50%
40%
30%
20%
10%
0%
0
20
40
60
80
100
120
140
160
180
200
220
Progress Free Survival (Months)
Progression Free
Altered group
Unaltered group
G
100%
Logrank Test P-Value: 0.0143
90%
80%
70%
Disease-specific
60%
50%
40%
30%
20%
10%
0%
0
20
40
60
80
100
120
140
160
180
200
220
Months of disease-specific survival
Disease-specific
Altered group
Unaltered group
2.6 Immune infiltration analysis |
Tumor immune estimation resource24-26 is a comprehensive re- source for systematical analysis of immune infiltrates across diverse
cancer types. The abundances of six immune infiltrates (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells) are estimated by TIMER algorithm. We explored the associa- tion between YTHDC2 expression and immune infiltration across
TCGA tumours. Additionally, we used GSCA20 to assess the corre- lation between YTHDC2 expression and immune infiltration. Also, we used immunedeconv package for reliable immune score evalua- tion. Furthermore, we examined the correlation between YTHDC2 expression and immune checkpoint-related genes (SIGLEC15,27 IDO1,28 CD274,29 HAVCR2,30 PDCD1,31 CTLA4,26 LAG332 and PDCD1LG233) using R software. The horizontal axis represents the expression of immune checkpoint-related genes, and the vertical axis represents different tumour tissues. Different colours represent correlation coefficients, blue colour represents positive correlation, while red colour represents negative correlation, and the darker colour represents the stronger correlation (*p < 0.05, ** p < 0.01, *** p < 0.001).
2.7 Diagnostic value analysis |
The clinicopathological parameters of 33 tumour patients from TCGA database and the corresponding normal tissue data from GTEX da- tabase were extracted to assess the diagnostic value of YTHDC2 by receiver operating characteristic (ROC) curve using pROC package for analysis and ggplot2 package for visualization. Note: The area value under the ROC curve is between 0.5 and 1. The closer the area under the curve (AUC) is 1, the better the diagnostic effect is. AUC in 0.5-0.7 has a low accuracy, AUC in 0.7-0.9 has a certain accuracy, and AUC above 0.9 has a high accuracy.
2.8 Survival prognosis analysis
Kaplan-Meier plots were presented to assess the relationship between YTHDC2 expression level and prognosis for various cancer types, sur- vminer package was used for visualization, and survival package was used for statistical analysis of survival data. The Log-rank test was used in the hypothesis test, and p < 0.05 is considered statistically significant.
3 RESULTS
|
3.1 Analysis of YTHDC2 expression in cancers
We displayed YTHDC2 expression in normal tissues from GTEX data- base (Figure 1A), the different expression levels in TCGA tumours and adjacent normal tissues from TIMER (Figure 1B), and the distribution of YTHDC2 expression in tumours with the data of the GTEX database as controls (Figure 1C). The expression level of YTHDC2 was signifi- cantly upregulated in CHOL, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), kidney renal clear cell carcinoma (KIRC), acute my- eloid leukaemia (LAML), LGG, pancreatic adenocarcinoma (PAAD) and thymoma (THYM). On the contrary, the expression level of YTHDC2 was notably down-regulated in ACC, bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), CESC, colon adenocarci- noma (COAD), oesophageal carcinoma (ESCA), kidney chromophobe
(KICH), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), LUSC, OV, prostate adenocarcinoma (PRAD), rectum adeno- carcinoma (READ), SKCM, TGCT, THCA, UCEC, and UCS.
3.2 Genetic alteration analysis of YTHDC2 |
The results of the preliminary analysis of the genetic alteration status of YTHDC2 are presented in Figure 2. The results indicated that mutation as the highest alteration frequency (8.36%) occurred in patients with UCEC. Furthermore, samples of CESC with genetic alteration only had mutation of YTHDC2, with the alteration fre- quency showed 4.35% and 3.19% respectively. Besides, patients with ACC had the higher alteration frequency (2.2%) of amplifica- tion than all the other tumours, and all mature B-cell neoplasms cases with genetic alteration had deep deletion (2.08%) of YTHDC2 (Figure 2B). We also observed 268 mutations of YTHDC2 presented in Figure 2C, including types, sites and case numbers of mutations and found that the key gene alteration type was the missense muta- tion. In Figure 2D-G, we explored that the prognosis of UCEC pa- tients with YTHDC2 genetic alteration was better than the cases without YTHDC2 genetic alteration in PFS, DFS and DSS. However, patients with YTHDC2 hypermethylation had a worse OS than pa- tients with YTHDC2 hypomethylation in ACC, CESC, LUSC and UCEC (Figure 3).
3.3 Enrichment analysis of YTHDC2-related genes in cancers |
We screened out 50 targeted binding proteins of YTHDC2 using STRING’s website and showed the interaction network by cytoscape (Figure S1). Then, we conducted the GO and KEGG pathway enrich- ment analyses to further investigate the molecular mechanism of YTHDC2 (Figure 4, Figure S2). The results suggested that the biologi- cal process (BP) was primarily involved in the aspect of RNA Splicing, RNA3’-end processing, DNA-templated transcription termination and termination of RNA polymerase II transcription; the major as- pects of molecular function (MF) contained catalytic activity acting on RNA, helicase activity, and snRNA binding; the cellular compo- nent (CC) mainly included Spliceosomal Complex, Nuclear Speck, U2-Type Spliceosomal Complex, Catalytic Step 2 Spliceosome, U2-Type Catalytic Step 2 Spliceosome, and mRNA Cleavage Factor Complex. Additionally, KEGG enrichment analysis was mainly linked to the spliceosome and mRNA surveillance pathway.
3.4 Molecular subtypes and immune subtype’s analysis in cancers |
We used TISIDB database to show the correlation between YTHDC2 expression and molecular subtypes or immune subtypes across TCGA tumours. As shown in Figure S3, YTHDC2 expression
A
Survival difference between high and low methylation in each cancer
Hazard ratio
9
Symbol
5
YTHDC2
10
Logrank P value
· <0.05
0
>0.05
ACC
CESC
LUSC
UCEC
BLCA
BRCA
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCS
UVM
Cancer types
B OS survival of YTHDC2 methylation in ACC
C OS survival of YTHDC2 methylation in CESC
1.00
1.00
0.75
0.75
OS probability
OS probability
0.50
Hypermethylation, n=40
0.50
Hypermethylation, n=115
Hypomethylation, n=40
Hypomethylation, n=109
0.25
Logrank P value
=0.01
0.25
Logrank P value
0.02
0.00
0.00
0
1000
2000
3000
4000
0
1000
2000
3000
4000
5000
Time (days)
Time (days)
D OS survival of YTHDC2 methylation in LUSC
E
OS survival of YTHDC2 methylation in UCEC
1.00-
1.00
0.75
0.75
OS probability
OS probability
0.50
Hypermethylation, n=137
0.50
Hypermethylation, n=192
Hypomethylation, n=129
Hypomethylation, n=191
0.25
Logrank
0.25
value
Logrank P value = 0.03
0.00
0.00
0
1000
2000
3000
4000
5000
0
1000
2000
3000
4000
5000
6000
Time (days)
Time (days)
was significantly correlated with molecular subtypes in ACC, BRCA, COAD, HNSC, LGG, pheochromocytoma and paraganglioma (PCPG), PRAD, stomach adenocarcinoma (STAD) and UCEC.
A total of six immune subtype were compared with YTHDC2 expression across diverse cancers, including C1 (wound healing),
C2 (IFN-gamma dominant), C3 (inflammatory), C4 (lymphocyte de- pleted), C5 (immunologically quiet) and C6 (TGF-b dominant), and YTHDC2 was notably associated with immune subtypes of nine can- cer types, including BLCA, BRCA, KIRC, LIHC, LUAD, PAAD, SKCM, STAD and UCEC (Figure S4).
WILEY
BP
MF
A
B
RNA Splicing
Catalytic Activity, Acting on RNA
RNA Splicing, via
Transesterification
Helicase Activity
Reactions
mRNA Splicing, via Spliceosome
p.adjust
p.adjust
RNA Splicing, via
4e-10
RNA Helicase Activity
Transesterification
3e-10
0.009
Reactions with Bulged
2e-10
snRNA Binding
0.006
Adenosine as Nucleophile
1e-10
0.003
RNA 3’-End Processing
Exonuclease Activity
Counts
Counts
mRNA 3’-End Processing
☐ 6
DNA Helicase Activity
☐ 2
☐ 14
Exonuclease Activity,
☐ 10
DNA-Templated
☐
23
Active with Either Ribo-
Transcription,
Or Deoxyribonucleic
☐ 17
Termination
Termination of
Acids and Producing 5’-
RNA Polymerase II
Phosphomonoesters
Telomerase RNA Binding -
Transcription
mRNA Polyadenylation
U6 snRNA Binding
mRNA Cleavage
mRNA Methyltransferase
Activity
0.1
0.2
0.3
0.4
0.5
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
GeneRatio
GeneRatio
C
CC
D
KEGG
Spliceosomal Complex
Nuclear Speck
U2-Type Spliceosomal Complex
p.adjust
p.adjust
1.5e-07
Spliceosome
Catalytic Step 2 Spliceosome
1.0e-07
2.582861e-11
5.0e-08
U2-Type Catalytic Step 2 Spliceosome
Counts
Counts ☐ 9
mRNA Cleavage Factor
☐ 4
Complex
☐ 8
☐
10
U4/U6 x U5 Tri-snRNP
☐
11
☐
Complex
10
U4 snRNP .
mRNA Surveillance Pathway
Catalytic Step 1
Spliceosome
U2-Type Catalytic Step 1
Spliceosome
0.10
0.15
0.20
0.25
0.41
0.42
0.43
0.44
0.45
GeneRatio
GeneRatio
3.5 Immune infiltration analysis in cancers |
We used the TIMER to assess the correlation between YTHDC2 ex- pression and immune infiltration in cancers. The results showed that YTHDC2 expression was closely related to tumour purity in 17 types of cancers. Among them, YTHDC2 had negative associations with tumour purity in seven cancer types, including BRCA-Basal, READ, COAD, KIRC, HNSC-HPVneg, LUAD, together with OV. YTHDC2 ex- pression was significantly positively correlated with the infiltration levels of B Cell, CD8+ T cell, CD4+ T cell, macrophage, Neutrophil, and Dendritic Cell in BRCA-Basal, COAD, KIRC, and LUAD; with the
infiltration levels of CD8+ T cell, CD4+ T cell, macrophage, neutro- phil and dendritic cell in HNSC-HPVneg; with the infiltration levels of B cell, CD8+ T cell, neutrophil and dendritic cell in READ; with the infiltration levels of CD8+ T cell, neutrophil and dendritic cell in OV (Figure S5). Yet, YTHDC2 was positively associated with tumour purity in 10 cancer types, such as BRCA, SKCM, SKCM-primary, glio- blastoma multiforme (GBM), BRCA-Luminal, BLCA, Sarcoma (SARC), TGCT, LGG and ACC (Figure S6). Meanwhile, we performed a spear- man correlation analysis heat map of immune score and YTHDC2 gene expression in multiple tumour tissues (Figure S7). The re- sults showed that YTHDC2 expression was significantly positively
correlated with the infiltration levels of CD8+ T cell, CD4+ T cell, neutrophil, myeloid dendritic cell, macrophage and B cell in five types of cancer, containing COAD, KIRC, LIHC, LUAD and PAAD. Then, we examined the associations between YTHDC2 expression and im- mune infiltrates in GSCA (Figure S8). In COAD, YTHDC2 showed a positive correlation with the infiltration levels of mucosal associated invariant T cell (MAIT), T follicular helper cell (Tfh) and induced regula- tory T cell (iTreg), but a negative correlation with the infiltration levels of CD8 naïve T cell (CD8_native). In KIRC, YTHDC2 showed a posi- tive correlation with the infiltration levels of CD4 T cell (CD4_T), CD8 T cell (CD8_T), central memory T cell (Central_memory), cytotoxic T cell (Cytotoxic), dendritic cells (DC), exhausted T cell (Exhausted), Infiltration Score, macrophage, monocyte, Tfh, T helper type 1 (Th1), type 1 regulatory T cell (Tr1), iTreg and natural Treg (nTreg), but a negative correlation with the infiltration levels of CD4 naïve T cell (CD4_native), CD8_native, neutrophil, T helper type 17 (Th17) and T helper type 2 (Th2). In LIHC, YTHDC2 was positively correlated with the infiltration levels of CD4_T, CD4_native, central memory T cell (Central_memory) and iTreg, but negatively correlated with the infiltration levels of B cell, CD8_T, cytotoxic, effector memory T cell (Effector_memory), Gamma delta T cell (Gamma_delta), Infiltration Score and Natural killer T cell (NKT). In LUAD, YTHDC2 was posi- tively correlated with the infiltration levels of CD4_T, Central_mem- ory, Infiltration Score, Tfh, Tr1 and iTreg, but negatively correlated with the infiltration levels of Effector_memory, exhausted, neutrophil and nTreg. In PAAD, YTHDC2 was positively correlated with the in- filtration levels of CD4_T, Central_memory, cytotoxic, Gamma_delta, MAIT, NK, Tfh, Tr1 and iTreg, while negatively correlated with the in- filtration levels of macrophage, monocyte, neutrophil, Th17 and Th2.
We further explored the correlation between YTHDC2 and eight immune checkpoint-related genes and found that YTHDC2 was significantly associated with the expression of SIGLEC15, IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3 or PDCD1LG2 in almost all types of cancers except for CESC and TGCT (Figure 5).
3.6 Diagnostic value of YTHDC2 in cancers |
We performed the ROC curve analysis to evaluate the diagnostic value of YTHDC2. As shown in Figure 6, YTHDC2 had a certain ac- curacy in predicting normal and tumour outcomes in 15 types of tu- mours, including CHOL, COAD, LUSC, LUAD, PRAD, READ, THCA, UCEC, ACC, CESC, LAML, OV, SKCM, TGCT and UCS. It is worth noting that YTHDC2 had a high diagnostic value for seven types of tumours, including CHOL (AUC = 0.972), LUSC (AUC = 0.906), THCA (AUC = 0.947), OV (AUC = 0.999), SKCM (AUC = 0.908), TGCT (AUC = 0.996) and UCS (AUC = 0.996).
3.7 Survival prognosis of YTHDC2 in cancers
YTHDC2 expression was significantly correlated with the prognosis of LGG, READ and SKCM (Figure 7). For LGG, Cox regression results
showed that the difference in survival time distribution in group was statistically significant for OS (Hazard Ratio [HR] = 1.76, 95% Confidence Interval (CI): 1.25-2.48, p = 0.001], DSS (HR = 1.81, 95% CI: 1.26-2.60, p = 0.001) and progress-free interval (PFI; HR = 1.47, 95% CI: 1.12-1.93, p = 0.006), illustrating that the high expression level of YTHDC2 had a worse prognosis. In contrast, for READ, Cox regression results showed that the difference in survival time distribution in group was statistically significant for OS [HR = 0.29, 95% CI: 0.12-0.70, p = 0.006], DSS (HR = 0.11, 95% CI: 0.03-0.51, p = 0.005) and PFI (HR = 0.32, 95% CI: 0.16-0.65, p = 0.002), il- lustrating that the high expression level of YTHDC2 had a better prognosis. For SKCM, Cox regression results showed that the dif- ference in survival time distribution in group was significant for OS (HR = 0.74, 95% CI: 0.56-0.98, p = 0.035), DSS (HR = 0.73, 95% CI: 0.54-0.97, p = 0.033) and PFI (HR = 0.79, 95% CI: 0.63-0.99, p = 0.041), indicating that the high expression level of YTHDC2 had a better prognosis.
4 DISCUSSION |
YTHDC2 serves as not only the multi-domain mºA reader contain- ing the YTH domain, the R3H domain, the Helicase domain and the DUF1065 domain, but also the largest YTH domain-containing pro- tein owning 1430 amino acid sequences, especially possesses ATP- dependent RNA helicase activity.12,13 The YTH domain mediates RNA-binding, recognizes and binds mºA-containing RNAs. Recent studies have reported that YTHDC2 plays a key role in meiosis and spermatogenesis.10-13 Existing research has recognized the critical part played by YTHDC2 in the initiation and progression of diseases including cancers. For instance, YTHDC2 could suppress mRNA sta- bility of lipogenic genes by finding m6A methylated sites and binding their mRNAs in order to regulate lipogenic genes expression in the liver, as a result of affecting the metabolism of liver lipids and sup- pressing liver steatosis34; YTHDC2 might be a potential molecular target in terms of the susceptibility of pancreatic cancer as well as a valuable marker for early detection35; YTHDC2 was identified to be a potential therapeutic target in radio sensitization of nasopharyngeal carcinoma (NPC) by favouring the translation efficiency of IGF1R mRNA to promote radiotherapy resistance36; the loss of YTHDC2 contributed to the proliferation in oesophageal squamous-cell carci- noma.37 However, in reviewing the literature, no study was found to comprehensively assess its distinct influence on pan-cancer.
In the present study, firstly, we examined YTHDC2 expression in TCGA tumours and found that YTHDC2 was significantly down- regulated in the majority of cancers than in the normal samples, in- cluding ACC, BLCA, BRCA, CESC, COAD, ESCA, KICH, LIHC, LUAD, LUSC, OV, PRAD, READ, SKCM, TGCT, THCA, UCEC and UCS, but was up-regulated in CHOL, DLBC, KIRC, LGG, PAAD, LAML and THYM. The results show that there is a considerable difference in YTHDC2 expression in the vast majority of malignant tumours, and moreover, YTHDC2 is tumour-suppressive in most cancers and is oncogenic in a few tumours, respectively. Like the dual role
A
B
KICH
KICH
8
C
UVM
**
**
**
*
**
**
*
**
7
The expression of CD274 Log2 (TPM+1)
The expression of SIGLEC15 Log2 (TPM+1)
UCS
*
6
6
UCEC
**
**
*
5
THYM
*
**
**
4
4
THCA
3
*
TGCT
2
Spearman
2
r = 0.560
Spearman
·
1
· r = 0.530
STAD
**
**
**
**
**
**
*
**
P < 0.001
0
0
P < 0.001
SKCM
T
**
-
2
3
4
2
3
4
The expression of YTHDC2 Log2 (TPM+1)
The expression of YTHDC2 Log2 (TPM+1)
SARC
**
*
READ
**
**
**
**
PRAD
**
**
**
**
**
**
**
**
D
UVM
E
STAD
3.5
PCPG
**
**
*
**
**
The expression of CD274
3.0
The expression of CD274 Log2 (TPM+1)
8
PAAD
**
**
**
*
**
**
*
**
Log2 (TPM+1)
2.5
OV
**
*
**
*
*
**
6
2.0
MESO
*
*p<0.05
1.5
4
LUSC
**
**
*
**
*
**
** p < 0.01
Correlation
1.0
LUAD **
**
**
**
0.50
Spearman
2
Spearman
0.25
0.5
r = 0.540
..
*= 0.480
LIHC
**
* **
**
**
**
0.00
0.0
P < 0.001
0
P < 0.001
0.25
LGG
**
**
**
**
**
*
1
2
3
4
1
2
3
4
The expression of YTHDC2 Log2 (TPM+1)
The expression of YTHDC2 Log2 (TPM+1)
LAML
**
**
**
**
**
KIRP
**
*
*
**
*
**
F
PCPG
G
KIRC
KIRC
**
** **
**
**
**
**
**
5
8
KICH
**
**
**
*
The expression of CD274 Log2 (TPM+1)
The expression of CD274 Log2 (TPM+1)
HNSC
**
**
**
**
**
**
**
**
4
6
GBM
*
**
3
ESCA
4
*
*
2
DLBC
**
*
Spearman
2
COAD
**
**
**
*
*
**
**
1
r = 0.510
Spearman
·P < 0.001
r = 0.480
P < 0.001
CHOL
*
*
0
0
2
3
4
2
3
4
5
CESC
The expression of YTHDC2 Log2 (TPM+1)
The expression of YTHDC2 Log2 (TPM+1)
BRCA
**
**
**
**
**
**
**
BLCA
*
*
ACC
*
**
*
CD274
CTLA4
HAVCR2
LAG3
PDCDI
PDCDILG2
SIGLEC15
TIGIT
of m6A methylation playing in human cancers, on the one hand, it could accelerate the progression of certain tumours through posi- tively affecting mºA modification; on the other hand, it may have a protective effect on tumour suppression. It is of great importance that YTHDC2 expression differs in different molecular subtypes of
cancers, including ACC, BRCA, COAD, HNSC, LGG, PCPG, PRAD, STAD and UCEC, suggesting that it could be more meaningful for YTHDC2 study focusing on a certain molecular subtype of cancers.
Then, we observed that the missense mutation of YTHDC2 was the primary type, and the highest incidence of that occurred in
WILEY
A
CHOL
B
COAD
1.0
1.0
C
LUSC
1.0
D
LUAD
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
YTHDC2
0.2
YTHDC2
AUC: 0.972
0.2
YTHDC2
0.2
YTHDC2
AUC: 0.816
AUC: 0.876
0.0
CI: 0.930-1.000
0.0
CI: 0.783-0.848
AUC: 0.906
CI: 0.883-0.928
0.0
CI: 0.851-0.901
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.0
0.2
0.4
0.6
0.0
0.2
0.4
0.6
0.8
1.C
1-Specificity (FPR)
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
E
PRAD
F
READ
G
THCA
H
UCEC
1.0
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
YTHDC2
YTHDC2
YTHDC2
0.2
AUC: 0.723
0.2
AUC: 0.866
0.2
YTHDC2
AUC: 0.947
AUC: 0.852
0.0
CI: 0.675-0.771
0.0
CI: 0.823-0.909
0.0
CI: 0.930-0.965
0.0
CI: 0.802-0.902
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
I
ACC
J
CESC
K
LAML
L
OV
1.0
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
YTHDC2
AUC: 0.805
0.2
YTHDC2
0.2
YTHDC2
AUC: 0.803
AUC: 0.738
0.2
YTHDC2
CI: 0.735-0.875
AUC: 0.999
0.0
0.0
CI: 0.631-0.976
0.0
CI: 0.677-0.800
0.0
CI: 0.999-1.000
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.C
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
M
SKCM
N
TGCT
0
UCS
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
YTHDC2
AUC: 0.908
0.2
YTHDC2
0.2
YTHDC2
AUC: 0.996
0.0
CI: 0.890-0.925
AUC: 0.996
0.0
CI: 0.991-1.000
0.0
CI: 0.989-1.000
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
UCEC patients. Interestingly, we also found that there was a mean- ingful difference between the altered group of YTHDC2 and the unaltered group in PFS, DFS and DSS, indicating that the missense mutation of YTHDC2 might be essential for the prognosis of UCEC.
Meanwhile, we confirmed that the YTHDC2 methylation level was closely related to the prognosis of patients with ACC, CESC, LUSC and UCEC, identifying the potential prognostic value of YTHDC2 methylation in the above cancers.
A
LGG
B
LGG
C
LGG
1.0
YTHDC2
1.0
YTHDC2
1.0
YTHDC2
Survival probability
++ Low
Low
Survival probability
Low
0.8
++ High
Survival probability
0.8
High
0.8
High
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival
0.2
Disease Specific Survival
+
Progress Free Interval ++
HR = 1.76 (1.25-2.48)
HR = 1.81 (1.26-2.60)
0.2
HR = 1.47 (1.12-1.93)
0.0
P = 0.001
0.0
P = 0.001
0.0
P = 0.006
+
0
2000
4000
6000
0
2000
4000
6000
0
1000
2000
3000
4000
5000
Time (days)
Time (days)
Time (days)
Low
264
39
8
0
Low
259
37
8
0
Low
-264
73
24
9
3
1
High
-263
21
7
1
High
260
21
7
1
High
263
51
9
3
1
0
D
READ
E
READ
F
READ
1.0
YTHDC2
1.0
YTHDC2
1.0
YTHDC2
Survival probability
Low
0.8
++ Low
Survival probability
High-
0.8
+ Lowt
Survival probability
++ High
0.8
High
0.6
0.6
0.6
0.4
0.4
+
0.4
F
+
0.2
Overall Survival
+
0.2
Disease Specific Survival
Progress Free Interval
HR = 0.29 (0.12-0.70)
HR = 0.11 (0.03-0.51)
0.2
HR = 0.32 (0.16-0.65)
0.0
P = 0.006
0.0
P = 0.005
0.0
P = 0.002
0
1000
2000
3000
4000
0
1000
2000
3000
4000
0
1000
2000
3000
4000
Time (days)
Time (days)
Time (days)
Low - 83
23
2
1
0
Low
78
20
2
1
0
Low
83
17
1
1
0
High
83
29
4
2
0
High
82
29
4
2
0
High
83
26
4
2
0
G
SKCM
H
SKCM
I
SKCM
1.0
YTHDC2
1.0
YTHDC2
1.0
YTHDC2
Survival probability
0.8
++ Low
Survival probability
0.8
Low
Survival probability
++ High
High
0.8
Low
++ High
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival HR = 0.74 (0.56-0.98)
0.2
Disease Specific Survival HR = 0.73 (0.54-0.97)
0.2
Progress Free Interval
HR = 0.79 (0.6010.90)
0.0
P = 0.035
0.0
P = 0.033
0.0
P = 0.041
-
0
3000
6000
9000
0
3000
6000
9000
0
3000
6000
9000
Time (days)
Time (days)
Time (days)
Low
270
43
8
0
0
Low
-251
40
7
0
0
Low -260
20
3
0
0
High
186
46
14
6
0
High
199
47
15
6
0
High
197
30
9
3
0
As previous studies demonstrated, YTHDC2 can promote the efficiency of translation of mRNA secondary structures probably due to RNA helicase activity,38 and also, RNA-binding domains con- tribute to the interrelationship between m6A-containing mRNAs and the ribosomes. Additionally, YTHDC2 favours the RNA degradation for the purpose of managing the stability of mRNAs.39 Thus, we con- ducted GO and KEGG analysis of 50 targeted binding proteins of YTHDC2 to further investigate its molecular mechanism, revealing that the catalytic activity, helicase activity, snRNA binding, spliceo- some and mRNA surveillance were mainly involved in BP, MF and enrichment pathway.
Prior studies have noted the crucial correlation between mºA modification and tumour microenvironment, implying that diverse mºA modification patterns play a vital role in the multiplicity and per- plexity of tumour microenvironment.40,41 According to Spearman cor- relation analysis of immune score and YTHDC2 gene expression, our study found that YTHDC2 was critically involved in the immune infil- tration of cancers, especially in COAD, KIRC, LIHC, LUAD and PAAD, due to the positive correlation between YTHDC2 expression and the infiltration levels of CD8+ T cell, CD4+ T cell, neutrophil, myeloid dendritic cell, macrophage and B cell. In addition, the results showed that YTHDC2 expression was significantly positively correlated with
B cell in 21 types of cancers, with CD4+ T cell in 17 types of cancers, with CD8+ T cell in 27 types of cancers, with dendritic cell in 23 types of cancers, with macrophage in 21 types of cancers and with neutro- phil in 30 types of cancers, but significantly negatively correlated with CD4+ T cell in five types of cancers, with CD8+ T cell in THCA, and with dendritic cell in SARC. Furthermore, we observed that YTHDC2 was closely associated with the expression of immune checkpoint- related genes in most types of cancers except for CESC and TGCT. In addition, YTHDC2 expressed differently in immune subtypes of can- cers, including BLCA, BRCA, KIRC, LIHC, LUAD, PAAD, SKCM, STAD and UCEC. The result suggests that YTHDC2 may have an important influence on the immune infiltration in most malignant tumours. Thus, research on the value of YTHDC2 in immune infiltration from a dis- tinct immune subtype in individual tumours may give assistance to provide new thinking of tumour immunotherapy.
It remains unknown whether YTHDC2 expression is associated with the diagnosis of cancers. Here, we set out to investigate the di- agnostic value of YTHDC2 in cancers. As a result, YTHDC2 presented a high diagnostic value for seven types of cancers, including CHOL, LUSC, THCA, OV, SKCM, TGCT and UCS. It is worth pointing out that YTHDC2 may be a remarkable diagnostic biomarker for CHOL, OV, TGCT and UCS, due to its higher sensitivity and specificity. Hence, further studies need to explore the early diagnostic value of YTHDC2 in the above cancers, given essential clinical implications of YTHDC2. Concomitantly, we wonder whether YTHDC2 takes an essential part in the prognosis of cancers as well. The results demonstrated that YTHDC2 expression was closely related to the survival prognosis of LGG, READ and SKCM, involving OS, DSS and PFI. Therefore, the analysis of prognosis in cancers provides a bioinformatics basis for further experimental research on the prognostic value of YTHDC2.
Pan-cancer analysis has emerged as an important research focus for tumorigenesis and development in recent years, our study pres- ents the significance of YTHDC2 from the perspective of pan-cancer, including gene expression, genetic alteration, molecular mechanism, immune infiltration, diagnostic value and clinical prognosis, sug- gesting YTHDC2 may be a promising biomarker for diagnosis and prognosis in certain cancers, and targeting YTHDC2 may provide new thinking to the immunotherapy of individual cancers. Although we identify the different roles of YTHDC2 in cancers from the per- spective of bioinformatics, there are also several limitations in the present study. Firstly, data analysed in our study only come from the TCGA database and the GTEX database without any other database or actual clinical data. Secondly, further molecular regulatory mecha- nism of YTHDC2 affecting cancer initiation and progression remains unknown. Broad exploration and deep verification of molecular bi- ology experiments are still need to perform and investigate in the individual cancer study, due to the heterogeneity and complexity of tumours. Furthermore, diverse computational methods can provide novel insights of bioinformatics exploration, such as lncRNA-miRNA interaction predictions, 42-44 the identification of microRNA combi- natorial biomarkers45-47 and predictive model.48,49 In summary, our study contributes to uncovering cancer promoting or suppressing effects of YTHDC2 in various cancer types comprehensively and
provides evidence on the role of YTHDC2 in tumour cell immune infiltration, diagnostic value and clinical prognosis.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interests.
AUTHOR CONTRIBUTIONS
Chiyuan Zhang: Conceptualization (lead); Data curation (lead); Investigation (lead). Cuishan Guo: Data curation (supporting); Formal analysis (supporting); Methodology (supporting). Yan Li: Data cura- tion (supporting); Methodology (supporting). Ling Ouyang: Data curation (supporting); Methodology (supporting); Supervision (sup- porting). Qi Zhao: Software (equal); Writing-original draft (equal). Kuiran Liu: Methodology (equal); Supervision (lead); Writing-original draft (equal).
CONSENT TO PARTICIPATE
Not applicable.
CONSENT FOR PUBLICATION
Not applicable.
DATA AVAILABILITY STATEMENT
Publicly available datasets were analysed in this study, which can be found in UCSC XENA (https://xenabrowser.net/datapages/).
ORCID Qi Zhao iD https://orcid.org/0000-0001-9713-1864
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SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section.
How to cite this article: Zhang C, Guo C, Li Y, Ouyang L, Zhao Q, Liu K. The role of YTH domain containing 2 in epigenetic modification and immune infiltration of pan-cancer. J Cell Mol Med. 2021;25:8615-8627. https://doi.org/10.1111/ jcmm.16818