Analysis
Pan-cancer analysis reveal that m’A writer WTAP involve in tumor cell cycle regulation and tumor immunity
Jingwei Shi1 . Gongyi Xie2 . Sijing Ye1 . Xiaoqiong Weng1 . Qingmei Zhou3
Received: 26 October 2024 / Accepted: 20 March 2025
Published online: 29 March 2025
@ The Author(s) 2025 OPEN
Abstract
Background Wilm’s tumor 1-associated protein (WTAP) is a critical component of the methyltransferase complex respon- sible for N6-methyladenosine (m6A) modification in RNA. This modification is involved in various cancer-related biologi- cal processes. However, the precise role of WTAP in tumor cell cycle regulation and immune responses remains poorly understood.
Methods A comprehensive analysis was conducted using multi-database resources to investigate the role of WTAP in tumorigenesis. Data from 33 tumor types were collected from the Genotype-Tissue Expression (GTEx), The Cancer Genome Atlas (TCGA), and Cancer Cell Line Encyclopedia (CCLE) databases. Correlations between WTAP expression and prognosis, immune microenvironment, immune neoantigens, immune checkpoint molecules, tumor mutation burden (TMB), and microsatellite instability (MSI) were analyzed. Additionally, Gene Set Enrichment Analysis (GSEA) was per- formed to explore the signaling pathways associated with WTAP expression.
Results Pan-cancer analysis revealed differential expression of WTAP across multiple tumor types compared to nor- mal tissues. High WTAP expression was significantly associated with poor prognosis in adrenocortical carcinoma (ACC), brain lower-grade glioma (LGG), liver hepatocellular carcinoma (LIHC), and ovarian serous cystadenocarcinoma (OV). In contrast, low WTAP expression correlated with improved survival in skin cutaneous melanoma (SKCM) and thymoma (THYM). WTAP expression demonstrated a positive correlation with immune cell infiltration, including B cells, CD4+T cells, CD8 +T cells, dendritic cells, macrophages, and neutrophils. Additionally, WTAP expression was positively associated with stromal, immune, and overall immune estimate scores. No significant association was identified between WTAP expression and immune neoantigen counts. However, WTAP expression correlated with the expression of most common immune checkpoint genes, DNA mismatch repair genes, and DNA methyltransferases. Furthermore, WTAP expression significantly influenced TMB and MSI levels. GSEA indicated that WTAP predominantly contributes to cell cycle regula- tion, thereby promoting tumorigenesis.
Conclusion WTAP is a potential immune-related prognostic biomarker in malignancies. Its role in regulating the cell cycle and immune microenvironment highlights its influence on tumor development and progression.
Jingwei Shi and Gongyi Xie contributed equally to this work and should be considered co-first authors
☒ Gongyi Xie, kongyee.zhe@foxmail.com; ☒ Qingmei Zhou, gjz209@126.com | 1Integrated Traditional Chinese Medicine and Western Medicine Department, The Second People’s Hospital of Baiyun Guangzhou, Guangzhou 510450, Guangdong Province, China. 2Department of Stomatology, The Second People’s Hospital of Baiyun Guangzhou, Guangzhou 510450, Guangdong Province, China. 3Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou 341000, Jiangxi Province, China.
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| https://doi.org/10.1007/s12672-025-02196-w
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1 Introduction
The reversible methylation of adenosine residues in eukaryotic RNA, known as N6-methyladenosine (m6A), is one of the most prevalent forms of RNA modification [1]. Increasing evidence indicates that this epigenetic alteration is crucial for a wide array of biological processes, including stem cell differentiation, sex determination, and cardiac rhythm regulation [2, 3]. Recent studies have demonstrated that aberrant global levels of m6A are associated with various cancers, suggesting a pivotal role for this modification in tumorigenesis [4, 5]. Both increased and decreased levels of m6A in RNA have been reported across different tumor types, with this dynamic modification closely linked to cancer initiation and progression [6]. Mechanistically, m6A involves the replacement of a hydrogen atom (-H) at the nitrogen-6 position of adenosine with a methyl group (-CH3). This reaction is catalyzed by a class of enzymes collec- tively referred to as “m6A writers”, which include the m6A methyltransferase complex. These methyltransferases serve as key regulators of m6A modification and have been implicated in the pathogenesis of various human cancers [7].
m6A represents the most prevalent post-transcriptional RNA modification in eukaryotic cells. The m6A meth- yltransferase complex, composed of methyltransferase-like 3 (METTL3), methyltransferase-like 14 (METTL14), and Wilms’ tumor 1-associated protein (WTAP), plays a central role in catalyzing this modification [8-10]. WTAP is essen- tial for the nuclear localization of the m6A methyltransferase complex, facilitating the recruitment and interaction of METTL3 and METTL14 [11, 12]. WTAP was initially identified in research on the Wilm’s tumor suppressor gene (WT1) [13]. The absence of WTAP expression has been shown to be embryonically lethal, while its overexpression is implicated in tumorigenesis, including glioma [14], ovarian cancer [15], and renal cell carcinoma [16]. Subsequent studies revealed that WTAP is an integral component of the human m6A methyltransferase complex, mediating the interaction between METTL3 and METTL14 and enabling their binding to RNA m6A motifs [11]. Furthermore, WTAP has been proposed as a prognostic biomarker in specific tumor types across a pan-cancer context, particularly due to its association with immune cell infiltration [17]. However, the extent to which WTAP influences the expression of other immune-related genes, immune-infiltrating cells, and broader tumorigenic processes remains unclear.
In this study, we observed that elevated WTAP expression was significantly associated with poor prognosis in patients with adrenocortical carcinoma (ACC), lower-grade glioma (LGG), liver hepatocellular carcinoma (LIHC), and ovarian serous cystadenocarcinoma (OV). Conversely, in patients with skin cutaneous melanoma (SKCM) and thy- moma (THYM), lower WTAP expression was associated with improved survival outcomes. WTAP expression exhib- ited a positive correlation with the infiltration of various immune cell types, including B cells, CD4 +T cells, CD8 +T cells, dendritic cells, macrophages, and neutrophils. Furthermore, WTAP expression was positively associated with key immune-related metrics such as stromal score, immune score, and estimate immune score. Additionally, WTAP expression demonstrated a significant correlation with the expression of commonly studied immune checkpoint genes, DNA mismatch repair genes, and DNA methyltransferases. Our analysis further revealed that tumor mutational burden (TMB) and microsatellite instability (MSI) levels were influenced by WTAP expression. Mechanistically, WTAP appears to play a role primarily in cell cycle regulation, thereby contributing to tumorigenesis.
2 Materials and methods
2.1 Sample information
Gene and clinical information for normal and tumor tissues were retrieved from the Genotype-Tissue Expression (GTEx) database (https://gtexportal.org/) and The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer. gov/). A total of 31 cancer types from the GTEx database were used for gene expression analyses, while 27 cancer types from TCGA were included in integrated analyses with GTEx. Data for cancer cell lines were obtained from the Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/), and immune-infiltrating cell scores for each tumor type were downloaded from the TIMER database (https://cistrome.shinyapps.io/timer/). The abbreviation of each tumor as follow: ACC (Adrenocortical carcinoma); BLCA (Bladder Urothelial Carcinoma); BRCA (Breast invasive carcinoma); CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma); CHOL (Cholangiocarcinoma); COAD (Colon adenocarcinoma); DLBC (Lymphoid Neoplasm Diffuse Large B-cell Lymphoma); ESCA(Esophageal carcinoma); GBM (Glioblastoma multiforme); HNSC (Head and Neck squamous cell carcinoma);
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KICH(Kidney Chromophobe); KIRC (Kidney renal clear cell carcinoma); KIRP (Kidney renal papillary cell carcinoma); LAML (Acute Myeloid Leukemia); LGG (Brain Lower Grade Glioma); LIHC (Liver hepatocellular carcinoma); LUAD (Lung adenocarcinoma); LUSC (Lung squamous cell carcinoma); MESO (Mesothelioma); OV (Ovarian serous cystadenocarci- noma); PAAD (Pancreatic adenocarcinoma); PCPG (Pheochromocytoma and Paraganglioma); PRAD (Prostate adeno- carcinoma); READ (Rectum adenocarcinoma); SARC (Sarcoma); SKCM (Skin Cutaneous Melanoma); STAD (Stomach adenocarcinoma); TGCT (Testicular Germ Cell Tumors); THCA(Thyroid carcinoma); THYM (Thymoma); UCEC (Uterine Corpus Endometrial Carcinoma); UVM (Uveal Melanoma).
2.2 Pan-cancer analysis of WTAP expression
We analyzed the expression levels of WTAP across various normal tissues, cell lines, and tumor types. The differences in WTAP expression between normal and tumor tissues were assessed using the Kruskal-Wallis test. Violin plots illustrating these differences were generated using the ggplot2 package in R.
2.3 Pan-cancer analysis of the prognostic impact of WTAP
WTAP expression was categorized into high- and low-expression groups using the bipartite method. Univariate Cox proportional hazards regression was employed to evaluate the prognostic differences between the two groups within each tumor type, with the results presented in a forest plot. Survival curves were subsequently constructed using the Kaplan-Meier method.
2.4 Association analysis of WTAP with immune cell infiltration and the tumor microenvironment
Data on immune cell infiltration were obtained from the TIMER database. Stromal score, immune score, and ESTIMATE score were calculated using the ESTIMATE package in R. The correlation between WTAP expression and immune cell infil- tration scores was deemed significant when the p-value was less than 0.05 and the correlation coefficient (R) exceeded 0.20.
2.5 Association analysis of WTAP with neoantigens and immune checkpoint genes
Tumor-specific neoantigens arise from mutations, such as insertions or deletions, in cancer genomes. Neoantigen iden- tification was performed using ScanNeo, which calculates the binding affinity scores of epitopes with lengths ranging from 8 to 11 amino acids. Epitopes with binding affinity scores below 500 nM were classified as neoantigens. Furthermore, the correlation between WTAP expression and 47 commonly studied immune checkpoint genes from the TCGA database was analyzed. Correlations were considered statistically significant and positive when the p-value was less than 0.05 and the correlation coefficient (R) exceeded 0.20.
2.6 Association analysis of WTAP with tumor mutational burden and microsatellite instability
Tumor mutational burden (TMB) refers to the number of somatic mutations per megabase of the genomic sequence, while microsatellite instability (MSI) is characterized by mutations in simple repetitive DNA sequences. TMB and MSI mutation data were extracted from the TCGA database for each tumor type. The relationship between WTAP expression and TMB/MSI levels was assessed using Spearman’s rank correlation test, and radar plots were generated using R.
2.7 Association analysis of WTAP with DNA mismatch repair genes and DNA methylation
DNA mismatch repair (MMR) is a critical mechanism for correcting mismatches that occur during DNA replication. The correlation of WTAP expression with five MMR-related genes (MLH1, MSH2, MSH6, PMS2, and EPCAM) was investigated. Additionally, DNA methylation, an epigenetic process that modifies DNA without altering the reading frame, was studied. The relationship between WTAP expression and four methylation-related genes (DNMT1, DNMT2, DNMT3, and DNMT3b) was analyzed.
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WTAP expression levels and their association with overall cancer survival were analyzed using TCGA data. Univariate Cox regression analysis was conducted to assess survival differences between tumors with high and low WTAP expression. As
3 Results 3.1 WTAP expression is elevated in pan-cancer were considered the thresholds for significant enrichment. A normalized enrichment score (|NES|) greater than 1, p-value less than 0.05, and false discovery rate (FDR) below 0.25 We initially analyzed WTAP expression across 31 tissue types obtained from the GTEx database (Fig. 1A) and 21 tissue types derived from tumor cell lines in the CCLE database (Fig. 1B). Subsequently, WTAP expression levels in cancer and adjacent normal tissues for each tumor type were extracted from the TCGA database (Fig. 1C). To increase sample size and robustness, we integrated normal tissue data from GTEx with TCGA datasets (Fig. 1D).
The potential influence of WTAP expression on gene set enrichment was evaluated using gene set enrichment analysis (GSEA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Hallmark gene sets were employed for this analysis.
2.8 Gene set enrichment analysis of WTAP in pan-cancer
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3.2 Prognostic implications of WTAP expression in pan-cancer
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| https://doi.org/10.1007/s12672-025-02196-w
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CHOLIN=9,T=36)
Small Instinc(N=92)
Fig. 1 WTAP Expression in Pan-Cancer. A WTAP expression levels across 31 normal tissues obtained from the GTEx dataset. B: WTAP expres- sion levels across 21 tumor cell lines derived from the CCLE dataset. C: Comparative analysis of WTAP expression in tumor tissues and paired
Spien(N= 100)
COAD(N=4|T=458)
Stomach(N=174)
*** P <0.001. D: WTAP expression differences in 27 cancer types by integrating normal tissue data from the GTEx dataset and tumor tissue adjacent noncancerous tissues across 20 cancer types from the TCGA dataset. Statistical significance is indicated as *P <0.05, ** P <0.01,
ESCA(N=11.T=162)
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Thyroid( Nu 279)
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Utrus( N=78)
data from the TCGA dataset. Statistical significance is denoted as *P <0.05, ** P < 0.01, *** P <0.001
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billary_track Nu7)
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KIRP(N=32T=29)
D huemabpoictc_and_lymphoid(N=146) central_pervous system(N= 103)
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LOG(N=5,T=525)
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LIHC(N=50,T=373)
intestine(N=61)
LUAD(N=59.T=515)
kidney(N=36)
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LUSC(N=49.T=501)
75
Ihver(N=108)
PAAD(No4;T=178)
king( N= 107)
PRAD(N=5).T=46)
log2(TPM+1)
oesophagta(N=26)
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READX(N=10T=167)
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ovary(N=52)
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pancreas( N=52)
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THCA(N=58,T=510)
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sakvary gland Na2)
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ACC(N=128,T=79)
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Normal
thyroid( N=12)
BLCA(N=28,T=408)
upper_acrodigestive_tract(N=32)
BRCA(N=2)2T=1098)
urinary_tract(N=27)
CESC(N=13,T=306)
denx(N=27)
CHOL(N=9,T=36)
COAD(N=349,T=458)
ESCA(N=664T=162)
GBM(N=1157;T=167)
HNSC(N=44,T=502)
KICH(N=52T=65)
KIRC(N=72.T=531)
KIRP(N=32.T=289)
LAMIAN=70,T=151)
LOG(N=1157.T=525)
LIHC(N=160,T=373)
LUAD(N=347,T=515)
LUSC(N=49.T=501)
OV(N=8&T=379)
PAAD(N=[71,T=(78)
PRAD( N=\52,T=496)
READ(N=10,T=167)
SKCM(N=813,T=471)
STAD(N=206,T=375)
TGCT(N=165.T=156)
THCA(N=337.T=510)
UCEC(N=35.T=544)
UCS(N=78T=56)
depicted in Fig. 2, WTAP expression was significantly associated with overall survival in six tumor types: ACC (HR = 1.03, p=0.0072), LGG (HR=1.02, p<0.001), LIHC (HR= 1.03, p <0.001), OV (HR= 1.01, p=0.010), SKCM (HR= 0.99, p = 0.042), and THYM (HR=0.96, p=0.018). Kaplan-Meier survival curves were constructed to visualize the survival differences (Fig. 3). These results indicated that high WTAP expression serves as a poor prognostic indicator in ACC, LGG, LIHC, and OV, while it is protective in SKCM and THYM.
3.3 WTAP association with tumor immune infiltration and microenvironment in pan-cancer
WTAP expression levels were significantly correlated with tumor-infiltrating immune cells across multiple cancers. For example, in BRCA, WTAP expression positively correlated with CD8 +T cells (R=0.259, p<0.001), dendritic cells (R=0.224, p<0.001), and neutrophils (R=0.311, p<0.001) (Fig. 4A). Similarly, WTAP expression showed significant correlations with all six types of tumor-infiltrating lymphocytes in KIRC (Fig. 4B). In KIRP, WTAP was positively correlated with B cells (R=0.249, p<0.001), CD8+T cells (R=0.239, p<0.001), dendritic cells (R=0.441, p<0.001), and neutrophils (R=0.473, p<0.001) (Fig. 4C). To further investigate the tumor microenvironment, we calculated stromal, immune, and ESTIMATE scores for each tumor. As shown in Fig. 4D, WTAP expression was positively associated with stromal score, immune score, and ESTIMATE score, indicating its significant role in modulating the tumor microenvironment.
3.4 Association of WTAP with tumor neoantigens and immune checkpoint genes
In the pan-cancer analysis, WTAP expression showed no significant correlation with the number of neoantigens across cancer types (Fig. 5A). However, analysis of the relationship between WTAP expression and common immune checkpoint
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| ACC | 1.03(1.01 ~ 1.05) | 0.00720 | |
| BLCA | 1(0.99 ~ 1.01) | 0.54000 | |
| BRCA | 1(1 ~ 1.01) | 0.40000 | |
| CESC | 1.01(1 ~ 1.02) | 0.17000 | |
| CHOL | 1(0.97 ~ 1.04) | 0.98000 | |
| COAD | 1(0.98 ~ 1.01) | 0.64000 | |
| DLBC | 0.96(0.91 ~ 1.02) | 0.20000 | |
| ESCA | 1(0.98 ~ 1.02) | 0.98000 | |
| GBM | 1(0.99 ~ 1) | 0.90000 | |
| HNSC | 1(0.99 ~ 1.01) | 0.65000 | |
| KICH | 1.05(0.97 ~1.13) | 0.22000 | |
| KIRC | 1(0.99 ~ 1.01) | 0.40000 | |
| KIRP | 1.02(1 ~ 1.04) | 0.05300 | |
| LAML | 1(0.98 ~ 1.02) | 0.96000 | |
| LGG | 1.02(1.01 ~ 1.02) | 0.00013 | |
| LIHC | 1.03(1.02 ~ 1.05) | 0.00017 | |
| LUAD | 1(1 ~ 1.01) | 0.33000 | |
| LUSC | 1(0.99 ~ 1.01) | 0.89000 | |
| MESO | 1.01(1 ~ 1.03) | 0.05700 | |
| OV | 1.01(1 ~ 1.02) | 0.01000 | |
| PAAD | 1(0.98 ~ 1.02) | 0.88000 | |
| PCPG | 0.99(0.91 ~ 1.07) | 0.75000 | |
| PRAD | 1.01(0.98 ~ 1.04) | 0.72000 | |
| READ | 0.97(0.93 ~ 1.01) | 0.09700 | |
| SARC | 1(0.99 ~ 1.01) | 0.80000 | |
| SKCM | 0.99(0.98 ~ 1) | 0.04200 | |
| STAD | 1(0.98 ~ 1.01) | 0.64000 | |
| TGCT | 1(0.94 ~ 1.06) | 0.92000 | |
| THCA | 1(0.97 ~ 1.04) | 0.82000 | |
| THYM | 0.96(0.92 ~ 0.99) | 0.01800 | |
| UCEC | 1.01(0.99 ~ 1.02) | 0.36000 | |
| UCS | 0.99(0.97 ~ 1.02) | 0.65000 | |
| UVM | 1.01(0.98 ~ 1.05) | 0.48000 | |
| 0.71 | 1.0 HR(95%CI) |
A
BRCA B_col
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BRCA
BRCA
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rman conel R=0.509
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rman comel Ru0 244 P.1.350-08
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Spearman’s rho:
Spearman’s rho:
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Spearman’s rho:
Spearman’s rho:
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ESTIMATEScore
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man com R=0.099
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nan corr R=0.178
LUAD
man corr R=0.112
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nan corr R=0.011
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CD200
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log 2(Neoantigen count)
log2(Ncoantigen count) density
density
P=0.233
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P=0.152
P=0.884
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log2(Neoantigen count)
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00.02.04
density
log2(WTAP TPM+1)
000.10.2
density
log2(WTAP TPM+1)
000204
density
CD40LG
log2(Neoantigen count) density
BRCA
man corr 2.0.045
KIRC
nan corr
KIRP
R=0.045
man corr
density
UCEC
nan corr R=0.035 P=0.589
CTLA4
R=0.04
ONA
CD48
P=0.228
ONE
P=0.367
P=0.607
CD28
log 2(Neoantigen count)
75
log2(Ncoantigen count)
log 2(Neoantigen count)
CD200R1
%
HAVCR2
SØ
ADORA2A
CD276
35
2
KIR3DL1
af
CD80
log2(WTAP TPM+1)
0:00.10.20.3 density
log2(WTAP TPM+1)
0.00.10.20.3
log2(WTAP TPM+1)
log2(WTAP TPM+1)
10015.000520
PDCD1
density
density
density
LGALS9
log2(Neoantigen count) density
COAD
man corr Ru-0.103
density
READ
nan corr
STAD
man corr R=0.199 ৳0.0019
density
HNSC
nan corr
3=0.201
R=0.06 P=0.316
CD160
P=0.304
P=0.145
0:00
TNFSF14
9
S
log 2(Neoantigen count)
log2(Ncoantigen count)
log2(Neoantigen count)
IDO2
10
12
ICOSLG
2
TMIGD2
C
2
VTCN1
CA
IDO1
No
PDCD1LG2
25
1
ausiais
3
log2(WTAP TPM+1)
000102
density
log?(WTAP TPM+1)
density
40 43 10 55 60 6000102 log2(WTAP TPM+1)
log2(WTAP TPM+1)
00.0.03
HHLA2
density
density
TNFSF18
log2(Ncoantigen count) density
LIHC
nan corr R-0.018
density
SKCM
nan corr
CESC
3 .- 0.035
%
nan com
R =- 0.1
density
THCA
nan coer
BTNL2
3 =- 0.104
CD70
P=0.798
P=0.733
P=0.167
3.0.0684
TNFSF9
log 2(Neoantigen count)
log2(Neoantigen count)
log 2(Neoantigen count)
TNFRSF8
6
0
CD27
TNFRSF25
S
15
VSIR
0
A
TNFRSF4
CD40
log2[WTAP TPM+1)
0:00.10.20.3 density
log2(WTAP TPM+1)
0.00.10.20.3
log2(WTAP TPM+1)
0 00 10 203
density
density
log2(WTAP TPM+1)
0.00.10.20.3
density
log2(Ncoantigen count) density
TNFRSF18 TNFSF15
BLCA
man com R =- 0.117
density
PRAD
nan corr
LGG
man corr R=0.164
R=0.011
P=0.173
S
TIGIT
P=0.857
3.0.021:
CD274
log 2(Neoantigen count)
Ing2(Neoantigen count)
CD86
CD44
TNFRSF9
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
3
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA THYM
UCEC
UCS
UVM
log2(WTAP TPM+1)
0 00 10.203
density
000034
log2(WTAP TPM+1)
density
log2(WTAP TPM+1)
0002.04 density
Pearson’s rho
-log10(p value)
0.43 -0.21 0
0.21
0.435
6.42 7.85 $ 9.2
10.7
A
B
UVM.P-0.076 BLCA,P-0.77 BRCA,P-0.85
UCS,P-0.81
CESC.P-0.14
UVM,P-0.69 BLCA,P-0.94 BRCA,P-0.73
UCEC,P-0.18
0.34
UCS,P-0.094
CESC.P-0.4
CHOL,P-0.85
UCEC.P-9.1e-07
0.36
CHOL,P-0.15
THYM.P-0.00076
COAD,P-8.6e-05
THYM,P-0.82
COAD.P-7.1e-08
THCA,P-1.8e-10
DLBC.P-0.57
THCA.P-0.55
DLBC.P-0.12
TGCT,P-0.8
ESCA,P-0.32
TGCT,P-0.042
ESCA,P-0.84
STAD,P-1.3e-06
GBM.P-0.43
STAD,P-0.033
GBMP-0.02
-0.34
-0.36
SKCM.P-0.036
HNSC,P-0.083
SKCM,P-0.0035
HNSC.P-0.9
SARC.P-0.96
KICH,P-0.6
SARC.P-0.52
KICH,P-0.46
READ,P-0.12
KIRC.P-0.25
READ,P-5.7e-06
KIRC.P-0.75
PRAD.P-0.0024
KIRP.P-0.21
PRAD.P-3.1e-06
KIRP.P-0.1
PCPG,P-0.67
LAML,P-0.074
PCPG,P-0.67
LAML,P-0.29
PAAD,P-0.13
LGG,P-2.1e-08
PAAD,P-0.022
LGG,P-0.17
OV.P-0.035
MESO,P-0.59 LUSC.P-0.92 LUAD,P-0.14
LIHC,P-0.018
OV.P-0.081
MESO,P-0.89LUSC.P-4.3e-05LUAD,P-5.3e-06
LIHC,P-0.6
genes revealed significant correlations in several tumors, including KICH, KIRC, LGG, and PAAD (Fig. 5B). These findings suggest that WTAP may play a role in modulating immune checkpoint gene expression in a tumor-specific manner.
Discover
3.5 WTAP association with tumor mutational burden and microsatellite instability in pan-cancer
WTAP expression was significantly correlated with tumor mutational burden (TMB) in multiple cancers (Fig. 6A). Posi- tive correlations were observed in COAD (p<0.001), LGG (p<0.001), SKCM (p=0.036), and STAD (p<0.001), whereas negative correlations were identified in LIHC (p=0.018), PRAD (p=0.0024), THCA (p<0.001), and THYM (p<0.001). For microsatellite instability (MSI), WTAP expression showed positive correlations in COAD (p<0.001), READ (p<0.001), STAD (p=0.033), TGCT (p=0.042), and UCEC (p<0.001) (Fig. 6B). Negative correlations were observed in GBM (p=0.02), LUAD (p<0.001), LUSC (p<0.001), PAAD (p=0.022), PRAD (p<0.001), and SKCM (p=0.0035). These results indicate that WTAP may influence tumor genomic stability and mutation rates in a context-dependent manner.
3.6 WTAP association with DNA mismatch repair and DNA methylation in pan-cancer
WTAP expression was significantly correlated with DNA mismatch repair (MMR) genes in most tumors, with the exception of CHOL, GBM, and MESO (Fig. 7A). Similarly, WTAP expression showed significant correlations with four DNA methyla- tion-related genes (DNMT1, DNMT2, DNMT3A, and DNMT3B) across most tumor types, except for UCS, DLBC, and MESO (Fig. 7B). These findings suggest that WTAP may contribute to tumorigenesis through its involvement in DNA repair mechanisms and epigenetic modifications.
3.7 WTAP involvement in cancer cell cycle and immunity regulatory pathways
To investigate the functional role of WTAP in cancer, pan-cancer samples were stratified into high and low WTAP expres- sion groups. Gene Set Enrichment Analysis (GSEA) was performed using KEGG and Hallmark gene sets, with the results summarized in Tables 1 and 2. As illustrated in Fig. 8, the most significantly enriched KEGG pathways included ubiquitin- mediated proteolysis, oocyte meiosis, and cell cycle regulation. In the Hallmark analysis, the PI3K-AKT-mTOR pathway, mitotic spindle regulation, and G2/M checkpoint control were the most enriched pathways. These findings suggest that WTAP is involved in tumorigenesis and progression by modulating pathways related to the cell cycle and immune regulation.
4 Discussion
Wilm’s tumor 1-associated protein (WTAP) was initially identified using the yeast two-hybrid system during the char- acterization of its interaction with the Wilms’ tumor 1 (WT1) gene in a subset of renal carcinoma cases [13]. WTAP plays a critical role in various physiological processes, and recent studies have established WTAP as an essential component of the m6A methyltransferase complex, facilitating the recruitment of other key elements such as METTL3 [18, 19]. Our findings demonstrated that overexpression of WTAP was associated with poor prognosis across most cancers, with the
A
B
READ
SKCM
MLH1
PRAD
PCPG
TOCT
THCA
MSH2
0
MSH6
LUMO
PMS2
4)
UHC
EPCAM
100
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
DONW
Pearson’s rho
-log10(p value)
-0.69
-0.35
0
0.35 0.690
11.31 22.62 33.93 45.24
Discover
| TERM | ES | NES | NP | FDR | FWER |
|---|---|---|---|---|---|
| KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS | -0.6373 | -2.2351 | 0.0000 | 0.0024 | 0.002 |
| KEGG_OOCYTE_MEIOSIS | -0.5925 | -2.1026 | 0.0000 | 0.0148 | 0.012 |
| KEGG_CELL_CYCLE | -0.6949 | -2.0919 | 0.0000 | 0.0122 | 0.016 |
| KEGG_RNA_DEGRADATION | -0.6942 | -2.0890 | 0.0000 | 0.0091 | 0.016 |
| KEGG_PROGESTERONE_MEDIATED_OOCYTE_MATURATION | -0.5653 | -2.0393 | 0.0000 | 0.0148 | 0.03 |
| KEGG_CHEMOKINE_SIGNALING_PATHWAY | -0.5191 | -2.0135 | 0.0000 | 0.0177 | 0.044 |
| KEGG_NUCLEOTIDE_EXCISION_REPAIR | -0.6741 | -1.9774 | 0.0000 | 0.0 0.0242 | 0.058 |
| KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY | -0.5919 | -1.9654 | 0.0020 | 0.0250 | 0.07 |
| KEGG_SMALL_CELL_LUNG_CANCER | -0.5518 | -1.9608 | 0.0000 | 0.0228 | 0.071 |
| KEGG_P53_SIGNALING_PATHWAY | -0.5471 | -1.9583 | 0.0000 | 0.0211 | 0.072 |
| KEGG_SNARE_INTERACTIONS_IN_VESICULAR_TRANSPORT | -0.5814 | -1.9540 | 0.0000 | 0.0208 | 0.075 |
| KEGG_NON_SMALL_CELL_LUNG_CANCER | -0.5553 | -1.9337 | 0.0000 | 0.0233 | 0.092 |
| KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY | -0.5528 | -1.9274 | 0.0061 | 0.0233 | 0.099 |
| KEGG_FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS | -0.5340 | -1.9250 | 0.0040 | 0.0221 | 0.101 |
| KEGG_DNA_REPLICATION | -0.7945 | -1.9145 | 0.0061 | 0.0233 | 0.112 |
| KEGG_ENDOMETRIAL_CANCER | -0.5714 | -1.9059 | 0.0000 | 0.0243 | 0.122 |
| KEGG_COLORECTAL_CANCER | -0.5477 | -1.9003 | 0.0020 | 0.0241 | 0.128 |
| KEGG_NEUROTROPHIN_SIGNALING_PATHWAY | -0.5147 | -1.8997 | 0.0041 | 0.0228 | 0.128 |
| KEGG_PANCREATIC_CANCER | -0.5409 | -1.8889 | 0.0040 | 0.0244 | 0.145 |
| KEGG_RIG_I_LIKE_RECEPTOR_SIGNALING_PATHWAY | -0.5300 | -1.8857 | 0.0000 | 0.0243 | 0.153 |
| TERM | ES | NES | NP | FDR | FWER |
|---|---|---|---|---|---|
| HALLMARK_PI3K_AKT_MTOR_SIGNALING | -0.5575 | -2.0716 | 0.0000 | 0.0133 | 0.007 |
| HALLMARK_MITOTIC_SPINDLE | -0.6159 | -2.0429 | 0.0000 | 0.0123 | 0.01 |
| HALLMARK_G2M_CHECKPOINT | -0.7226 | -2.0148 | 0.0019 | 0.0140 | 0.019 |
| HALLMARK_PROTEIN_SECRETION | -0.6084 | -1.9660 | 0.0058 | 0.0173 | 0.029 |
| HALLMARK_E2F_TARGETS | -0.7519 | -1.9531 | 0.0039 | 0.0158 | 0.034 |
| HALLMARK_TGF_BETA_SIGNALING | -0.5811 | -1.8849 | 0.0076 | 0.0275 | 0.069 |
| HALLMARK_DNA_REPAIR | -0.5663 | -1.8799 | 0.0078 | 0.0252 | 0.073 |
| HALLMARK_ANDROGEN_RESPONSE | -0.5133 | -1.8706 | 0.0039 | 0.0240 | 0.081 |
| HALLMARK_MYC_TARGETS_V1 | -0.6826 | -1.8556 | 0.0096 | 0.0238 | 0.089 |
| HALLMARK_COMPLEMENT | -0.4726 | -1.8192 | 0.0078 | 0.0302 | 0.117 |
| HALLMARK_INTERFERON_GAMMA_RESPONSE | -0.5808 | -1.8095 | 0.0202 | 0.0294 | 0.122 |
| HALLMARK_IL2_STAT5_SIGNALING | -0.4399 | -1.8093 | 0.0039 | 0.0270 | 0.122 |
| HALLMARK_MTORC1_SIGNALING | -0.5539 | -1.7707 | 0.0176 | 0.0355 | 0.156 |
| HALLMARK_INFLAMMATORY_RESPONSE | -0.4880 | -1.7640 | 0.0196 | 0.0347 | 0.164 |
| HALLMARK_KRAS_SIGNALING_UP | -0.4359 | -1.7570 | 0.0099 | 0.0347 | 0.172 |
| HALLMARK_UV_RESPONSE_DN | -0.4919 | -1.7207 | 0.0146 | 0.0449 | 0.221 |
| HALLMARK_ALLOGRAFT_REJECTION | -0.5397 | -1.7152 | 0.0524 | 0.0436 | 0.228 |
| HALLMARK_SPERMATOGENESIS | -0.4363 | -1.6776 | 0.0158 | 0.0535 | 0.264 |
| HALLMARK_HEME_METABOLISM | -0.3968 | -1.6752 | 0.0230 | 0.0518 | 0.269 |
| HALLMARK_APOPTOSIS | -0.4199 | -1.6494 | 0.0252 | 0.0607 | 0.304 |
exception of SKCM and THYM. This observation aligns with previous reports indicating that WTAP acts as a protective prognostic factor in cutaneous melanoma [20]. However, WTAP has not been previously investigated in thymoma (THYM). Notably, our study is the first to suggest that WTAP functions as a protective factor in THYM, underscoring its potential tumor-specific roles in cancer prognosis.
Discover
A
Enrichment plot KEGG terms
B
Enrichment plot KEGG terms
0.0
0.4
Enrichment score
Enrichment score
-0.2
0.2
-0.4
Term
0.0
Term
UBIQUITIN MEDIATED PROTEOLYSIS
SULFUR METABOLISM
-0.6
ES-0.64,NES — 2.2,P-0,FDR-0.0024
ES-0.35,NES-0.92,P-0.55,FDR-1
OOCYTE MEIOSIS
LINOLEIC ACID METABOLISM
ES — 0.59,NES-2.1,P-0,FDR-0.015
11
ES-0.34.NES-1,P-0.38,FDR-1
CELL CYCLE
ARACHIDONIC_ACID_METABOLISM
ES-0.69,NES — 2.1,P-0,FDR-0.012
ES-0.31,NES-T.1.P-0.28,FDR-1
ALPHA_LINOLENIC ACID METABOLISM
ES-0.4T.NES-1.2,P=0.21,FDR-1
0.4
Rank
0.0
Low_exp
High_exp
0.4
High_exp
0.4
Rank
0.0
0.4
Low_exp
0.8
0.8
-1.2
0
10000
20000
-1.2
0
10000
20000
Rank in ordered dataset
Rank in ordered dataset
C
Enrichment plot HALLMARK terms
D
Enrichment plot HALLMARK terms
0.0
0.1
Enrichment score
Enrichment score
-0.2
0.0
-0.4
Term
-0.1
Term
-0.6
PI3K_AKT MTOR_SIGNALING
KRAS_SIGNALING_DN
ES-0.56.NES-2.1,P-0,FDR-0.013
ES-0.16.NES-0.71.P-0.98,FDR-0.83
MITOTIC SPINDLE
PANCREAS BETA CELLS
ES-0.62.NES-2,P-0,FDR-0.012
ES — 0.22.NES — 0.68.P-0.9,FDR-0.84
G2M_CHECKPOINT
COAGULATION
ES-0.72,NES-2,P-0.0019,FDR-0.014
ES — 0.16.NES-0.6.P-0.97,FDR-0.92
MYOGENESIS
ES — 0.14,NES — 0.57,P-0.99,FDR-0.92
0.4
Rank
0.0
Low_exp
High_exp
0.4
High_exp
0.4
Rank
0.0
Low_exp
0.8
0.4
0.8
-12
0
10000
20000
-12
0
10000
20000
Rank in ordered dataset
Rank in ordered dataset
Abnormal expression levels of m6A modifications have been reported to be associated with poor prognosis, chemoresistance, and other malignant characteristics [14, 15, 21]. Notably, m6A modifications can also interfere with the cell cycle, a series of events in which a single cell divides into two daughter cells, encompassing the duplication of DNA, separation of organelles, and distribution of cytoplasmic components. This process is tightly regulated by cyclins and cyclin-dependent kinases (CDKs). While CDKs generally remain stable throughout the cell cycle, cyclin expression oscillates in a phase-specific manner. These oscillations ensure the accurate transition between phases, and cellular mechanisms prevent the accumulation of errors that could lead to faulty genetic information being passed to progeny [22].
Dysregulation of the cell cycle is a hallmark of most cancers, often leading to tumorigenesis. The progression of the cell cycle depends on the timely elimination of phase-specific regulatory components. On one hand, cyclins are pre- dominantly exported from the nucleus and degraded via ubiquitin-mediated pathways. On the other hand, the degra- dation of their mRNA prevents further translation [23]. This dynamic balance between protein degradation and mRNA stability is critical for cell cycle progression. Emerging evidence indicates that m6A modifications play a significant role in the post-transcriptional regulation of the cell cycle, thereby contributing to tumor progression. For instance, the m6A reader IGF2BP proteins can stabilize a broad spectrum of mRNA transcripts containing the GG(m6A)C motif, thereby promoting their stability in cancer biology [24]. Hirayama et al. demonstrated that depletion of FTO, the first identified m6A demethylase, increased the m6A level of cyclin D1 mRNA, promoting its degradation and thereby interfering with the G1 phase of the cell cycle. Moreover, in normal cells, cyclin D1 mRNA exhibits significantly lower m6A levels in the G1 phase compared to non-G1 phases, inversely correlating with cyclin D1 protein levels [25]. Other m6A regulators, such as YTHDF1 [26] and METTL16 [27], have also been implicated in regulating cell-cycle-associated mRNA expression through m6A-dependent mechanisms. In our pan-cancer gene enrichment analysis, we stratified samples based on WTAP expression levels. High WTAP expression was associated with significant enrichment in several biosynthetic and regulatory pathways, including cell cycle regulation, ubiquitin-mediated proteolysis, RNA degradation, mitotic spindle formation, G2/M checkpoint, PI3K-AKT-MTOR pathway, TGF-ß signaling, IL2-STAT5 signaling, and the p53 pathway. These findings indicate a potential role of WTAP in positively regulating these pathways. Collectively, these results suggest that aberrant WTAP expression contributes to cell cycle dysregulation by interfering with proteolysis, mRNA decay, and chromosome separation, potentially through its influence on various signal transduction pathways.
Discover
In addition to its role in cell cycle regulation, the tumor microenvironment (TME) is another well-studied area associ- ated with m6A modifications. The TME, composed of diverse stromal cells, such as infiltrating immune cells and mesen- chymal stem cells, along with regulatory factors like cytokines, acts as the “soil” for the “seeds” of cancer cells to grow, while also influencing their resistance to anti-cancer therapies. Recent studies have highlighted the involvement of m6A modifications in shaping tumor immunity. Li et al. demonstrated that T cells lacking the m6A writer METTL3 remain in a naïve state due to reduced mRNA decay and increased protein expression of SOCS family genes, which encode inhibitors of STAT signaling [28]. Furthermore, Wang et al. reported that METTL3-mediated m6A modification of immune-related genes, such as CD40, CD80, and TLR4, is critical for dendritic cells to activate T cell-mediated immunity [29]. Similarly, Zhang et al. analyzed over 1,900 gastric cancer samples and found that the immune phenotypes of the TME are closely associated with m6A modification patterns based on clustering of m6A regulators [30]. Our analysis revealed that WTAP expression is significantly correlated with various tumor-infiltrating lymphocytes, suggesting that WTAP-mediated regu- lation may play a role in TME development. This finding highlights WTAP’s potential involvement in modulating the immune landscape of tumors and influencing tumor progression.
Despite these insights, our study has several limitations. The results are based on publicly available datasets, which may be subject to selection bias and limited representation of diverse patient cohorts. Additionally, our findings rely solely on bioinformatics analyses, and experimental validation is necessary to elucidate the precise mechanisms underlying WTAP’s role in tumor biology.
In summary, our work provides a comprehensive understanding of WTAP’s role in oncology, particularly its involve- ment in cell cycle regulation and tumor immunity. These findings suggest that WTAP may serve as a potential biomarker and therapeutic target in cancer. Further studies are warranted to validate its clinical relevance and explore its functional mechanisms in tumorigenesis.
Author contributions QM Zhou and GY Xie designed this study; JW Shi and GY Xie, Collated data from public databases; QM Zhou, JW Shi and GY Xie analyzed and interpreted the data; QM Zhou and GY Xie drafted the manuscript.QM Zhou and GY Xie edited the manuscript. SJ Ye, XQ Weng, QM Zhou, JW Shi and GY Xie supervised the study. All the authors read and approved the final manuscript.
Data availability All data in our study are available upon request.
Declarations
Ethics approval and consent to participate Not application.
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco mmons.org/licenses/by-nc-nd/4.0/.
References
1. Zhao BS, Roundtree IA, He C. Post-transcriptional gene regulation by mRNA modifications. Nat Rev Mol Cell Biol. 2017;18(1):31-42.
2. Lence T, Akhtar J, Bayer M, Schmid K, Spindler L, Ho CH, Kreim N, Andrade-Navarro MA, Poeck B, Helm M, Roignant JY. m(6)A modulates neuronal functions and sex determination in Drosophila. Nature. 2016;540(7632):242-7.
3. Wu Y, Zhou C, Yuan Q. Role of DNA and RNA N6-adenine methylation in regulating stem cell fate. Curr Stem Cell Res Ther. 2018;13(1):31-8.
4. Hou J, Zhang H, Liu J, Zhao Z, Wang J, Lu Z, Hu B, Zhou J, Zhao Z, Feng M, et al. YTHDF2 reduction fuels inflammation and vascular abnor- malization in hepatocellular carcinoma. Mol Cancer. 2019;18(1):163.
5. Wang Q, Chen C, Ding Q, Zhao Y, Wang Z, Chen J, Jiang Z, Zhang Y, Xu G, Zhang J, et al. METTL3-mediated m(6)A modification of HDGF mRNA promotes gastric cancer progression and has prognostic significance. Gut. 2020;69(7):1193-205.
6. Li Z, Peng Y, Li J, Chen Z, Chen F, Tu J, Lin S, Wang H. N(6)-methyladenosine regulates glycolysis of cancer cells through PDK4. Nat Com- mun. 2020;11(1):2578.
7. Huang H, Weng H, Chen J. m(6)A modification in coding and non-coding RNAS: roles and therapeutic implications in cancer. Cancer Cell. 2020;37(3):270-88.
Discover
8. Barranco C. Viral infection linked to m6A alterations in host mRNAs. Nat Rev Mol Cell Biol. 2020;21(2):64-5.
9. Wang T, Kong S, Tao M, Ju S. The potential role of RNA N6-methyladenosine in cancer progression. Mol Cancer. 2020;19(1):88.
10. Jiang X, Liu B, Nie Z, Duan L, Xiong Q, Jin Z, Yang C, Chen Y. The role of m6A modification in the biological functions and diseases. Signal Transduct Target Ther. 2021;6(1):74.
11. Ping X-L, Sun B-F, Wang L, Xiao W, Yang X, Wang W-J, Adhikari S, Shi Y, Lv Y, Chen Y-S, et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 2014;24(2):177-89.
12. Liu J, Yue Y, Han D, Wang X, Fu Y, Zhang L, Jia G, Yu M, Lu Z, Deng X, et al. A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat Chem Biol. 2014;10(2):93-5.
13. Little NA, Hastie ND, Davies RC. Identification of WTAP, a novel Wilms’ tumour 1-associating protein. Hum Mol Genet. 2000;9(15):2231-9.
14. Xi Z, Xue Y, Zheng J, Liu X, Ma J, Liu Y. WTAP Expression predicts poor prognosis in malignant glioma patients. J Mol Neurosci. 2016;60(2):131-6.
15. Yu HL, Ma XD, Tong JF, Li JQ, Guan XJ, Yang JH. WTAP is a prognostic marker of high-grade serous ovarian cancer and regulates the pro- gression of ovarian cancer cells. Onco Targets Ther. 2019;12:6191-201.
16. Tang J, Wang F, Cheng G, Si S, Sun X, Han J, Yu H, Zhang W, Lv Q, Wei JF, Yang H. Wilms’ tumor 1-associating protein promotes renal cell carcinoma proliferation by regulating CDK2 mRNA stability. J Exp Clin Cancer Res. 2018;37(1):40.
17. Lei J, Fan Y, Yan C, Jiamaliding Y, Tang Y, Zhou J, Huang M, Ju G, Wu J, Peng C. Comprehensive analysis about prognostic and immunologi- cal role of WTAP in pan-cancer. Front Genet. 2022;13:1007696.
18. Yan X, Liu F, Yan J, Hou M, Sun M, Zhang D, Gong Z, Dong X, Tang C, Yin P. WTAP-VIRMA counteracts dsDNA binding of the m6A writer METTL3-METTL14 complex and maintains N6-adenosine methylation activity. Cell Discov. 2023;9(1):100.
19. Zhang J, Wei J, Sun R, Sheng H, Yin K, Pan Y, Jimenez R, Chen S, Cui X-L, Zou Z, et al. A lncRNA from the FTO locus acts as a suppressor of the m6A writer complex and p53 tumor suppression signaling. Mol Cell. 2023;83:15.
20. Feng Z-Y, Wang T, Su X, Guo S. Identification of the m6A RNA Methylation Regulators WTAP as a novel prognostic biomarker and genomic alterations in cutaneous melanoma. Front Mol Biosci. 2021;8: 665222.
21. Zhang Y, Kang M, Zhang B, Meng F, Song J, Kaneko H, Shimamoto F, Tang B. m(6)A modification-mediated CBX8 induction regulates stemness and chemosensitivity of colon cancer via upregulation of LGR5. Mol Cancer. 2019;18(1):185.
22. Evan GI, Vousden KH. Proliferation, cell cycle and apoptosis in cancer. Nature. 2001;411(6835):342-8.
23. Phan TG, Croucher PI. The dormant cancer cell life cycle. Nat Rev Cancer. 2020;20(7):398-411.
24. Huang H, Weng H, Sun W, Qin X, Shi H, Wu H, Zhao BS, Mesquita A, Liu C, Yuan CL, et al. Recognition of RNA N(6)-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat Cell Biol. 2018;20(3):285-95.
25. Hirayama M, Wei FY, Chujo T, Oki S, Yakita M, Kobayashi D, Araki N, Takahashi N, Yoshida R, Nakayama H, Tomizawa K. FTO Demethylates Cyclin D1 mRNA and Controls Cell-Cycle Progression. Cell Rep. 2020;31(1): 107464.
26. Lou X, Ning J, Liu W, Li K, Qian B, Xu D, Wu Y, Zhang D, Cui W. YTHDF1 Promotes Cyclin B1 Translation through m(6)A Modulation and Contributes to the Poor Prognosis of Lung Adenocarcinoma with KRAS/TP53 Co-Mutation. Cells. 2021;10:7.
27. Wang XK, Zhang YW, Wang CM, Li B, Zhang TZ, Zhou WJ, Cheng LJ, Huo MY, Zhang CH, He YL. METTL16 promotes cell proliferation by up-regulating cyclin D1 expression in gastric cancer. J Cell Mol Med. 2021;25(14):6602-17.
28. Li HB, Tong J, Zhu S, Batista PJ, Duffy EE, Zhao J, Bailis W, Cao G, Kroehling L, Chen Y, et al. m(6)A mRNA methylation controls T cell homeo- stasis by targeting the IL-7/STAT5/SOCS pathways. Nature. 2017;548(7667):338-42.
29. Wang H, Hu X, Huang M, Liu J, Gu Y, Ma L, Zhou Q, Cao X. Mettl3-mediated mRNA m(6)A methylation promotes dendritic cell activation. Nat Commun. 2019;10(1):1898.
30. Zhang B, Wu Q, Li B, Wang D, Wang L, Zhou YL. m(6)A regulator-mediated methylation modification patterns and tumor microenviron- ment infiltration characterization in gastric cancer. Mol Cancer. 2020;19(1):53.
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