Analysis
EBNA 1BP2 identified as potential prognostic biomarker for multiple tumor types in pan-cancer analysis
Li-Yue Sun1 . Yu-Ying Jiang1,2 . Xin-Xin Zeng1 . Ju Shen 1,2 . Ke-Xin Xian1,2 . Quan-An Xu1 . Xian Xu1,2 . Lei Liang3 . Xu-Hui Zhang1
Received: 19 March 2024 / Accepted: 6 September 2024
Published online: 11 October 2024
@ The Author(s) 2024 OPEN
Abstract
Background Epstein-Barr virus (EBV) infection has been closely linked to the development of various types of cancer. EB nuclear antigen 1 binding protein 2 (EBNA1BP2) is a crucial molecule for stable isolation of EBV in latent infection. However, the role of EBNA 1BP2 in multiple tumor types is remains unclear. In this study, we comprehensively analyzed the functional characteristics of EBNA1BP2 and investigate its potential as a prognostic biomarker in pan-cancer.
Methods We utilized data from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) databases and employed various bioinformatics analysis tools, including TIMER2.0, HPA, GEPIA2.0, PrognoScan, cBioPortal, CancerSEA, and BioGRID to explore the expression pattern, prognostic value, immune infiltration, and methylation level of EBNA1BP2 in pan-cancer. Additionally, we conducted enrichment analysis of genes associated with EBNA1BP2 to identify potential biological functions and pathways.
Results Our analysis revealed that EBNA1BP2 expression was significantly higher in tumor tissues compared to tumor- adjacent tissues. We observed that lower expression of EBNA1BP2 in adrenocortical carcinoma (ACC), brain lower grade glioma (LGG), sarcoma (SARC), and uterine carcinosarcoma (UCS) was significantly associated with improved overall sur- vival (OS) and disease-free survival (DFS). Furthermore, the promoter methylation level of EBNA1BP2 was downregulated in the majority of cancer types. At the single-cell level, EBNA1BP2 was found to be positively correlated with cell cycle and DNA repair processes, while negatively correlated with hypoxia. Additionally, EBNA1BP2 was associated with the infiltration of immune cells such as B cells, cancer-associated fibroblast cells, and CD8+T cells. Gene enrichment analysis indicated that EBNA1BP2 was mainly involved in nucleoplasm and RNA binding pathways.
Conclusion Our findings suggest that EBNA 1BP2 may serve as a potential prognostic biomarker for survival in pan-cancer. Further experimental studies are needed to validate these findings and explore the underlying mechanisms by which EBNA 1BP2 contributes to tumorigenesis.
Keywords EBNA1BP2 . Prognosis . Pan-cancer . Biomarker
Li-Yue Sun, Yu-Ying Jiang and Xin-Xin Zeng contributed equally to this work.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s12672-024- 01326-0.
☒ Lei Liang, leiliang@jnu.edu.cn; ☒ Xu-Hui Zhang, 9158863@qq.com; Li-Yue Sun, sunly7@mail2.sysu.edu.cn; Yu-Ying Jiang,
jiangyy0216@163.com; Xin-Xin Zeng, 1094259057@qq.com; Ju Shen, shenju33@163.com; Ke-Xin Xian, xkxmzh@126.com; Quan-An Xu, xuquanan23@163.com; Xian Xu, space0227@163.com | 1Second Department of Oncology, Guangdong Second Provincial General Hospital, Guangzhou, China. 2Department of Radiation Oncology, Guangdong Medical University, Zhanjiang, China. 3Guangdong Engineering Research Center of Chinese Medicine & Disease Susceptibility, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
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(2024) 15:549 | https://doi.org/10.1007/s12672-024-01326-0
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1 Introduction
Tumors are a highly prevalent and lethal disease characterized by multifactorial involvement and complex developmen- tal stages [1]. They are also a leading cause of death globally [2]. With advancements in research, a plethora of publicly funded databases have emerged, focusing on genomics, epigenomics, transcriptomics, proteomics, and other tumor- related fields, such as The Cancer Genome Atlas (TCGA) [3]and Gene Expression Omnibus (GEO) [4]. These resources are instrumental in identifying similarities and differences across various cancer types, and in discovering new therapeutic targets and biomarkers. Additionally, research into biological pathways and immune cell infiltration lays the ground- work for advancing treatments for malignant tumors. Consequently, analyzing the expression, pathways, prognosis, and immune infiltration of genes of interest across multiple cancer types is essential for assessing their clinical relevance.
EB nuclear antigen 1 binding protein 2 (EBNA1BP2) is a conserved eukaryotic homolog of Saccharomyces cerevisiae and is predominantly localized within the nucleolus. It has been shown to play a significant role in pre-ribosomal RNA processing, ribosomal subunit assembly, and cellular growth [5, 6]. In addition to regulating the binding of EB virus (EBV) to chromosomes, EBNA 1BP2 can also regulate cellular processes such as the cell cycle and induce chromosomal instabil- ity, ultimately leading to carcinogenesis [7]. Previous study have demonstrated that EBNA1BP2 promotes proliferation in anaplastic large-cell lymphoma (ALCL) cells by regulating the tumor suppressor p53 [8]. However, there is currently a lack of research on the role and underlying mechanisms of EBNA1BP2 in multiple types of tumors.
In this study, we employed a comprehensive approach using various bioinformatics methods to analyze the expression profiles and survival outcomes associated with EBNA1BP2. Our aim was to further investigate the potential mechanisms underlying EBNA1BP2 in human tumorigenesis and identify a potential prognostic biomarker for survival.
2 Materials and methods
2.1 Gene expression analysis of EBNA1BP2
We utilized the “Gene DE” plate of TIMER2.0 (Tumor Immune Estimation Resource, version 2, http://timer.cistrome.org) to analyze the expression level of EBNA 1BP2 in different tumor types compared to adjacent normal samples [9]. To further investigate the relationship between EBNA1BP2 expression and patients’ pathological stages across all TCGA cancers, we generated box plots of complementary gene expression and performed analysis using the GEPIA2.0 (Gene Expression Profiling Interactive Analysis, version 2, http://gepia2.cancer-pku.cn/#analysis) tool [10]. Moreover, we examined the total protein expression of EBNA1BP2 in pan-cancers using the “Clinical Proteomic Tumor Analysis Consortium (CPTAC)” mod- ule of UALCAN (The University of Alabama at Birmingham CANcer data analysis Portal, http://ualcan.path.uab.edu/analy sis-prot.html) [11, 12]. Additionally, we obtained promoter methylation levels of EBNA1BP2 in all cancers using this tool.
2.2 Immunohistochemistry staining
To verify the expression of EBNA1BP2 at the protein level, we utilized the HPA (Human Protein Atlas, http://www.prote inatlas.org/) tool to download immunohistochemistry (IHC) images and compare the protein expression level [13].
2.3 Survival prognosis analysis
We obtained Kaplan-Meier plots for overall survival and disease-free survival of EBNA 1BP2 expression in all tumor types using the “Survival Analysis” module of GEPIA2.0. TCGA tumor patients were divided into high and low EBNA 1BP2 expres- sion groups using a cut-off value of 50%. Comparisons between these groups were performed using the log-rank test. Additionally, we utilized the Kaplan-Meier plotter tool (https://kmplot.com/analysis/) to obtain Kaplan-Meier survival curves of EBNA 1BP2 in pan-cancer analysis [14].
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2.4 Genetic alteration analysis
“Cancer Types Summary” module of cBioPortal (The cBio Cancer Genomics Portal) tool (https://www.cbioportal.org/) was used to acquire the alteration frequency, mutation type, mutated site information of the EBNA1BP2 protein across all TCGA tumors. Additionally, we obtained the three-dimensional (3D) structure of the protein from the “Mutations” module [15]. The “Comparison/Survival” module of cBioPortal can also be used to obtain information on the progression-free survival (PFS), disease-specific survival (DSS), disease-free survival (DFS), and overall survival (OS) of EBNA1BP2 gene mutations in various cancers.
2.5 Immune cells infiltration analysis
The correlation analysis between EBNA1BP2 expression in all tumor types and immune infiltration cells using the “Immune” module of TIMER2.0, combining various algorithms such as TIMER, EPIC, TIDE, QUANTISEQ, CIBERSORT, CIB- ERSORT-ABS, XCELL, MCPCOUNTER [16].
2.6 Single cell sequencing
To investigate the functional status of cancer cells at the single-cell level and explore the relationship between EBNA1BP2 expression and cancer cell function in different tumors, we utilized the CancerSEA tool (Atlas of Cancer Single Cell Status, CancerSEA-Database Commons.mhtml). Additionally, we generated t-SNE plots to visualize the EBNA1BP2 expression profiles in TCGA tumors, allowing us to observe the clustering patterns and distribution of EBNA1BP2 expression across different tumor samples [17].
2.7 Gene enrichment analysis
Protein-protein interactions network was obtained from BioGRID (Biological General Repository for Interaction Data- sets, https://thebiogrid.org/). To identify genes that are correlated with EBNA1BP2 expression, we used the “Similar Gene Detection” module of GEPIA2.0 to obtain the top 100 EBNA1BP2-correlated genes from the datasets of all TCGA tumor and normal tissues [18]. We then performed pairwise gene-gene Pearson correlation analysis between EBNA1BP2 and the selected genes using the “Correlation Analysis” module of GEPIA2.0. Raw data were downloaded from the DAVID website (https://david.ncifcrf.gov), and we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the potential biological functions and signaling pathways associated with EBNA1BP2 in TCGA tumors [19].
2.8 Tissue specimen collection
Nine LUAD cases along with their paired normal lung tissue samples were collected from patients who underwent lung cancer resection surgery at the Guangdong Second Provincial General Hospital in 2023. The sample was fixed with 10% neutral formalin and embedded in paraffin for further analysis. This study has been approved by ethnic committee of Guangdong Second Provincial General Hospital (2022-KZ-295). All procedures were strictly in accordance with the appropriate version of the Declaration of Helsinki (as revised in Brazil 2013). Informed consent were obtained from each participant.
2.9 DNA extraction and real-time PCR
The total DNA of LUAD tissue and normal lung tissue was extracted with a FFPE DNA extraction kit (TianGen Biochem- istry, Beijing). The real-time PCR (RT-PCR) was tested with a TaqMan probe method (ABI QuantStudio 6, Applied Bio- systems, CA). The reaction was performed in a 96-well plate. Each well contained FAM-labeled probes for EBNA1BP2 and Cy5-labeled probe for reference gene GAPDH. The total volume of 10 uL of included 10 ng of tissue DNA, 7.5 uL of mix, and 0.64 uL of primer. The primer sequence was mentioned as below: EBNA1BP2 (F) 5’-3’: CGAAGCGACCCA CTGATTAT and (R) 5’-3’: TCCATGGCAGCCTGTTTAG. GAPDH (F) 5’-3’: TAGGCAGCAGCAAGCATTCC and (R) 5’-3’: ACG
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AAGCCCTTCCAGGAGAA. EBNA1BP2 5’-3’probe: FAM-AGATGCAGAAGATTCGACAGAAGCTGC-BHQ1. GAPDH5’-3’probe: Cyc-TTGTGCCCAGACTGTGGGTGGCAGT-BHQ3. Cycling conditions included one cycle at 95 ℃ for 5 min, 20 cycles at 95 ℃ for 15 s, 64 ℃ for 30 s, and 40 cycles at 60 ℃ for 10 s. The relative expression levels of EBNA1BP2 are calculated with the 2-AAct method.
3 Results
3.1 Gene expression analysis of EBNA1BP2
In our analysis using the TIMER2.0 database, we observed that the expression level of EBNA1BP2 was generally higher in tumor samples compared to adjacent normal tissues in most of cancer types. Specifically, we found higher expres- sion levels of EBNA1BP2 in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carci- noma (THCA), and uterine corpus endometrial carcinoma (UCEC) (Fig. 1A). However, in kidney chromophobe (KICH), we observed the opposite trend, with lower expression of EBNA1BP2 in tumor samples compared to normal tissues. To further validate our findings, we combined data from both TCGA and GTEx databases. In this analysis (Fig. 1B), we found higher expression of EBNA1BP2 in diffuse large B-cell lymphoma (DLBC), ovarian serous cystadenocarcinoma (OV), and thymoma (THYM) tumors compared to adjacent normal controls. In contrast, no significant differential expression was observed in adrenocortical carcinoma (ACC), brain lower grade glioma (LGG), sarcoma (SARC), and uterine carcinosarcoma (UCS) tumors (Supplementary Figure S1). Overall, our analysis indicates that EBNA1BP2 is highly expressed in most tumor types, suggesting its potential role in cancer development and progression.
To analyze EBNA1BP2 expression at the protein level, we utilized the National Cancer Institute’s CPTAC tool. Our analysis revealed that the total protein expression of EBNA1BP2 significantly increased in BRCA, OV, COAD, KIRC, LUAD, HNSC, pancreatic adenocarcinoma (PAAD), polymorphous glioblastoma (GBM), and LIHC (Fig. 1C). Furthermore, we examined the relationship between EBNA 1BP2 expression and clinical tumor pathological stages using the GEPIA2.0 tool. Our analysis indicated that the expression level of EBNA 1BP2 in KICH, KIRC, and LIHC showed significant differ- ences across different pathological stages (Fig. 1D; Supplementary Figure S2).
In order to further validate the expression of EBNA1BP2, IHC results of the tumors were acquired using the HPA database. The IHC staining of EBNA1BP2 was found to be moderately or strongly expressed in BRCA, LICH, and LUSC tumors. This analysis was consistent with previous EBNA1BP2 gene expression results from the TCGA database (Fig. 2A-C).
3.2 Survival analysis data
Next, we explored the relationship between the expression of EBNA1BP2 and the prognosis of survival. The result of GEPIA2.0 showed that low levels of EBNA1BP2 expression in ACC, BLCA, LGG, LIHC, MESO, SARC, UCS were significantly associated with longer OS (Fig. 3A). In ACC, HNSC, KIRP, LGG, PRAD, SARC and UCS, low EBNA1BP2 expression was also associated with better DFS (Fig. 3B).
As shown in Supplementary Figure S3, our analysis revealed that lower expression of EBNA1BP2 in BLCA, LIHC, LUAD, SARC, and UCEC was associated with improved OS. However, the results were contrary in KIRC, OV, PCPG, STAD, and THCA. Specifically, the combined analysis of GEPIA2.0 and Kaplan-Meier plotter tools showed that low EBNA1BP2 expression was significantly associated with longer OS in BLCA, LIHC, and SARC. These findings were primarily derived from the TCGA database. Additionally, we further assessed the OS for EBNA1BP2 using the PrognoScan website, which mainly extracted data from the GEO database. The results demonstrated that EBNA1BP2 expression was significantly associ- ated with bladder, brain, and lung cancers, and surprisingly, low EBNA1BP2 expression was also related to better OS (Supplementary Figure S4A-C). These findings suggest that EBNA 1BP2 may serve as a potential prognostic marker for a wide variety of cancer types.
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A
EBNA1BP2 Expression Level (log2 TPM)
10
**
00
3
A
₹
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
F
8
S
5
Expression log.(TPM +1)
Expression log2(TPM +1)
00
Expression log2(TPM +1)
00
Expression log2(TPM +1)
.
₹
0
.
”
.
2
2
~
-
3
0
0
C
DLBC (num(T)=47; num(N)=337)
LAML (num(T)=173; num(N)=70)
OV (num(T)=426; num(N)=88)
THYM (num(T)=118; num(N)=339)
BRCA
OV
COAD
3-
p=3.18459440086964E-08
3
p=8.42317985854299E-17
4-
p=6.50865253461221E-55
2-
2-
2
1-
1-
Z- value
Z- value
Z- value
0
0
0
2-
-1
-1
-2
-2
4
3
Normal (n=18)
Primary tumor (n=125)
3
Normal (n=25)
Primary tumor (n=100)
6
Normal (n=100)
Primary tumor (n=97)
KIRC
LUAD
HNSC
4
p=6.54656013628915E-60
a
p=1.54367311582675E-23
3- p=1.58265975634903E-16
2
2
2
0-
1
Z-value
Z-value
Z- value
1
A
0
4
-1
8
-2.
2
-8
Normal (n=84)
Primary tumor (n=110)
-3
Normal (n=111)
Primary tumor (n=111)
-3
Normal (n=71)
Primary tumor (n=108)
PAAD
GBM
LIHC
3-
p=4.67790698206085E-11
3-
3
P=9.80926920982816E-05
p=2.43165655185967E-60
2-
2
2-
1
.
.
Z- value
Z- value
Z- value
0
0
1-
”
.2-
-1-
3
-2
-2-
4
-3
Normal (n=74)
Primary tumor (n=137)
-3
Normal (n=10)
Primary tumor (n=99)
.5
Normal (n=165)
Primary tumor (n=165)
D
-
KICH
F value = 3.56 Pr(>F) = 0.0191
A
KIRC
F value = 3.75 Pr(>F) = 0.011
-
LIHC
F value = 3.38 Pr(>F) = 0.0186
.
-
6
1
0
5
.
5
4
0
+
0
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
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A
BRCA
200
p < 1E-12
175
Transcript per million
150
8
125
100
75
50
25
0
Normal (n=114)
Primary tumor (n=1097)
Breast normal
BRCA
B
LIHC
120
p=1.62447832963153E-12
100-
Transcript per million
80
60
40
20
0
Normal (n=50)
Primary tumor (n=371)
Liver normal
LIHC
C
LUSC
200
p < 1E-12
175
Transcript per million
150
125
100
75
50
25
Normal (n=52)
Primary tumor (n=503)
Lung normal
LUSC
3.3 Genetic alteration landscape analysis
The EBNA 1BP2 gene mutation has been shown to affect cellular functions [20]. To analyze the mutation status of the EBNA1BP2 gene in various types of tumors, we utilized the cBioPortal platform. The results revealed that in ovarian cancer (OV), the primary type of alteration was “amplification,” with the highest frequency of EBNA1BP2 alteration (>5%). UCEC had the highest incidence of “mutation,” with a frequency of approximately 2% of cases (Fig. 4A). Fig- ure 4B illustrates other mutations and their locations within the EBNA1BP2 gene. We observed that missense muta- tions were the most common type of mutation in EBNA1BP2. Among these, the G291 =/K291N/X291_splice mutation was the most frequently mutated region of the EBNA1BP2 protein. Figure 4C displays the gene site visualized in the 3D structure of the EBNA1BP2 protein. Furthermore, we analyzed the potential association between EBNA1BP2 gene alterations and pan-cancer survival prognosis. However, we found that alterations in the EBNA1BP2 gene did not significantly impact patient prognosis (Supplementary Figure S5). Further verification may require additional patient clinical data in the future.
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kg 10(1 IR:
€.8
ENSG00000117395.10 (EBNA1BP2)
€ 3
ACC BLCA
☐
€.3
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
-c.3
€.8
ACC
BLCA
LGG
LIHC
1.0
Low EBNA1BP2 Group
1.0
High Group
Low Group
1.0
Low EBNA1BP2 Group
1.0
Low EBNA1BP2 Group
Logrank p=0.016
High EBNA1BP2 Group
HR(high)=2.7
Logrank p=0.0041
High Group
High EBNA1BP2 Group
HR(high)=1.5
Logrank p=1.1e-06
Logrank p=0.0013
0.B
p(HR)=0.021
0.8
p(HR)=0.0044
0.8
HR(high)=2.5
p(HR)=2.4e-06
O.B
HR(high)=1.8
p(HR)=0.0015
Percent survival
n(high)=38 nįlow)=38
Percent survival
n(high)=201 n(low)=201
Percent survival
n(high)=257
nílow)=257
Percent survival
n(high)=182
0.6
0.6
0.6
0.6
n(low)=182
0.4
0.4
0.4
0.4
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0
50
100
150
0
50
100
15D
0
50
100
150
200
0
20
40
60
80
100
120
Months
Months
Months
Months
MESO
SARC
UCS
9
Low
1.0
High EBNA1BP2 Group
Low EBNA1BP2
1.0
High EBNA 1BP2 Group
Low EBNA1BP2 Group
Logrank p=0.0032
Logrank p=0.0012
High EBNA1BP2 Group
Logrank p=0.028
0.8
HR(high)=2
p(HR)=0.0037
0.8
HR(high)=1.9
p(HR)=0.0014
0.8
HR(high)=2.1
p(HR)=0.032
Percent survival
n(high)=41
n(low)=41
Percent survival
n(high)=131 n(low)=131
Percent survival
n(high)=28
0.6
0.6
0.6
n(law)=28
D.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
20
40
60
80
0
50
100
150
0
20
40
60
80
100
120
140
Months
Months
Months
log10(HR)
B
CE
ENSG00000117395.10
€.2
(EBNA1BP2)
ACC BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD READ
SARC
SKCM
STAD
TGCT
THCA
UCEC THYM
C.E
UCS
UVM
-0.3
-C.C
ACC
HNSC
KIRP
LGG
1.0
Low
1.0
High EBNA1BP2 Group
Low EBNA1BP2 Group
1.0
Low EBNA1BP2 Group
4
Low EBNA1BP2 Group
Logrank p=0.02
High
Logrank p=0.022
High EBNA1BP2.Group
Logrank p=0.0042
High
HR(high)=2.2
p(HR)=0.024
HR(high)=1.5
HR(high)=2.3
Logrank p=0.00021
0.8
0.8
0.8
0.8
HR(high)=1.8
P(HR)=0.022
P(HR)=0.0054
P(HR)=0.00025
Percent survival
n(high)=38
n(low)=38
Percent survival
n(high)=259
Percent survival
n(high)=141 n(low)=141
Percent survival
n(high)=257
0.6
0.6
ń(low)=259
0.6
0.8
n(low)=257
0.4
0.4
0.4
0.4
++
D.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0
50
100
150
0
50
100
150
200
0
50
100
150
200
0
50
100
150
Months
Months
Months
Months
PRAD
SARC
UCS
1.D
Low EBNA1BP2
1.0
High EBNA1BP2 Group
Low EBNA1BP2 Group
1.0
High EBNA1BP2 Group
Low EBNA 1BP2 Group
Logrank p=0.027
High EBNA1BP2 Group
0.8
HR(high)=1.6
Logrank p=0.012
Logrank p=0.038
HR(high)=1.6
P(HR)=0.029
0.8
p(HR)=0.012
0.8
HR(high)=2.1
P(HR)=0.045
Percent survival
n(high)=245
nflow)=245
Percent survival
n(high)=131
Percent survival
n(high)=28
0.6
0.6
n(low)=131
0.6
nílow)=28
0.4
0.4
0.4
0.2
0.2
0.2
O.D
0.0
0.0
0
50
100
150
0
50
100
150
0
20
40
60
80
100
120
140
Months
Months
Months
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Alteration Frequency
Mutation
Structural Variant
Amplification
Deep Deletion Multiple Alterations
6%
5%
4%
3%
2%
1%
Structural variant data
Mutation data
CNA data +
Ovarian Serous Cystadenocarcinoma
Uterine Corpus Endometrial Carcinoma
Bladder Urothelial Carcinoma
Pheochromocytoma and Paraganglioma
Sarcoma
Diffuse Large B-Cell Lymphoma
Skin Cutaneous Melanoma
Stomach Adenocarcinoma
Head and Neck Squamous Cell Carcinoma
Colorectal Adenocarcinoma
Mesothelioma
Esophageal Adenocarcinoma
Adrenocortical Carcinoma
Liver Hepatocellular Carcinoma
Lung Adenocarcinoma
Lung Squamous Cell Carcinoma
Breast Invasive Carcinoma
Cervical Squamous Cell Carcinoma
Glioblastoma Multiforme Acute Myeloid Leukemia)
Prostate Adenocarcinoma
Kidney Renal Clear Cell Carcinoma
Kidney Renal Papillary Cell Carcinoma
Brain Lower Grade Glioma
Thymoma
Kidney Chromophobe
Testicular Germ Cell Tumors
Cholangiocarcinoma
Pancreatic Adenocarcinoma
Uterine Carcinosarcoma
Uveal Melanoma
Thyroid Carcinoma
B
# EBNA1BP2 Mutations
5
44
Missense
G291=/K291N/X291_splice
5 Truncating
1 Inframe
5
Splice
5
Fusion
RefSeq:NM_001159936
0
Ebp2
Ensembl:ENST00000236051
CCDS:CCDS478
Uniprot:EBP2_HUMAN
0
100
200
306aa
C
3.4 Methylation level analysis
Abnormal methylation of gene promoter regions is frequently implicated as a contributing factor in carcinogenesis [21]. To investigate the methylation levels of the EBNA1BP2 gene in tumor and normal tissues, we utilized the UAL- CAN tool. The results demonstrated that the promoter methylation of the EBNA1BP2 gene was significantly higher in normal tissues compared to tumor tissues. However, in KIRC and KIRP, the methylation of the EBNA1BP2 promoter
Discover
BLCA p=1.62436730732907E-12
BRCA
COAD p=1.61891000000081E-05
ESCA
0.14-
013-
p=2.092100E-02
4.14
0.13
p=4.141600E-02
0.12
0.12-
Q13-
4.12-
0.11
0.12
Beta value
Beta value
Beta value
Beta value
0.11
0.1
411
0.1
41
0.08-
0.1
0.09-
0.00
0.09
0.06
0.08
0.08
0.08
0.04
Normal (n=21)
Primary tumor (n=418)
GOT
Normal (n=97)
Primary tumor (n=793)
0.07
Normal (n=37)
Primary tumor (n=313)
0.07
Normal (n=16)
Primary tumor (n=185)
HNSC
KIRC p=1.65001345919791E-12
LIHC
LUAD p=9.92239999999533E-05
0.14
p=1.9180599999946E-05
0.15
016
p=5.980100E-03
4.18
0.13
014-
(14
0.16
013
Beta value
0.12
Beta value
Beta value
0.12-
Beta value
Q12
0.14-
0.11
a11
0.1
0.12
01
0.1
0.00
0.08
0.1-
0.09
0.08
Normal (n=50)
Primary tumor (n=528)
0.06
Normal (n=160)
Primary tumor (n=324)
0.08
Normal [n=50)
Primary tumor (n=377)
0.08
Normal (n=32)
Primary tumor (n=473)
LUSC p=1.62536650805123E-12
PRAD p=1.62447832963153E-12
READ
UCEC
0.14
013
Q13
p=2.97699975781995E-10
0.16
p=1.62436730732907E-12
0.13
0.12-
0.12-
4.14
0.12-
Beta value
Beta value
Q11-
Beta value
Q#1
Beta value
Q11
4.12
0.1
0.1
0.1
0.09
0.09-
a.m
0.08
0.08
0.08
0.08
0.07
Normal (n=42)
Primary tumor (n=370)
0.07
Normal (n=50)
Primary tumor (n=502)
0.07
Normal (n=7)
Primary tumor (n=98)
0.06
Normal (n=46)
Primary tumor (n=438)
was significantly increased (Fig. 5; Supplementary Figure S6). These findings suggest that altered promoter methyla- tion may play a role in the transcriptional expression of EBNA1BP2.
3.5 Immune infiltration analysis data
Studies have demonstrated that immune cell infiltration is closely associated with various tumor behaviors, including tumor occurrence and development [22, 23]. Therefore, we utilized multiple algorithms such as TIMER, EPIC, QUAN- TISEQ, XCELL, MCPCOUNTER, CIBERSORT, CIBERSORT-ABS, and TIDE to investigate the correlation between EBNA1BP2 expression and immune cell infiltration in pan-cancer. In LUSC, we observed an inverse correlation between EBNA1BP2 expression and B-cell infiltration (Fig. 6A). Additionally, in BRCA, STAD, TGCT, and THCA, there was an inverse correlation between cancer-associated fibroblast infiltration and EBNA1BP2 expression (Fig. 6B). In UVM, EBNA1BP2 expression was positively correlated with CD8+T cell infiltration (Fig. 6C). Furthermore, we found that EBNA1BP2 expression was posi- tively correlated with neutrophils in KIRC and negatively correlated with monocytes in THCA (Supplementary Figure S7; Supplementary Figure S8A-E). These findings suggest that EBNA1BP2 may have potential as a tumor immune-related biomarker in the future.
3.6 Single cell sequencing data analysis
To further validate the potential functions of the candidate genes at the level of the single cell, we used the Cancer- SEA tool to investigate the correlation of EBNA1BP2 gene expression with the function of cancer cells in pan-cancer. EBNA 1BP2 expression was positively correlated with angiogenesis, differentiation, and inflammation in retinoblastoma (RB). Conversely, it was inversely correlated with cell cycle, DNA damage response, and DNA repair response. In uveal melanoma (UM), EBNA1BP2 expression showed negative correlations with almost all tumor biological behaviors, includ- ing apoptosis, DNA repair response, invasion, and metastasis (Fig. 7A). Furthermore, Fig. 7B shows significant correlations between EBNA1BP2 expression and angiogenesis differentiation and DNA repair in RB, DNA repair and DNA damage in UM, and quiescence in LUAD. And then, EBNA1BP2 single-cell expression profile of RB, UM and LUAD by T-SNE plots is also shown in Fig. 7C.
Discover
A
B
C
B cell
Cancer associated fibroblast
T cell CD8+
B coll QUANTISEQ
B coll_MCPCOUNTER
B cell memory_CIBERSORT
B coll memory_CIBERSORT-ABS
B cell memory XCELL
B coll naive_CIBERSORT
B cell naive CIBERSORT-ABS
B coll naive_XCELL
B cell plasma CIBERSORT
B cell plasma_CIBERSORT-ABS
B cell plasma XCELL
Class-switched memory B cell_XCELL
Cancer associated fibroblast EPIC
Cancer associated fibroblast MCPCOUNTER
Cancer associated fibroblast_XCELL
Cancer associated fibroblast_TIDE
B cell TIMER B cell_EPIC
B cell XCELL
T cell CD8+ TIMER T cell CD8+_EPIC
T cell CD8+ MCPCOUNTER
T cell CD8+ CIBERSORT
T cell CD8+_CIBERSORT-ABS
T cell CD8+ QUANTISEQ
T cell CD8+ XCELL
T cell CD8+ naive XCELL
T cell CD8+ central memory_XCELL
T cell CD8+ effector memory_XCELL
ACC (n=79)
ACC (n=79)
☒
X
BLCA (n=408)
BLCA (n=408)
BRCA (n=1100)
ACC (n=79)
BRCA (n=1100)
BRCA-Basal (n=191)
☒
BLCA (n=408)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
☒
☒
☒
BRCA (n=1100)
BRCA-Her2 (n=82)
BRCA-LumA (n=568)
BRCA-Basal (n=191)
BRCA-LumA (n=568)
BRCA-LumB (n=219)
BRCA-Her2 (n=82)
BRCA-LumB (n=219)
CESC (n=306)
BRCA-LumA (n=568)
CESC (n=306)
CHOL (n=36)
☒
BRCA-LumB (n=219)
CHOL (n=36)
☒
☒
X ☒
COAD (n=458)
CESC (n=306)
COAD (n=458)
DLBC (n=48)
CHOL (n=36)
☒
DLBC (n=48)
ESCA (n=185)
COAD (n=458)
ESCA (n=185)
GBM (n=153)
☒
DLEC (n=48)
GBM (n=153)
HNSC (n=522)
ESCA (n=185)
HNSC (n=522)
HNSC-HPV- (n=422)
GEM (n=153)
HNSC-HPV+ (n=98)
☒
HNSC (n=$22)
HNSC-HPV- (n=422)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
☒
KICH (n=66)
☒
p > 0.05
HNSC-HPV+ (n=98)
KICH (n=66)
X ☒
p > 0.05
KIRC (n=533)
KICH (n=66)
KIRP (n=290)
p … 0.05
☒ p > 0.05
KIRC (n=533)
KIRC (n=533)
KIRP (n=290)
p … 0.05
LGG (n=518)
KIRP (n=290)
p … 0.05
LGG (n=516)
LIHC (n=371)
LGG (n=516)
LIHC (n=371)
LUAD (n=515)
Partial_Cor
LIHC (n=371)
LUAD (n=515)
Partial_Cor
LUSC (n=501)
1
LUAD (n=515)
Partial_Cor
LUSC (n=501)
1
MESO (n=87)
LUSC (n=501)
1
MESO (n=87)
0
0
OV (n=303)
MESO (n=87)
PAAD (n=179)
-1
OV (n=303)
0
OV (n=303)
PAAD (n=179)
-1
PCPG (n=181)
PAAD (n=179)
-1
PCPG (n=181)
PRAD (n=498)
PCPG (n=181)
PRAD (n=498)
READ (n=166)
☒
PRAD (n=498)
READ (n=166)
SARC (n=260)
READ (n=166)
SARC (n=260)
SKCM (n=471)
SARC (n=260)
SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM (n=471)
SKCM-Primary (n=103)
SKCM-Metastasis (n=368)
SKCM-Metastasis (n=368)
STAD (n=415)
SKCM-Primary (n=103)
SKCM-Primary (n=103)
STAD (n=415)
TGCT (n=150)
STAD (n=415)
TGCT (n=150)
TGCT (n=150)
THCA (n=509)
THYM (n=120)
THCA (n=509)
THCA (n=509)
THYM (n=120)
THYM (n=120)
UCEC (n=545)
UCEC (n=545)
UCEC (n=545)
UCS (n=57)
UCS (n=57)
UCS (n=57)
X
UVM (n=80)
UVM (n=80)
UVM (n=80)
3.7 Gene enrichment analysis
Finally, we combined all of the tumor expression data from the TCGA from the GEPIA2.0 tool to obtain the top 100 genes associated with the expression of EBNA1BP2 (Supplementary Table S1). Subsequently, functional enrichment analysis was performed to evaluate these potential mechanisms. Using the BioGRID network tool, we identified 11 molecules that inter- act with EBNA1BP2 (Fig. 8A). Notably, EBNA 1BP2 expression showed a high correlation with Homo sapiens peptidylprolyl isomerase H (PPIH), diphthamide biosynthesis 2 (DPH2), Homo sapiens Y-box in most cancers binding protein 1 (YBX1), Homo sapiens MRT4 homolog, ribosome maturation factor (MRTO4), Homo sapiens mediator complex subunit 8 (MED8), and Homo sapiens cell division cycle 20 (CDC20) (Fig. 8B, C). Furthermore, GO and KEGG enrichment analysis revealed that the genes associated with EBNA1BP2 expression were primarily related to the nucleoplasm and RNA binding pathways (Fig. 8D). These findings suggest that EBNA 1BP2 may play a role in tumorigenesis and development through these pathways.
3.8 The expression of genes in cancer and normal tissue
In addition, we detected the expression of EBNA1BP2 in clinical samples of LUAD and normal lung tissue using RT-PCR method. The results showed that the expression of the EBNA1BP2 was upregulated in lung cancer tissues compared to normal tissues (Supplementary Figure S9).
Discover
A
B
AML
*
**
*
**
**
**
**
ALL
**
*
**
*
CML
**
**
**
**
**
**
**
**
**
*
* p<0.05
geneExp
GBM
**
**
**
**
**
*
** p<0.01
Glioma
**
**
**
**
**
**
*
*
Correlation
Pvalue
AST
Correlation
**
Angiogenesis
0.42
*
*
HGG **
**
1.0
**
**
*
*
**
**
**
**
**
ODG
**
**
**
*
**
**
*
**
LUAD
**
**
0.5
Differentiation
*
0.40
*
**
**
**
**
**
*
**
NSCLC
**
**
**
**
**
**
**
**
**
**
**
**
MEL
**
**
**
*
**
**
**
**
*
**
*
DNArepair
-0.48
RCC
0
*
*
*
**
**
*
*
BRCA
**
**
**
*
**
Correlation
Pvalue
PC
*
*
*
*
**
*
*
**
-0.5
DNArepair
-0.47
HNSCC
*
**
**
**
**
**
**
**
**
OV
*
**
*
**
CRC
-1.0
DNAdamage
-0.45
RB
**
**
**
**
**
**
**
**
**
**
**
**
**
UM
**
**
**
**
**
**
**
**
**
**
**
**
**
**
Angiogenesis
Apoptosis
Cellcycle
Differentiation
DNAdamage
DNArepair
EMT
Hypoxia
inflammation
Invasion
Metastasis
Proliferation
Quiescence
Stemness
Correlation
Pvalue
Quiescence
-0.51
C
Expression distribution with t-SNE plot
Expression distribution with t-SNE plot
Expression distribution with t-SNE plot
75
50
7.5
50
5
25
tSNE2
25
tSNE2
tSNE2
2.5
0
0
0
-25
-2.5
-25
-50
-5
-75
-50
-7.5
-60
-40
-20
0
20
40
60
-60
-40
-20
0
20
40
60
-7.5
-5
-2.5
0
2.5
5
7.5
tSNE1
tSNE1
tSNE1
RB
UM
LUAD
4 Discussion
Traditional research suggests that EBNA 1BP2 primarily functions in EBV latent infections [24]. However, its expression and specific functions in malignant tumors are still not well understood. In this study, we conducted a comprehensive analysis of the EBNA1BP2 gene in 33 different types of tumors. Our findings revealed a significant upregulation of EBNA1BP2 expression in various tumor tissues, including BLCA, BRCA, CESC, COAD, ESCA, and HNSC. Survival analysis demonstrated that low expression of EBNA1BP2 was associated with better survival outcomes in certain tumors such as ACC, BLCA, LGG, LIHC, and others. These findings suggest that EBNA1BP2 has the potential to serve as a biomarker for predicting the prognosis of cancer patients. However, further studies are needed to validate the prognostic value of EBNA1BP2 in different types of cancer.
EBV is the first human tumorigenic virus identified to establish lifelong asymptomatic persistence and is associated with a wide range of diseases, including benign disease, lymphoid malignancies, and epithelial cancers. Through various EBV coding genes, EBV can establish latent infection in host cells, which contributes to the development of related diseases. Among these genes, EBNA1 is the only virus-encoded gene expressed in all EBV-associated tumors [25, 26]. Previous studies have shown that EBNA1BP2 interacts with EBNA1, leading us to hypothesize that EBNA1BP2 is also closely involved in tumorigenesis [27]. Furthermore, previous research has demonstrated that EBNA1BP2 is expressed ubiquitously in human tissues and is highly expressed in Burkitt’s lymphoma, melanoma, lung cancer, and colorectal cancer [28]. Another study shows that EBNA1BP2 is overexpressed in most tumors, but not in kidney tumors [29]. In our study, we not only found that EBNA 1BP2 has a similar expression pattern in lung cancer and colorectal
Discover
A
B
p-value = D
·
h
p-value = 0
p-value = 0
:
.
R = 0.71
R = 0.68
₦
R = 0.66
00
-
SCYL2
:
log2(DPH2 TPM)
HNRNPK
log2(PPIH TPM)
log2(YBX1 TPM)
-
FANCD2
4
-
4
0
2
4
N
RPL15
ATXN1
-
2
1
.
0
-
.
0
2
4
€
8
0
2
č
e
6
¢
2
4
6
8
EBNA1BP2
log2(EBNA1BP2 TPM)
log2(EBNA1BP2 TPM)
log2(EBNA1BP2 TPM)
7%
p-value = D
-
p-value = 0
p-value = 0
TOP1
RPS6
R = 0.63
R = 0.63
2
R = 0.63
.
log2(MRTO4 TPM)
₱
4
log2(MED8 TPM)
log2(CDC20 TPM)
-
.
+
-
-
4
CCNF
EED
2
2
₦
RPL6
RPL7A
0
.
0
¥
·
.
0
2
4
6
8
0
2
4
8
8
0
2
4
E
6
log2(EBNA1BP2 TPM)
log2(EBNA1BP2 TPM)
log2(EBNA1BP2 TPM)
C
D
Spearman_Cor
mRNA splicing
·
rRNA processing
-log,(pvalue)
SKCM-Metastasis (n=368)
p > 0.05
p … 0.05
mitotic spindle organization
20
1
0
-1
16
cell division
12
cytosol
8
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)
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)
cytoplasm
count
10
spliceosome
20
30
CDC20
nucleus
40
50
DPH2
RNA binding
60
MED8
MRTO4
nucleoplasm
PPIH
YBX1
Oe+001e-062e-063e-064e-06
cancer based on public databases, but also observed differential expression of EBNA1BP2 in various tumors com- pared to normal tissues, such as BRCA, KIRC, LIHC, and others. These findings strongly indicate that the expression of EBNA 1BP2 plays a crucial role in tumor incidence and development.
EBNA 1BP2 can also serve as a prognostic predictor for some malignant tumors. Studies have shown that EBNA1BP2 is associated with a poorer prognosis in high-grade bladder cancer [30]. According to the TCGA database in GEPIA2.0, low expression level of EBNA1BP2 was significantly correlated with the increase of overall survival in ACC, BLCA, LGG, LIHC, MESO, SARC and UCS. Similarly, EBNA 1BP2 low expression also showed statistically significant differences with increased disease-free survival in ACC, HNSC, KIRP, LGG, PRAD, SARC and UCS. We also obtained from Kaplan-Meier plotter data that low expression of EBNA1BP2 in BLCA, LIHC, LUAD, SARC, and UCEC was associated with overall survival improve- ment. Overall, the combined analysis of GEPIA2.0 and Kaplan-Meier plotter data showed that low EBNA1BP2 expression was significantly associated with longer overall survival in BLCA, LIHC, and SARC. In addition to TCGA database, we also used PrognoScan based on GEO database to obtain the prognostic relationship of EBNA1BP2 expression in tumors. The results showed that EBNA 1BP2 expression was significantly associated with bladder, brain, and lung cancer, and we also found that low EBNA1BP2 expression was significantly associated with increased OS.
Discover
Immune cell infiltration plays a crucial role in the malignant behavior of tumors and the immune microenvironment [31]. Li et al. discovered that the proportion of plasma cells, CD4 activated memory T cells, and follicular helper T cells increased as the expression of EBNA1BP2 elevated in COVID-19 samples [32]. Another study demonstrated that the expression of EBNA 1BP2 was positively correlated with mast cells and negatively correlated with B cells in vitiligo [33]. These studies indirectly suggest a relationship between EBNA1BP2 expression and lymphocyte infiltration, but there is a lack of research on tumor cells. In our study, we observed a positive correlation between EBNA1BP2 expression and infiltration of B cells, cancer-associated fibroblasts, and CD8+T cells in several tumor types. Our results indicated the underlying effect of EBNA1BP2 in tumor immunity and it may be an effective target for immunotherapy and provide new hope for treating cancer patients in the clinic.
EBNA 1BP2 is a highly conserved protein in eukaryotes and is predicted to form coiled-coil interactions and it is con- tributed to the biogenesis of human ribosomes because it was identified in the nucleolus of Hela cells [34, 35]. Studies suggest that EBNA1BP2 is a novel binding partner of c-Myc, which regulates nucleolar c-Myc function, cell proliferation, and tumorigenesis through positive feedback [9, 36]. However, the specific function of EBNA1BP2 in tumorigenesis remains unclear. Previous research has reported that EBNA1BP2 interacts with nucleophosmin-anaplastic lymphoma kinase (NPM-ALK) in the nucleolus and promotes the proliferation of anaplastic large-cell lymphoma (ALCL) cells. Knock- down of EBNA 1BP2 has been shown to activate the tumor suppressor p53 [10]. Additionally, EBNA1BP2 has been found to destabilize p53 by inhibiting its interaction with HAUSP/USP7, a deubiquitination enzyme for p53 [37, 38]. However, the detailed roles and underlying mechanisms of EBNA1BP2 in human tumors require further exploration. In this study, we performed gene enrichment analysis and found that the genes expressed by EBNA1BP2 were mainly associated with nucleoplasmic and RNA binding pathways. We also investigated the correlation between EBNA1BP2 gene expression, methylation levels, and mutation frequency in pan-cancer. These results will provide a foundation for understanding the role and molecular mechanisms of EBNA1BP2 in malignant tumors.
In conclusion, our comprehensive bioinformatics analysis using various tools and databases explored the expression level, clinical prognosis, genetic mutations, methylation levels, immune cell infiltration, and pathway mechanisms of EBNA 1BP2 in pan-cancers. Our findings suggest that EBNA1BP2 may serve as a potential novel biomarker associated with the prognosis of cancer patients. Furthermore, this study lays the foundation for further investigation into the mechanisms underlying the occurrence, development, and treatment of pan-cancer involving EBNA1BP2.
5 Limitation
Despite our integration of information from multiple website tool, this research remain has limitations. Firstly, this study relies on bioinformatics analysis and lacks validation of expression and prognosis using clinical samples from various cancer types. Second, we lack in vitro/in vivo experiments to further verify the mechanism of EBNA1BP2.
Acknowledgements All authors are grateful for the GEO and TCGA databases to providing public data and all the bioinformatics tools used to analyze data.
Author contributions LS, and YJ took part in article conception and design. QX, KX and XX acquired the data. XZ and JS analyzed and interpreted the data. LS, and YJ wrote the manuscript. LL and XZ supervised the whole research. All authors contributed to the article and approved the submitted version.
Funding This study was supported by National Natural Science Foundation of China (No. 82302640), the Guangzhou Science and Technol- ogy Plan Project (Grant Nos. 202002030404 & 2023A04J1129), the Foundation of Guangdong Second Provincial General Hospital (Grant No. 3DA2021015), Doctoral Workstation Foundation of Guangdong Second Provincial General Hospital (Grant No. 2021BSGZ018), the science foundation of Guangdong Second Provincial General Hospital (Grant No. TJGC-2021007), Guangdong Medical Scientific Research (Grant No. B2023038), and Natural Science Foundation of Guangdong Province (Grant No. 2021A1515012329).
Data availability Publicly datasets were analyzed in this study can be found in online. This date can be found in the article and supplementary material.
Declarations
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
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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/.
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