Diffuse large B-cell lymphoma (DLBCL) is highly aggressive and fatal hematological malignancy. There are few biomarkers that can be used to predict the survival of DLBCL patients. Therefore, there is an urgent need to find new biological targets to improve the predictive value and sensitive diagnosis of DLBCL.E2F family play an essential role in tumorigenesis, however, remains obscure in DLBCL.E2F transcription factor family(E2Fs) mRNA expression between DLBCL and nonmalignant samples were screened by GEPIA,CCLE and EMBL-EBI. The associated regulation pathway in DLBLC was established using the GeneMANIA,Metascape ,SMATAPP database. Transcription analysis indicated E2F1/4/5/8 mRNA expression was significantly higher in patients and the cell lines.What’s more, the high E2F5/8 expression had significantly lower survival rate. Further functional analysis showed that E2F1/3/5 were hypomethylated in DLBCL,which may associated with patient chemo-resistance. Subsequently,these genes with their co-expression genes mainly formed transcription factor complex, regulated G1/S transition of mitotic cell cycle and through TGF-beta signaling pathway to participate DLBCL tumorigenesis. This results demonstrate that E2F5 were potential prognostic biomarkers for better survival of DLBCL patients.
Research Article
Multiple Omics Analysis Reveals E2F5 Predict Prognostic Biomarker in Diffuse Large B-Cell Lymphoma
https://doi.org/10.21203/rs.3.rs-996006/v1
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DLBCL is a heterogeneous malignancy molecularly can be classified into at least three distinct subtypes (ABC, GCB, PMBL)1. According to adverse outcomes,biomarkers including MYC/BCL2 rearrangement, and TP53 mutations implicated worse prognosis2. R-CHOP regimen indeed improved overall survival, but more than 30% of patients will ultimately relapse 3.The cell origin phenotype and gene mutation are suggested as predictors in DLBCL, however, due to the heterogeneity and complexity of lymphoma, there are still many unknown oncogenes and tumor suppressor genes that need to be discovered. Thus, finding reliable biomarkers that can accurately detection of DLBCL is necessary.
The E2F family has been considered as an important regulator of the cell cycle,which plays vital roles in regulating transcription and tumor suppressor proteins. E2F family members are divided into transcriptional activators or repressors 4. E2F1, E2F2 and E2F3A, as activator proteins, lead to adequate transition from the G1 to S phase and enhanced cellular proliferation 5 6. E2F3b and E2F4/5/6,which are ‘repressive’ E2F branches, repress the transcription in quiescent and early G1 cells7. As atypical repressors, E2F7/8 suppress E2F1-induced cell cycle8. The E2Fs mRNA expression is aberrant in several human malignancies, such as breast cancer 9, gastric cancer10, hepatocellular carcinoma11, lung adenocarcinoma12 and colon cancer13. To data, however, the overall biological role and clinical significance of the E2F factors in DLBCL remains not fully elucidated.
With the recent evolution of sequencing technologies, a vast amount sequencing data has been uploaded to the online repositories14. Here, we analyzed the unique expression patterns and significance for survival prognosis of eight E2Fs in DLBCL patients by bioinformatics.
Ethics statement
This research has been approved by the Ethics Committee of the First Affiliated Hospital of Xiamen University (Fujian, China). The TCGA and GTEx data was retrieved from published literature, and all procedures are implemented in accordance with relevant guidelines and regulations.
GEPIA Dataset Analysis
GEPIA (http://gepia.cancer-pku.cn/),which is a gene expression interactive analysis server,providing clinical and sequencing data from the TCGA and the GTEx projects. Meanwhile,it visualize this data by customizable functions depending on your need 15.
CCLE and EMBL-EBIDataset
The CCLE ( https://www.broadinstitute.org/ccle) and EMBL-EBI(https://www.ebi.ac.uk)conduct a detailed multi-omics analysis of thousands of cell lines,which provides reference for patient stratification16 17.
SMART APP
The SMART (Shiny Methylation Analysis Resource Tool) App combines multi-omics and clinical data with comprehensive DNA methylation analysis.It visualizes the relationship between gene expression and methylation in carcinoma 18.
Metascape
The Metascape (http://metascape.org),as a reliable, effective and intuitive tools,enables visualization analyses of multi-omic data. For this, it was utilized to conduct KEGG and GO(containing MF,BP,CC) analyses of E2Fs 19.
GeneMANIA analysis
GeneMANIA (http://www.genemania.org) uses genomics and proteomics data to predict genes with similar functions. Here it was employed for predict the function of E2Fs and co-expression genes in DLBCL 20.
Statistical analysis
The GSE31312 was analyzed by using R studio software version 1.2.1335. And it was used to verify OS and PFS between E2Fs expression of lymphoma patients.*p < 0.05, **p < 0.01, and ***p <0.001 mean statistically significant difference.
E2Fs were highly expressed in DLBCL in the mRNA level
To call DEGs of E2Fs family in DLBCL, we used the GEPIA dataset to compare the mRNA expression of DLBCL patient samples from TCGA tumors (n=47) and GTEx healthy tissues (n=337). We used log2 (TPM + 1) to quantity mRNA expression data for E2Fs and found that E2F1/4/5/6/8 were highly-expressed in DLBCL, while E2F2/3/7 were not significantly different. (Fig.1)
Expression of E2Fs translation factors in lymphoma cell lines
After determining the expression of E2Fs in clinical lymphoma patients, we analyzed E2Fs’ mRNA expression in cell lines through the CCLE and EMBL-EBI database. By assembling CCLE, we found that E2F1/2/4/5/8 were relatively highly expressed in DLBCL, comparing the average expression base line of all tumors (FigS1). Moreover, EMBL-EBI was employed to test mRNA level of E2F translation factors in DLBCL cell lines(n=21). E2F1/2/3/4/5/8 increased in most DLBCL cell lines (Fig.2), especially in SU-DHL-5, SU-DHL-10, WSU-NHL. Collectively, with the aim to further elucidate the major factors of E2Fs in DLCBL, three databases were searched for intersection of the predicted results. We found that E2F1/4/5/8 overexpress in the GEPIA, CCLE and EMBL-EBI. (Fig. 3)
Prognostic Values of E2F1/4/5/8 in Patients With DLBCL
Next, we initially assessed the E2F1/4/5/8 prognosis values in 471 DLBCL patients (GSE31312). It was determined that the higher expression of E2F1 mRNA predicts extended OS and PFS((p=0.0015,p=0.02, respectively). Elevated E2F5/8 were significantly correlated with shortened OS (p=0.0051,0.015, respectively) and PFS (p=0.019,0.15, respectively). (Fig.4). Next, multivariate Cox’s hazard regression analysis results indicated E2F1 (HR = 1.45, 95%CI = 1.141–1.841, P = 0.002), E2F5 (HR = 0.74, 95% CI = 0.584-0.938, P = 0.013), E2F8 (HR = 1.607, 95%CI = 1.103-2.343, P = 0.014) expression level are independent prognostic factors for overall survival in DLBCL patients (Table1).
Functional enrichment analysis of E2F1/4/5/8 and co-expression genes in patients with DLBCL
To reveal the function and potential mechanism of E2F1/4/5/8, we constructed a network of E2F1/4/5/8 and their neighboring genes (TFDP1/2, RBL1/2, E2F2) by the GeneMANIA database(Fig5A). By analyzing Metascape, we found the E2F1/4/5/8 and their neighboring genes were mainly enriched in G1/S cell cycle. These genes may form protein-DNA complex.Then they enhanced the interaction between RNA polymerase and specific promoters to promote targeted gene expression by changing the structure of DNA.(Fig.5B and Table2).
The top KEGG pathways showed that the cell cycle signaling pathway and TGF-beta signaling pathway were significantly found to be involved in the development of multiple tumor and participated in the tumorigenesis and pathogenesis of lymphoma.(Fig.5C and Table 3.) In addition, to further understand the role of E2Fs in lymphoma, we conducted a relevant protein–protein interaction (PPI) analysis(Fig.5D). The results showed that E2F2/3/4/5, RBL1/2, TFDP1/2, CDK2 mainly formed transcription factor complex, regulated the maligant proliferation of tumor through G1/S transition of mitotic cell cycle and TGF-beta signaling pathway.
E2Fs expression is negatively regulated by DNA methylation
In diffuse large B-cell lymphoma, DNA methylation is the most widely studied epigenetic process leading to chemotherapy resistance21. In order to explore the transcriptional regulation of E2Fs family in DLBCL, we analyzed the 450 K methylation array data using SMARTAPP Databases to verify whether E2Fs expression may be regulated by their DNA methylation status. By comparing the Correlation between gene expression and methylation in 47 DLBCL patients (TCGA), The method for Correlation Coefficient is Spearman, we found that E2F1/3/5 expression is negative with methylation sites (p=0.00013,0.026,0.0011, respectively) (Fig.6)
Gene–gene interaction network of the E2F1/3/5 with the DNA methylation associated genes
Above, we found that E2F1/3/5 were hypomethylated in the TCGA databases. Previous studies indicated that human DNA methylation is catalyzed by DNA transmethylase including DNMT1/3A/3B 22. Therefore, we used the GeneMANIA database to predict the interaction relationship of the E2F1/3/5 and DNMT1/3A/3B (Fig.7). The 6 central nodes representing E2F1/3/5, DNMT1/3A/3B were surrounded by 20 nodes representing genes,which have similar functions(Fig.7A). E2F2/4, TFDP1/2 was correlated with central nodes, participating in the same reaction within a pathway. (Fig.7B). E2F2/4/6, TFDP1/2, TRDMT1, DNMT3L, DMAP1, ZBTB18 was correlated with central nodes, predicting functional relationships between protein interactions(Fig.7C). As for co-localization, E2F4 was correlated with E2F3/5, DNMT1(Fig.7D). In addition, HDGF, TFDP1/2 was correlated with E2F1/3, DNMT1 in terms of co-expression(Fig.7E).
In GEPIA Dataset, E2F1, E2F4, E2F5, E2F6, E2F8, TFDP1, HDGF and DNMT1 expression is higher in DLBCL patients (Fig.1, FigS2). Next, we found that the methylation of HDGF, PWWP2A, TFDP1, TRDMT1, E2F1, E2F3, E2F5 and DNMT1 were significantly negative with gene expression in SMART APP Databases. In conclusion, elevated E2F1, E2F5, TFDP1, DNMT1 and HDGF were negative with DNA methylation in DLBCL patients (Fig6,FigS3). In GSE31312, correlation analysis showed that E2F5 is positive correlation with TFDP1(R=0.18, P<0.001) and HDGF (R=0.13, P<0.01), DNMT1 is negative correlation with TFDP1(R=-0.16, P<0.001) and HDGF (R=-0.16, P<0.001) (Fig.8) in DLBCL patients.
DLBCL is the most common form of lymphoma. Current biological and clinical research shows that it is a highly heterogeneous and aggressive malignant tumor, which is manifested in clinical outcome, genetic characteristics, cell origin, etc.23. Although with the clinical application of the RCHOP treatment strategy, the cure rate of DLBCL has been significantly improved, 30-40% of patients still relapse24. Therefore, researches looking for biomarkers to predict clinical prognosis in DLBCL are necessary. Researches suggested that the E2F transcription factors family plays a key role in cell cycle network regulation, which regulate cell proliferation, differentiation, apoptosis, and participate in physiological and pathological processes. 25,26. However, the multifaceted roles of E2Fs in the development, metastasis, and prognostication of DLBCL remain to be clarified. Our study analyzed the transcription levels of E2F mRNA expression, potential pathways and prognostic (OS/PFS) values in DLBCL.
E2F1 preferentially binds to the retinoblastoma protein (pRB) in a cycle-dependent manner27. It can simultaneously mediate cell proliferation and p53-dependent or independent apoptosis 28. Our results indicated that the higher expression of E2F1 mRNA means extended OS and PFS. Møller found that E2F1 as tumor suppressor gene in DLBCL and low E2F1 expression was associated with treatment failure of DLBCL, which may serving as prognostic markers for DLBCL patients 29. The research results are consistent with ours. However, Samaka found that high E2F1 expression had shorter survival time of DLBCL cases and upregulation of E2F1 indicated that the tumor is more malignant 30. Therefore, more detail researches need to do to prove the relation between the E2F1 and DLBCL patient survival.
As an oncogene, E2F5 can cooperate with other oncogenes promote cell malignant transformation. E2F5 is up-regulated and shows significant correlation with pathological variables and tumorigenesis31. E2F5 repressors as effectors of RB that control cell proliferation and apoptosis8. Previous studies reveal that E2F5 was highly expressed in several tumors32, such as glioblastoma33, breast cancer 34 and prostate cancer35. In this study, we found that Elevated expressions of E2F5 were significantly correlated with shortened OS/PFS in DLBCL patients.
As a tumor suppressor, E2F8 mediates transcriptional suppression by blocking cells from entering S phase36. Over-expression of E2F8 activates target genes that may promote cell cycle, mitosis, immune and other cancer related functions in Burkitt’s lymphoma (BURK) and mantle cell lymphoma (MCL) 37. Our research showed that elevated expressions of E2F8 were significantly correlated with shortened OS in DLBCL.
In our research, we revealed that E2F1/4/5/8 mRNA expression level were up-regulated in the patients and the cell lines of DLBCL. Further analysis showed that E2F2/3/4/5, RBL1/2, TFDP1/2, CDK2 mainly formed transcription factor complex participating cell cycle,and TGF-beta signaling pathway in DLBCL. Previous studies have shown that abnormally methylated genes affect its molecular expression regulation and patient’s survival, which found that gene expression is associated with methylation in DLBCL 38. We found that the E2F1/3/5 expression is negative with DNA methylation. Emerging studies have provided evidence that E2F and other transcription factors regulate DNMTs expression through E2F-Rb-HDAC-dependent and -independent pathways, which potentially contribute to tumor progression39. In our study, we found that elevated E2F1, E2F5, TFDP1, HDGF and DNMT1 were negative with DNA methylation in DLBCL patients. TFDP-1 and E2F factors jointly participate in the normal cell cycle procession. Somatic mutations in TFDP-1 uncouples the normal biological processes of the E2F pathway, which could lead to tumorigenesis40. HDGF upregulates in a variety of malignant tumors ,which is closely related to the tumor stage, grade and proliferation 41. Studies have found that cancers harboring mutant p53 genes are accompanied by high expression of HDGF42. In DLBCL patients, p53 mutations are accompanied by related gene expression dysregulation leading to poor prognosis43. In our research, we found that TFDP1and HDGF are positive correlation with E2F5, which is negative with DNMT1 expression in DLBCL patient databases. This result also consistent with observations in immortalized cell lines, indicating that only co-expressed with TFDP1,the transient overexpression of E2F5 can stimulate the cell proliferation31. We indicated that DNMT1-mediated DNA methylation of E2F5/TFDP1/HDGF ternary complex regulates prognosis in DLBCL patients. These deregulation makes cell cycle change and has prognostic significance in DLBCL.
We comprehensively analyzed the role of E2F transcription factors in the occurrence and development of DLBCL and its prognosis through bioinformatics. we found that E2F1/4/5/8 were over-expression in DLBCL cell lines and patients. And E2F2/3/4/5, RBL1/2, TFDP1/2, CDK2 mainly formed transcription factor complex, regulating cell cycle and TGF-beta signaling pathway in DLBCL. E2Fs expression and DNA methylation status showed that E2F1/3/5 expression might be negatively related to DNA methylation in DLBCL. These genes with DNA methylation genes were correlated with TFDP1 and HDGF, they may anticipate the pathogenesis of DLBCL. Furthermore, our study indicated that increased expression of E2F5 indicated worse prognosis in DLBCL, it may be potential targets for individualized treatment of DLBCL patients. In conclusion, this research demonstrates for the first time that E2F5 were potential prognostic biomarkers for better survival of DLBCL patients.
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Table I
Cox regression of E2F1, E2F4, E2F5, E2F8 in Patients with DLBCL
|
|
OS |
PFS |
||||
|
|
P |
HR |
95.0% CI |
P |
HR |
95.0% CI |
|
Age (≥60 vs. <60), years |
0.275 |
1.145 |
0.898-1.46 |
0.017* |
1.35 |
1.054-1.729 |
|
Sex (males vs. females) |
0.511 |
0.925 |
0.732-1.168 |
0.636 |
0.944 |
0.745-1.197 |
|
Stage (low vs. high) |
0.25 |
0.855 |
0.655-1.117 |
0.276 |
0.856 |
0.648-1.132 |
|
ECOG score (low vs. high) |
0.952 |
1.011 |
0.713-1.432 |
0.482 |
1.138 |
0.793-1.635 |
|
IPI score (low vs. high) |
0.552 |
1.099 |
0.805-1.499 |
0.261 |
1.198 |
0.874-1.642 |
|
Treatment response (CR+PR vs. PD+SD) |
0.011* |
2.758 |
1.258-6.047 |
0.022* |
2.433 |
1.135-5.214 |
|
Subtype (non-GCB vs. GCB) |
0.798 |
1.031 |
0.813-1.398 |
0.437 |
1.1 |
0.865-1.4 |
|
E2F1 expression (low vs. high) |
0.002* |
1.45 |
1.141-1.841 |
0.016* |
1.652 |
1.097-2.486 |
|
|
OS |
PFS |
||||
|
|
P |
HR |
95.0% CI |
P |
HR |
95.0% CI |
|
Age (≥60 vs. <60), years |
0.22 |
1.164 |
0.913-1.484 |
0.03* |
1.315 |
1.027-1.684 |
|
Sex (males vs. females) |
0.654 |
0.948 |
0.751-1.197 |
0.494 |
0.92 |
0.724-1.168 |
|
Stage (low vs. high) |
0.471 |
0.907 |
0.697-1.182 |
0.404 |
0.889 |
0.674-1.172 |
|
ECOG score (low vs. high) |
0.567 |
1.108 |
0.779-1.575 |
0.706 |
1.07 |
0.751-1.525 |
|
IPI score (low vs. high) |
0.869 |
1.026 |
0.753-1.399 |
0.387 |
1.15 |
0.838-1.578 |
|
Treatment response (CR+PR vs. PD+SD) |
0.022* |
2.485 |
1.138-5.427 |
0.058* |
2.057 |
0.976-4.334 |
|
Subtype (non-GCB vs. GCB) |
0.509 |
1.083 |
0.855-1.372 |
0.41 |
1.106 |
0.87-1.408 |
|
E2F4 expression (low vs. high) |
0.205 |
1.271 |
0.877-1.841 |
0.031* |
0.767 |
0.603-0.976 |
|
|
OS |
PFS |
||||
|
|
P |
HR |
95.0% CI |
P |
HR |
95.0% CI |
|
Age (≥60 vs. <60), years |
0.29 |
1.139 |
0.895-1.451 |
0.021 |
1.339 |
1.044-1.718 |
|
Sex (males vs. females) |
0.531 |
0.928 |
0.734-1.173 |
0.668 |
0.949 |
0.748-1.204 |
|
Stage (low vs. high) |
0.411 |
0.895 |
0.687-1.166 |
0.353 |
0.876 |
0.663-1.158 |
|
ECOG score (low vs. high) |
0.469 |
1.138 |
0.802-1.616 |
0.872 |
1.03 |
0.723-1.465 |
|
IPI score (low vs. high) |
0.925 |
1.015 |
0.745-1.383 |
0.287 |
1.188 |
0.865-1.632 |
|
Treatment response (CR+PR vs. PD+SD) |
0.035* |
2.325 |
1.063-5.089 |
0.081* |
1.947 |
0.921-4.116 |
|
Subtype (non-GCB vs. GCB) |
0.475 |
1.09 |
0.861-1.38 |
0.319 |
1.131 |
0.888-1.439 |
|
E2F5 expression (low vs. high) |
0.013* |
0.74 |
0.584-0.938 |
0.317 |
0.831 |
0.578-1.195 |
|
|
OS |
PFS |
||||
|
|
P |
HR |
95.0% CI |
P |
HR |
95.0% CI |
|
Age (≥60 vs. <60), years |
0.158 |
1.192 |
0.934-1.52 |
0.025* |
1.329 |
1.037-1.704 |
|
Sex (males vs. females) |
0.634 |
0.945 |
0.748-1.193 |
0.537 |
0.928 |
0.731-1.177 |
|
Stage (low vs. high) |
0.476 |
0.908 |
0.696-1.184 |
0.229 |
0.842 |
0.636-1.114 |
|
ECOG score (low vs. high) |
0.61 |
1.094 |
0.774-1.548 |
0.807 |
0.957 |
0.672-1.363 |
|
IPI score (low vs. high) |
0.729 |
1.056 |
0.775-1.44 |
0.198 |
1.231 |
0.897-1.69 |
|
Treatment response (CR+PR vs. PD+SD) |
0.009* |
2.885 |
1.31-6.352 |
0.036* |
2.233 |
1.056-4.721 |
|
Subtype (non-GCB vs. GCB) |
0.721 |
1.044 |
0.824-1.324 |
0.214 |
1.166 |
0.915-1.486 |
|
E2F8 expression (low vs. high) |
0.014* |
1.607 |
1.103-2.343 |
0.011* |
0.731 |
0.575-0.93 |
OS: Overall Survival; PFS: progression Free Survival; ECOG: Eastern Cooperative Oncology Group; IPI: International Prognostic Index; CR: Complete Response; PR: Partial Response; PD: Progressive Disease; SD: Stable Disease; GCB: germinal center B-cell-likely lymphoma; *: p < 0.05.
Top 8 clusters with their representative enriched terms (one per cluster). "Count" is the number of genes in the user-provided lists with membership in the given ontology term. "%" is the percentage of all of the user-provided genes that are found in the given ontology term (only input genes with at least one ontology term annotation are included in the calculation). "Log10(P)" is the p-value in log base 10. "Log10(q)" is the multi-test adjusted p-value in log base 10.
Table III:
KEGG Analysis of E2F1/4/5/8 and co-expression genes in Patients with DLBCL using Metascape
|
GO |
Category |
Description |
Count |
% |
Log10(P) |
Log10(q) |
|
hsa04110 |
KEGG Pathway |
Cell cycle |
10 |
45.45 |
-17.31 |
-14.61 |
|
hsa04350 |
KEGG Pathway |
TGF-beta signaling pathway |
5 |
22.73 |
-7.96 |
-5.57 |
|
hsa05203 |
KEGG Pathway |
Viral carcinogenesis |
5 |
22.73 |
-6.07 |
-3.95 |
|
hsa05222 |
KEGG Pathway |
Small cell lung cancer |
4 |
18.18 |
-6.04 |
-3.95 |
|
hsa05222 |
KEGG Pathway |
Small cell lung cancer |
4 |
18.18 |
-6.04 |
-3.95 |
Top 4 clusters with their representative enriched terms (one per cluster). "Count" is the number of genes in the user-provided lists with membership in the given ontology term. "%" is the percentage of all of the user-provided genes that are found in the given ontology term (only input genes with at least one ontology term annotation are included in the calculation). "Log10(P)" is the p-value in log base 10. "Log10(q)" is the multi-test adjusted p-value in log base 10.
No competing interests reported.
- SupplementaryFigure1.pdf
Supplementary Figure 1
- SupplementaryFigure2.pdf
Supplementary Figure 2
- SupplementaryFigure3.pdf
Supplementary Figure 3