We first explored the expression of E2Fs in EC using UALCAN (http://ualcan.path.uab.edu/index.html, a bioinformatics tool developed by Chandrashekar et al. (Chandrashekar et al., 2017). UALCAN is a bioinformatics tool for analyzing and visualizing TCGA. Thereafter, we used the same tool to analyze the expression of E2F family and its correlation with multiple clinical pathological parameters, including tumor grade, cancer stage, race among other factors, in EC. The analyses were performed on data extracted from the TCGA_UCEC (Uterine corpus endometrial carcinoma, n = 546) dataset P-value < 0.05 was considered statically significant.

A total of 27 EC tissues and 20 normal endometrial tissues were obtained from patients who underwent a hysterectomy or endometrial curettage for endometrial-irrelevant diseases in Shengjing Hospital of China Medical University, China, from 2017 to 2019. All patients provided informed consent, and this study was approved by the Ethics Committee of Shengjing Hospital of China Medical University. Histological diagnosis and tumor grade were assessed by three experienced pathologists in accordance with the International Federation of Gynecology and Obstetrics (FIGO 2009). None of the patients received any local or systemic treatment preoperatively.

Total RNA of EC tissues and normal endometrial tissues were extracted with TRIzol reagent (Vazyme, Nanjing, China). The synthesis of cDNAs corresponding to the mRNAs of interest depended on PrimeScript RT-polymerase (Vazyme). SYBR-Green Premix (Vazyme) with specific PCR primers (Sangon Biotech Co., Ltd., Shanghai, China). Glyceraldehyde-3-phosphate dehydrogenase was used as an internal control. The 2-ΔΔCt method was used to calculate fold-changes. Primer sequences are shown in Supplementary Table 1. The difference of the expression of E2Fs in tumor tissues and normal tissues were evaluated with Student’s t-test in GraphPad Prism7 software (GraphPad, Inc., La Jolla, CA, USA).

GEPIA (http://gepia.cancer-pku.cn/about.html) is a web-based bioinformatics tool developed by Tang, Z. et al. (Tang et al., 2017), utilized for fast and customized analysis of gene sets. We performed differential expression analysis of RNA sequences extracted from TCGA and the GTEx datasets. GEPIA is a bioinformatics tool for analyzing and visualizing TCGA. GEPIA facilitated the analysis of relative expression of E2Fs between UCEC and normal tissues. The correlation between each member of E2F family and UCEC was also performed using the same tool, based on TCGA_UCEC (n = 546) dataset in GEPIA.

Kaplan Meier-plotter (KM-plotter, http://www.kmplot.com/) is a also an online bioinformatic tool developed for comprehensive prognosis analyses (Hou et al., 2017). KM-plotter is a bioinformatics tool for analyzing and visualizing TCGA. It was utilized to explore the prognostic value of E2Fs in patients with UCEC. Here, the patients were stratified into low or high expression group, with the median value of E2F family set as the cutoff score. The same tool was utilized in generate the OS and relapse-free survival (RFS) curves for E2Fs in UCEC, based on the previously described data. A p-value < 0.05 was considered statically significant.

GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) is another bioinformatics tool, developed by An-Yuan Guo et al. (Liu C. J. et al., 2018). GSCAlite is a bioinformatics tool for analyzing and visualizing TCGA. It was utilized in assessing the Methylation module of E2Fs under UCEC, and the corresponding correlation of gene expression and patients’ survival. Differences in the methylation patterns between UCEC and normal tissues were assessed using Student t-test. The p-value was adjusted by false discovery rate (FDR), with TFDR < 0.05 considered to be statistically significant. The frequency and variant types of E2Fs in UCEC were displayed graphically using single nucleotide variation (SNV). Meanwhile, a summary of SNV and Oncoplot waterfall plot was generated using maftools (Mayakonda and Koeffler, 2016). The correlation between members E2F family and pathway activity were assessed using Pathway Activity module. The pathway scores were awarded as previous described (Ye et al., 2018). The correlation between members of E2F family and drug sensitivity was analyzed using the drug-sensitivity module. The linear correlation between the expression of E2Fs and the 265 small molecules from Genomics of Drug Sensitivity in Cancer (GDSC) was analyzed using the Pearson correlation coefficient.

cBioportal (https://www.cbioportal.org/) is another bioinformatics tool developed for comprehensive genomics analysis (Zeng et al., 2019). cBioportal is a bioinformatics tool for analyzing and visualizing TCGA. In particular, the platform was used to analyze 546 UCEC in TCGA_dataset patients. The mRNA expression z scores (RNA Seq V2 RSEM) were obtained using a z score threshold of ±2.0. Protein expression z scores (RPPA) were obtained using a z score threshold of ±2.0. “Survival” module explored the effects of genetic alteration E2F members on the prognosis of patients. Meanwhile, the “Network” module explored the correlation between E2Fs and genes adjacent to UCEC.

TIMER (http://cistrome.org/TIMER/) is a bioinformatics tool developed for analysis of immune infiltrations (Li et al., 2016). TIMER is a bioinformatics tool for analyzing and visualizing TCGA. It was used to explore the correlation between members of E2F family and infiltration of immune cells in UCEC. Significance difference in somatic copy number in relation to alterations in E2F genes was analyzed using a two-sided Wilcoxon rank-sum test. The analysis was performed based on the TCGA-UCEC dataset (n = 546). P-value < 0.05 was considered statically significant.

Enrichment analysis of E2Fs in UCEC was performed using GeneMANIA, DAVID V 6.8 and Metascape (Zeng et al., 2019). We first established significant genes adjacent to E2Fs using GeneMANIA correlation genes analysis. Thereafter, E2F gene family and neighboring associated genes were then analyzed using DAVID 6.8 and Metascape for the GO and KEGG pathways. The results were visualized with R project using a “ggplot2” package and a p < 0.05. Biological processes (BPs), cellular components (CCs), and molecular function (MF) were all assessed in the GO enrichment analysis. In Metascape, the analysis was performed with min overlap of 3, P-value of 0.01, and min enrichment of 1.5.

LinkedOmics (http://www.linkedomics.org/) is a bioinformatics tool that provides comprehensive multiomics data analysis across 32 TCGA cancer types (Zeng et al., 2019). LinkedOmics is a bioinformatics tool for analyzing and visualizing TCGA. The “LinkInterpreter” module of LinkedOmics was used to derive biological insights into kinase and miRNA target for E2F family in UCEC. Gene Set Enrichment Analysis (GSEA) was performed with a minimum number of genes (size) of 3 and a simulation of 500, within the TCGA UCEC dataset (n = 546). Results were analyzed statistically using the Spearman correlation test.

In this study, we first explored expression of E2Fs in UCEC using UALCAN. Our analysis revealed significant over-expression of E2F1 (Figure 1A, P < 1E-12), E2F2 (Figure 1B, P = 1.62E-12), E2F3 (Figure 1C, P = 1.62E-12), E2F5 (Figure 1E, P = 3.0E-15), E2F7 (Figure 1G, P = 1.62E-12), and E2F8 (Figure 1H, P = 1.62E-12) in UCEC tissues, converse to E2F4 (Figure 1D, P = 2.02E-12), which was downregulated in the same tissues. We then verified the expression E2Fs in UCEC using qRT-PCR. As a result, the expression of E2F1 (Figure 2A, P = 0.0033), E2F2 (Figure 2B, P = 0.0009), E2F3 (Figure 2C, P = 0.0422), E2F7 (Figure 2G, P < 0.0001), and E2F8 (Figure 2H, P = 0.0003) were upregulated in UCEC tissues while E2F4 (Figure 2D, P = 0.0028). However, there is no difference between UCEC tissues and normal tissues in the expression of E2F5 (Figure 2E, P = 0.2189) and E2F2 (Figure 2F, P = 0.7274). Thus, we suggested that the expression of E2F1/2/3/7/8 was significantly upregulated in EC tissues, converse to E2F4, which was downregulated.

In exploring the relative level of E2F in UCEC and normal tissues, overexpression of E2F4 ranked first, with E2F7 being the lowest expressed member in UCEC tissues (Figure 3A). In normal tissues, E2F4 was relatively the highest expressed gene, with E2F8 being the lowest (Figure 3B). Regardless of the tissues, there was always a low to high correlation between each member of E2F family and normal or UCEC tissues (Figure 3C,D).

Methylation analysis revealed that E2F2, E2F3, and E2F8 were not methylated in UCEC tissues (Figure 3E). Interestingly, methylation downregulates all the members of E2F family except for E2F2 (Figure 3F). We also observed that E2F7 hypermethylation is a risk factor for developing UCEC (Figure 3G).

We then analyzed the association between abnormally expressed E2Fs (E2F1/2/3/4/7/8) and UCEC clinical pathological along race, weight, age, menopause status, histological subtypes, TP53 mutation status, and cancer stage. Here, the transcription of E2F1 (Figure 4A), E2F2 (Figure 4B), and E2F3 (Figure 5A) mRNA was upregulated in UCEC patients compared with healthy individuals in subgroup analyses based on race, weight, age, menopause status, histological subtypes, TP53 mutation status, and cancer stage. Comparable findings were obtained for E2F7 (Figure 6A) and E2F8 (Figure 6B). Moreover, the transcription of E2F4 (Figure 5B) mRNA was downregulated in UCEC patients compared with healthy individuals in subgroup analyses based on race, weight, age, menopause status, histological subtypes, TP53 mutation status, and cancer stage.

KM-plotter analysis for prognostic value of E2F family in UCEC revealed that UCEC patients overexpressing E2F1 (HR = 2.23, 95%:1.43–3.46, P = 0.00026) and E2F3 (HR = 1.67, 95%:1.1–2.55, P = 0.015) had a worse OS (Figure 7A). However, the expression level of E2F2, E2F4, E2F5, E2F6, E2F7, and E2F8 had no effect on the OS of these patients (Figure 7A). With regard to RFS, we found UCEC patients expressing E2F1 (HR = 2.09, 95%:1.2–3.62, P = 0.0074) and E2F3 (HR = 2.08, 95%:1.23–3.54, P = 0.0054) to have a worse RFS (Figure 7B). However, the expression of E2F2, E2F4, E2F5, E2F6, E2F7, and E2F8 had no effect on the RFS of UCEC patients (Figure 7B). Therefore, E2F1 and E2F3 are potential prognostic biomarkers for UCEC.

Members of E2F were dysregulated to explore the role of these proteins in UCEC. Follow-up analyses were performed to explore the correlation between the E2F family and the major hallmarks of UCEC. Genetic alteration analysis revealed that the genetic alteration in the E2F genes encompassed missense and non-sense_Mutations. In particular, they included In_Frame_Del, Multi_Hit, Frame_shift_Ins, Spice_Site, and Frame_Shift_Del (Figures 8A,B). Among the E2F family, E2F8, E2F7, and E2F1 were the top three most-frequently altered genes in UCEC (Figures 8A,B). However, these genetic alterations had no effect on the OS (Figure 8C, P = 0.222) and disease-free survival (Figure 8D, P = 0.0301) of UCEC patients, even though the p-value of OS is less than 0.05. Pathway analysis validated the passive role of E2F8, E2F7, and E2F1 in the OS of UCEC patients by revealing that E2Fs were mainly associated with the activation of apoptosis pathways, disruption of cell cycle, and impairment of DNA damage response pathways (Figure 9A). Because the nature of genetic alternations influences clinical treatment intervention, mutation types are potential targets for anticancer drug matching. Meanwhile, drug-sensitivity analysis demonstrated that the expression of E2F2/3/8 negatively correlated with drug resistance (Figure 9B), suggesting the use of E2F2/3/8 as novel markers for drug-sensitivity screening.

As shown in Figure 10A, the expression of most members of E2F family was negatively correlated with the abundance of B cell, except for E2F4 and E2F8. That of CD8 + cell positively correlated with the expression of E2F4, E2F5, and E2F7 (Figure 10A). Moreover, the abundance of CD4 + cell negatively correlated with the expression of E2F5 and E2F7 (Figure 10A). As for the macrophage, an abundance of these cell types negatively correlated with the expression of E2F1, E2F2, and E2F6 (Figure 10A). Interestingly, the abundance of neutrophil negative correlated with all members of E2F family (Figure 10A). Moreover, we also found that somatic copy number alterations in the E2F family inhibit immune cell infiltrations (Figure 10B).

Enrichment analysis was performed to explore the role of E2F in UCEC. We first constructed a network to explore the association between neighboring genes and E2F family. Our findings are shown Figure 11, featuring 34 adjacent genes. GO enrichment analysis in Metascape of E2F family were mainly associated with nuclear TF complex, G1/S transition of mitotic cell cycle, sequence-specific DNA binding, initiation of DNA transcription from polymerase II promoter, DNA-dependent DNA replication, and intrinsic apoptosis signaling pathway mediated by P53 (Figures 12A,B). KEGG pathways analysis demonstrated the involvement of the E2F family in the cell cycle, TGF-beta signaling pathway, Epstein-Barr virus infection, and colorectal cancer (Figures 12C,D). MCODE further revealed that the E2F family and neighboring genes influenced for the binding of RNA polymerase II TF complex, nuclear TF complex, TF binding, and sequence-specific DNA binding (Figures 12E,F). We additionally validated our results using DAVID 6.8. GO enrichment analysis revealed that E2F genes were mainly involved in transcription, DNA-templated, regulation of transcription, DNA damage response, G1/S transition of mitotic cell cycle, protein binding, DNA binding, TF binding, damaged DNA binding, and p53 binding (Figure 13A). As for the KEGG pathways enrichment analysis, our results suggest that E2F participates in cell cycle progression, cancer pathways, viral carcinogenesis, TGF-beta signaling pathway, PI3K-Akt signaling pathway, and p53 signaling pathway (Figure 13B).

We further explored the influence of E2F on kinase and miRNA in UCEC. It was revealed that multiple kinases are targets for E2F (Table 1). In particular, AUPKB and CDK1 are targets for E2F1, whereas CDK1 and CDK2 are targets for E2F2. E2F3 and E2F4 target PLK1 and ATM; CDK2 and LCK, respectively. ATM kinase is targeted by E2F5, E2F6, and E2F7. PLK1 was additionally targeted by E2F5, E2F7, and E2F8. Meanwhile, miRNA targets for E2F family are shown in Table 2. Briefly, miRNA targets for E2F1 include (TATTATA) MIR-374 and (CATTTCA) MIR-203, whereas MIR-199A (CTACTGT) and MIR-137 (AAGCAAT) are targets for E2F2. Moreover, MIR-302C (ATGTTAA) and MIR-381 (CTTGTAT) are miRNA targets for E2F3. As E2F4, it targets MIR-384 (CTAGGAA) and MIR-381 (CTTGTAT), whereas E2F5 targets MIR-154, MIR-487 (GTATGAT), and MIR-302C (ATGTTAA). E2F6 on its part targets MIR-212, MIR-132 (GACTGTT), and MIR-499 (AGTCTTA). Finally, E2F7 targets MIR-522 (ACCATTT) and MIR-508 (TACAATC), with E2F8 targeting MIR-323 (TAATGTG) and MIR-522 (ACCATTT).