Conserved E2F mediated metastasis in mouse models of breast cancer and HER2 positive patients

Predicting prognosis and immune responses in hepatocellular carcinoma based on N7-methylguanosine-related long noncoding RNAs

Background: Hepatocellular carcinoma (HCC), which has high rates of recurrence and metastasis and is the main reason and the most common tumor for cancer mortality worldwide, has an unfavorable prognosis. N7-methylguanosine (m7G) modification can affect the formation and development of tumors by affecting gene expression and other biological processes. In addition, many previous studies have confirmed the unique function of long noncoding RNAs (lncRNAs) in tumor progression; however, studies exploring the functions of m7G-related lncRNAs in HCC patients has been limited.

Methods: Relevant RNA expression information was acquired from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov), and m7G-related lncRNAs were identified via gene coexpression analysis. Afterward, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate regression analyses were implemented to construct an ideal risk model whose validity was verified using Kaplan–Meier survival, principal component, receiver operating characteristic (ROC) curve, and nomogram analyses. In addition, the potential functions of lncRNAs in the novel signature were explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses and gene set enrichment analysis (GSEA). At last, in both risk groups and subtypes classified based on the expression of the risk-related lncRNAs, we analyzed the immune characteristics and drug sensitivity of patients.

Results: After rigorous screening processes, we built a model based on 11 m7G-related lncRNAs for predicting patient overall survival (OS). The results suggested that the survival status of patients with high-risk scores was lower than that of patients with low-risk scores, and a high-risk score was related to malignant clinical features. Cox regression analysis showed that the m7G risk score was an independent prognostic parameter. Moreover, immune cell infiltration and immunotherapy sensitivity differed between the risk groups.

Conclusion: The m7G risk score model constructed based on 11 m7G-related lncRNAs can effectively assess the OS of HCC patients and may offer support for making individualized treatment and immunotherapy decisions for HCC patients.

Identification of the Expression and Clinical Significance of E2F Family in Clear Cell Renal Cell Carcinoma

Background

Multiple studies have identified that E2F transcriptions act as important regulators for the tumorigenesis and progression of several human cancers. However, little is known about the function of E2Fs in clear cell renal cell carcinoma (ccRCC).

Methods

We firstly investigated the expression levels, genetic alteration, and biological function of E2Fs in patients with ccRCC and the connections between the immune cell infiltration and the overall survivals of ccRCC patients with the E2Fs expression levels based on UALCAN, The Cancer Genome Atlas database, Gene Expression Profiling Interactive Analysis, TIMER, STRING, GSCALite and cBioPortal databases.

Results

Results revealed that the expression levels of E2F1/2/3/4/6/7/8 were markedly upregulated in patients with ccRCC, while the expression of E2F5 displayed an opposite trend. We also experimentally validated the overexpression of E2F3/4/7 in human ccRCC tissues and ccRCC cell lines. Furthermore, the high E2F1/2/3/4/7/8 expression levels were clearly associated with worse pathological characteristics of ccRCC, including high pathological stage, poor molecular subtypes and high tumor grade. Meanwhile, high expression levels of E2F1/2/4/7/8 were evidently associated with worse overall survivals (OSs) and progression-free survivals (PFSs) of patients harboring ccRCC. Univariate and multivariate analyses illustrated that the expressions of E2F4/5/7 were independent factors associated with the OSs and PFSs of patients with ccRCC. Meanwhile, the mutations in E2Fs were also significantly related to poor OSs and PFSs of patients with ccRCC. Mechanically, the E2Fs genes synergistically promoted the progression of ccRCC by accelerating the cell cycle and inhibiting DNA damage response and apoptosis after performing the protein structure, functional enrichment, and PPI network analyses. In addition, E2Fs genes were also significantly associated with tumor immune cells infiltration and the drug sensitivity in ccRCC.

Conclusion

As a result, E2F4/7 were highly expressed in ccRCC and significantly associated with worse pathological characteristics of ccRCC, including high pathological stage, poor molecular subtypes and high tumor grade, tumor immune cell infiltration, and drug sensitivity, consequently translating into poor OSs and PFSs of patients with ccRCC. Our results indicated that E2F4/7 could be potential biomarkers and therapeutic targets of ccRCC patients.

Prognostic Values of E2F1/2 Transcriptional Expressions in Chromophobe Renal Cell Carcinoma Patients: Evidence from Bioinformatics Analysis

Background

Numerous studies on the E2F transcription factors have led to increasing insights that E2Fs could be an important driver of the formation and progression of many human cancers. Little is known about the function of distinct E2Fs in chromophobe renal cell carcinoma (chRCC).

Methods

We utilized the UALCAN, GEPIA, Cancer Genome Atlas (TCGA) database, cBioPortal, Metascape, STRING, Cytoscape, GeneMANIA, TIMER, TISIDB, GSCALite, and MEXPRESS databases to investigate the transcription level, genetic alteration, methylation, and biological function of E2Fs in chRCC patients, and its association with the occurrence, progress, prognosis, and immune cell infiltration in patients with chRCC.

Results

We found that E2F1/2/4/7/8 were more expressed in chRCC tissues than in normal tissues, while the expression of E2F5/6 was lower in the former than in the latter, and the expression levels of E2F1/2/4/5/6//7/8 were also associated with the histological parameters of chRCC, including T-stage and N-stage. Higher expression of E2F1/2/7/8 was found to be significantly correlated with worse overall survival (OS) in chRCC patients. Cox regression and time-dependent ROC analysis further suggested that E2F1/2 could be the potential independent biomarkers for chRCC prognosis. Besides, a moderate mutation rate of E2Fs (34%) was noticed in chRCC, and the genetic mutations in E2Fs were associated with poor survival of chRCC patients. We noticed that the expression of E2Fs was statistically correlated with the immune cell infiltration in chRCC. Moreover, we also found that the expression of E2F1 was significantly correlated with tumor-infiltrating lymphocytes and immunomodulators, E2F7 expression was associated with MHC molecules, and the expression of E2F1/8 was correlated to their methylation levels.

Conclusion

Our results provide novel insights for selecting the prognostic biomarkers for chRCC and suggest that E2F1/2 could act as potential prognostic biomarkers for the survival of chRCC patients. However, more in-depth experiments are required to identify the underlying mechanisms and verify the clinical value of E2F1/2 in the prognosis of chRCC.

Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases

Accumulating evidence has demonstrated that gene alterations play a crucial role in LUAD development, progression, and prognosis. The present study aimed to identify the hub genes associated with LUAD. In the present study, we used TCGA database to screen the hub genes. Then, we validated the results by GEO datasets. Finally, we used cBioPortal, UALCAN, qRT-PCR, HPA database, TCGA database, and Kaplan–Meier plotter database to estimate the gene mutation, gene transcription, protein expression, clinical features of hub genes in patients with LUAD. A total of 5930 DEGs were screened out in TCGA database. Enrichment analysis revealed that DEGs were involved in the transcriptional misregulation in cancer, viral carcinogenesis, cAMP signaling pathway, calcium signaling pathway, and ECM–receptor interaction. The combining results of MCODE and CytoHubba showed that ADCY8, ADRB2, CALCA, GCG, GNGT1, and NPSR1 were hub genes. Then, we verified the above results by GSE118370, GSE136043, and GSE140797 datasets. Compared with normal lung tissues, the expression levels of ADCY8 and ADRB2 were lower in LUAD tissues, but the expression levels of CALCA, GCG, GNGT1, and NPSR1 were higher. In the prognosis analyses, the low expression of ADCY8 and ADRB2 and the high expression of CALCA, GCG, GNGT1, and NPSR1 were correlated with poor OS and poor PFS. The significant differences in the relationship of the expression of 6 hub genes and clinical features were observed. In conclusion, 6 hub genes will not only contribute to elucidating the pathogenesis of LUAD and may be potential therapeutic targets for LUAD.

Low E2F2 activity is associated with high genomic instability and PARPi resistance

The E2F family, classically known for a central role in cell cycle, has a number of emerging roles in cancer including angiogenesis, metabolic reprogramming, metastasis and DNA repair. E2F1 specifically has been shown to be a critical mediator of DNA repair; however, little is known about DNA repair and other E2F family members. Here we present an integrative bioinformatic and high throughput drug screening study to define the role of E2F2 in maintaining genomic integrity in breast cancer. We utilized in vitro E2F2 ChIP-chip and over expression data to identify transcriptional targets of E2F2. This data was integrated with gene expression from E2F2 knockout tumors in an MMTV-Neu background. Finally, this data was compared to human datasets to identify conserved roles of E2F2 in human breast cancer through the TCGA breast cancer, Cancer Cell Line Encyclopedia, and CancerRx datasets. Through these methods we predict that E2F2 transcriptionally regulates mediators of DNA repair. Our gene expression data supports this hypothesis and low E2F2 activity is associated with a highly unstable tumor. In human breast cancer E2F2, status was also correlated with a patient's response to PARP inhibition therapy. Taken together this manuscript defines a novel role of E2F2 in cancer progression beyond cell cycle and could impact patient treatment.

Novel Potential Therapeutic Target for E2F1 and Prognostic Factors of E2F1/2/3/5/7/8 in Human Gastric Cancer

E2F transcription factors (E2Fs) were found to be related with cell activities and disease progression among a variety of different tumors, including regulating cell division and cell proliferation. In the analysis, it aimed to focus on transcriptional and survival information of E2Fs in gastric cancer (GC) from Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier plotter, cBioPortal, Database for Annotation, Visualization and Integrated Discovery (DAVID), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Oncomine databases. It was found that the expression of E2F1/2/3/5/7/8 in GC tissues was obviously higher than the normal. Of interest, none of the E2Fs was related with pathological stages. Nevertheless, high expression of E2F2/3/5/7/8 was related with better survival data, except E2F6 regarding shorter first-progression (FP) survival. High expression levels of E2F2/5/7/8 have significant correlations with overall survival (OS) in patients with intestinal and diffuse GC, and this prognostic value is not affected by gender. Oppositely, the lower level of E2F1/4 illustrated superior survival data. Moreover, increased expression of E2F1 in GC tissues might play an important role in the development of GC. Collectively, E2F1 could be a potential therapeutic target for patients with GC. E2F1/2/3/5/7/8 might be original prognostic predictors of GC.

A 17-gene expression-based prognostic signature associated with the prognosis of patients with breast cancer: A STROBE-compliant study

Identification of reliable predictive biomarkers for patients with breast cancer (BC).Univariate Cox proportional hazards regression model was conducted to identify genes correlated with the overall survival (OS) of patients in the TCGA-BRCA cohort. Functional enrichment analysis was conducted to investigate the biological meaning of these survival related genes. Then, patients in TCGA-BCRA were randomly divided into training set and test. Least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was performed and the risk score of BC patients in this model was used to build a prognostic signature. The prognostic performance of the signature was evaluated in the training set, test set, and an independent validation set GSE7390.2519 genes were demonstrated to be significantly associated with the OS of BC patients. Functional annotation of the 2519 genes suggested that these genes were associated with immune response and protein synthesis related gene ontology terms and pathways. 17 genes were identified in the LASSO Cox regression model and used to construct a 17-gene signature. Patients in the 17-gene signature low risk group have better OS and event-free survival compared with those in the 17-gene signature high risk group in the TCGA-BRCA cohort. The prognostic role of the 17-gene signature has been confirmed in the validation cohort. Multivariable Cox proportional hazards regression model suggested the 17-gene signature was an independent prognostic factor in BC.The 17-gene signature we developed could successfully classify patients into high- and low-risk groups, indicating that it might serve as candidate biomarker in BC.

Transcriptional E2F1/2/5/8 as potential targets and transcriptional E2F3/6/7 as new biomarkers for the prognosis of human lung carcinoma

E2F is a group of genes that encode a family of transcription factors (TFs) in higher eukaryotes and participate in cell cycle regulation and DNA synthesis in mammalian cells. Evidence from cell lines, mouse models, and human tissues indicates that TFs are implicated in lung cancer (LC) tumorigenesis. However, the diverse expression patterns and prognostic values of eight E2Fs have yet to be elucidated. In the current study, we examined the transcriptional and survival data of E2Fs in patients with LC from ONCOMINE, GEPIA, Kaplan-Meier Plotter, and cBioPortal databases. We found that the expression levels of E2F1/2/3/5/6/7/8 were higher in lung adenocarcinoma and squamous cell lung carcinoma tissues than in lung tissues, whereas the expression level of E2F4 was lower in the former than in the latter. The expression levels of E2F2/4/5/7/8 were correlated with advanced tumor stage. Survival analysis using the Kaplan-Meier Plotter database revealed that the high transcription levels of E2F1/2/4/5/7/8 were associated with low relapse-free survival (RFS) in all of the patients with LC. Conversely, high E2F3/6 levels predicted high RFS in these patients. This study implied that E2F3/6/7 are potential targets of precision therapy for patients with LC and that E2F1/2/4/5/8 are new biomarkers for the prognosis of LC.

Comprehensive Analysis of the Expression and Prognosis for E2Fs in Human Breast Cancer

E2F transcription factors (E2Fs), a group of genes that encode a family of transcription factors, have been identified as being involved in the tumor progression of various cancer types. Increasing experimental evidence indicates that E2Fs are implicated in breast cancer tumorigenesis. However, the diverse expression patterns and prognostic values of eight E2Fs have yet to be analyzed. Herein we investigated the transcriptional and survival data of E2Fs in patients with breast cancer from the Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier Plotter, and cBioPortal databases. We found that the expression levels of E2F1-3 and 5-8 were higher in breast cancer tissues than in normal breast tissues, whereas the expression level of E2F4 was lower in the former than in the latter. The expression levels of E2F2, 5, 7, and 8 were correlated with advanced tumor stage. Survival analysis using the Kaplan-Meier Plotter database revealed that the high transcription levels of E2F1-3, 5, 7, and 8 were associated with low relapse-free survival in all of the patients with breast cancer. Conversely, high E2F4 and E2F6 levels predicted high relapse-free survival in these patients. This study implied that E2F1-3, 5, 7, and 8 are potential targets of precision therapy for patients with breast cancer and that E2F4 and 6 are new biomarkers for the prognosis of breast cancer.

Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures

Cancer is a disease of subverted regulatory pathways. In this paper, we reconstruct the regulatory network around E2F, a family of transcription factors whose deregulation has been associated to cancer progression, chemoresistance, invasiveness, and metastasis. We integrate gene expression profiles of cancer cell lines from two E2F1-driven highly aggressive bladder and breast tumors, and use network analysis methods to identify the tumor type-specific core of the network. By combining logic-based network modeling, in vitro experimentation, and gene expression profiles from patient cohorts displaying tumor aggressiveness, we identify and experimentally validate distinctive, tumor type-specific signatures of receptor proteins associated to epithelial-mesenchymal transition in bladder and breast cancer. Our integrative network-based methodology, exemplified in the case of E2F1-induced aggressive tumors, has the potential to support the design of cohort- as well as tumor type-specific treatments and ultimately, to fight metastasis and therapy resistance.Deregulation of E2F family transcription factors is associated with cancer progression and metastasis. Here, the authors construct a map of the regulatory network around the E2F family, and using gene expression profiles, identify tumour type-specific regulatory cores and receptor expression signatures associated with epithelial-mesenchymal transition in bladder and breast cancer.

For robust big data analyses: a collection of 150 important pro-metastatic genes

Metastasis is the greatest contributor to cancer-related death. In the era of precision medicine, it is essential to predict and to prevent the spread of cancer cells to significantly improve patient survival. Thanks to the application of a variety of high-throughput technologies, accumulating big data enables researchers and clinicians to identify aggressive tumors as well as patients with a high risk of cancer metastasis. However, there have been few large-scale gene collection studies to enable metastasis-related analyses. In the last several years, emerging efforts have identified pro-metastatic genes in a variety of cancers, providing us the ability to generate a pro-metastatic gene cluster for big data analyses. We carefully selected 285 genes with in vivo evidence of promoting metastasis reported in the literature. These genes have been investigated in different tumor types. We used two datasets downloaded from The Cancer Genome Atlas database, specifically, datasets of clear cell renal cell carcinoma and hepatocellular carcinoma, for validation tests, and excluded any genes for which elevated expression level correlated with longer overall survival in any of the datasets. Ultimately, 150 pro-metastatic genes remained in our analyses. We believe this collection of pro-metastatic genes will be helpful for big data analyses, and eventually will accelerate anti-metastasis research and clinical intervention.

Expression and prognostic value of E2F activators in NSCLC and subtypes: a research based on bioinformatics analysis

E2F activators (E2F1-3) codify a family of transcription factors (TFs) in higher eukaryotes. E2F activators are involved in the cell cycle regulation and synthesis of DNA in mammalian cells, and their overexpression has been detected in many human cancers. However, their clinical significance has not been deeply researched in non-small-cell lung cancer (NSCLC), and bioinformatics analysis has never been reported to explore their clinical role in NSCLC. In the current study, we investigated the expression and prognostic value of E2F activators in NSCLC patients through the "TCGA datasets" and the "Kaplan-Meier plotter" (KM plotter) database. Hazard ratio (HR), 95 % confidence intervals, and log-rank P were calculated. Compared with normal tissue samples, E2F activators were overexpressed in NSCLC tissues, in lung adenocarcinoma (LUAD) tissues, and in lung squamous cell carcinoma (LUSC) tissues. In NSCLC patients, E2F1 expression was significantly correlated with age, sex, and tumor stage. E2F2 expression was found to be significantly correlated with sex and tumor size. We further demonstrated that E2F1 and E2F2 overexpressions were significantly associated with poor prognosis. In LUAD patients, E2F1 expression was significantly correlated with tumor size and tumor stage. E2F2 expression was significantly correlated with lymph node status and tumor stage. E2F1 and E2F2 overexpression showed a significant association with poor prognosis, while E2F3 overexpression was significantly correlated to better prognosis. In LUSC patients, E2F1 was concluded to be significantly correlated with tumor stage. However, E2F activators were not found to be correlated to prognosis.