Identification of key transcription factors in endometrial cancer by systems bioinformatics analysis

Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach

BACKGROUND

Endometrial cancer represents a significant health challenge, with rising incidence and complex prognostic challenges. This study aimed to develop a robust predictive model integrating programmed cell death-related genes and advanced machine learning techniques.

METHODS

Utilizing transcriptomic data from TCGA-UCEC and GSE119041 datasets, we employed a comprehensive approach involving 117 machine learning algorithms. Key methodologies included differential gene expression analysis, weighted gene co-expression network analysis, functional enrichment studies, immune landscape evaluation, and multi-dimensional risk stratification.

RESULTS

We identified 10 critical genes (PTGIS, TIMP3, SRPX, SNCA, HIC1, BAK1, STXBP2, TRIB3, RTKN2, E2F1) and constructed a prognostic model with superior predictive performance. The StepCox[forward] + plsRcox algorithm combination demonstrated excellent predictive accuracy (AUC > 0.8). Kaplan-Meier analysis revealed significant survival differences between high- and low-risk groups in both training (HR = 3.37, p < 0.001) and validation cohorts (HR = 2.05, p = 0.021). The model showed strong correlations with clinical characteristics, immune cell infiltration patterns, and potential therapeutic responses.

CONCLUSIONS

This study presents a novel, comprehensive approach to endometrial cancer prognosis, integrating machine learning and molecular insights to provide a more precise risk stratification tool with potential clinical translation.

Mechanism of E2F1 in the proliferation, migration, and invasion of endometrial carcinoma cells via the regulation of BMI1 transcription

BACKGROUND

Endometrial carcinoma (EC) is the most prevalent gynecological cancer. Transcription factor (TF) regulates a large number of downstream target genes and is a key determinant of all physiological activities, including cell proliferation, differentiation, apoptosis, and cell cycle. The transcription factor E2F1 shows prominent roles in EC. BMI1 is a member of Polycomb suppressor Complex 1 (PRC1) and has been shown to be associated with EC invasiveness. It is currently unclear whether E2F1 can participate in the proliferation, migration, and invasion processes of EC cells by regulating BMI1 transcription.

OBJECTIVE

We investigated whether E2F1 could participate in the proliferation, migration, and invasion processes of EC cells by regulating BMI1 transcription, in order to further clarify the pathogenesis and etiology of EC, and provide reference for identifying potential therapeutic targets and developing effective prevention and treatment strategies for this disease.

METHODS

Human endometrial epithelial cells (hEECs) and human EC cell lines were selected. E2F1 expression was assessed by Western blot. E2F1 was silenced in AN3CA or overexpressed in HEC-1 by transfections, or E2F1 was silenced and BMI1 was overexpressed in AN3CA by cotransfection. Cell proliferation, migration, and invasion were detected by MTT, wound healing, and Transwell assays. The binding sites between E2F1 and BMI1 promoters were predicted through JASPAR website, and the targeted binding was verified by dual-luciferase report and ChIP assays.

RESULTS

E2F1 was up-regulated in human EC cell lines, with its expression highest in AN3CA, and lowest in HEC-1. AN3CA invasion, migration, and proliferation were repressed by E2F1 knockdown, while those of HEC-1 cells were promoted by E2F1 overexpression. E2F1 overexpression increased the activity of wild type BMI1 reporter vector promoter, while this promotion was weakened after mutation of the predicted binding site in the BMI1 promoter. In the precipitated E2F1, BMI1 promoter site level was higher than that of IgG immunoprecipitant. BMI1 silencing suppressed AN3CA cell growth. BMI1 overexpression partially abrogated E2F1 silencing-inhibited EC cell growth.

CONCLUSION

E2F1 promoted EC cell proliferation, invasion, and migration by promoting the transcription of BMI1.

Identification of Potentially Novel Molecular Targets of Endometrial Cancer Using a Non-Biased Proteomic Approach

The present study was aimed at identifying novel proteins in endometrial cancer (EC), employing proteomic analysis of tissues obtained after surgery. A differential MS-based proteomic analysis was conducted from whole tissues dissected from biopsies from post-menopausal women, histologically confirmed as endometrial cancer (two endometrioid and two serous; n = 4) or normal atrophic endometrium (n = 4), providing 888 differentially expressed proteins with 246 of these previously documented elsewhere as expressed in EC and 372 proteins not previously demonstrated to be expressed in EC but associated with other types of cancer. Additionally, 33 proteins not recorded previously in PubMed as being expressed in any forms of cancer were also identified, with only 26 of these proteins having a publication associated with their expression patterns or putative functions. The putative functions of the 26 proteins (GRN, APP, HEXA, CST3, CAD, QARS, SIAE, WARS, MYH8, CLTB, GOLIM4, SCARB2, BOD1L1, C14orf142, C9orf142, CCDC13, CNPY4, FAM169A, HN1L, PIGT, PLCL1, PMFBP1, SARS2, SCPEP1, SLC25A24 and ZC3H4) in other tissues point towards and provide a basis for further investigation of these previously unrecognised novel EC proteins. The developmental biology, disease, extracellular matrix, homeostatic, immune, metabolic (both RNA and protein), programmed cell death, signal transduction, molecular transport, transcriptional networks and as yet uncharacterised pathways indicate that these proteins are potentially involved in endometrial carcinogenesis and thus may be important in EC diagnosis, prognostication and treatment and thus are worthy of further investigation.

The E2F1/MELTF axis fosters the progression of lung adenocarcinoma by regulating the Notch signaling pathway

BACKGROUND

Lung adenocarcinoma (LUAD) represents the predominant subtype of lung cancer. MELTF, an oncogene, exhibits high expression in various cancer tissues. Nevertheless, the precise role of MELTF in the progression of LUAD remains enigmatic. This work was devised to investigate the effect of MELTF on LUAD progression and its underlying mechanism.

METHODS

mRNA expression data of LUAD were from The Cancer Genome Atlas database, and the enrichment pathway of MELTF was analyzed. The upstream transcription factors of MELTF were predicted, and the correlation between MELTF and E2F1 as well as the expression of the two in LUAD tissues were dissected by bioinformatics. The expression of MELTF and E2F1 in LUAD tissues and cells was assayed by qRT-PCR. Effects of MELTF/E2F1 on proliferation, migration, and invasion of LUAD cells were tested by CCK-8, colony formation, and Transwell assays. The binding relationship between E2F1 and MELTF was estimated by dual-luciferase reporter gene assay and ChIP assay. Western blot was utilized to assay the expression of Notch signaling pathway-related proteins in different treatment groups.

RESULTS

Bioinformatics analysis and qRT-PCR results exhibited high expression of E2F1 and MELTF in LUAD tissues and cells, respectively. Dual-luciferase reporter gene assay and ChIP assay ascertained the binding of E2F1 to MELTF. MELTF was ascertained to enrich the Notch signaling pathway by bioinformatics means. In cell experiments, MELTF was shown to foster the malignant progression of LUAD cells and promoted the expression of NOTCH1 and HES1 proteins, but RO4929097 offset the effect of MELTF on cells. Rescue assay confirmed that E2F1 activated MELTF to promote LUAD progression via the Notch signaling pathway.

CONCLUSION

Together, our outcomes demonstrated that E2F1 fostered LUAD progression by activating MELTF via the Notch signaling activity. Hence, MELTF emerged as a feasible target for treating LUAD.

Excavation of Molecular Subtypes of Endometrial Cancer Based on DNA Methylation

Tumor heterogeneity makes the diagnosis and treatment of endometrial cancer difficult. As an important modulator of gene expression, DNA methylation can affect tumor heterogeneity and, therefore, provide effective information for clinical treatment. In this study, we explored specific prognostic clusters based on 482 examples of endometrial cancer methylation data in the TCGA database. By analyzing 4870 CpG clusters, we distinguished three clusters with different prognostics. Differences in DNA methylation levels are associated with differences in age, grade, clinical pathological staging, and prognosis. Subsequently, we screened out 264 specific hypermethylation and hypomethylation sites and constructed a prognostic model for Bayesian network classification, which corresponded to the classification of the test set to the classification results of the train set. Since the tumor microenvironment plays a key role in determining immunotherapy responses, we conducted relevant analyses based on clusters separated from DNA methylation data to determine the immune function of each cluster. We also predicted their sensitivity to chemotherapy drugs. Specific classifications of DNA methylation may help to address the heterogeneity of previously existing molecular clusters of endometrial cancer, as well as to develop more effective, individualized treatments.

Endothelial cell-specific molecule 1 (ESM1) promoted by transcription factor SPI1 acts as an oncogene to modulate the malignant phenotype of endometrial cancer

We aimed to study the function and mechanism of endothelial cell-specific molecule 1 (ESM1) in endometrial cancer (EC). The binding relationship between SPI1 and ESM1 was predicted by bioinformatics analysis and verified by the dual-luciferase reporter assay. The expressions and effects of SPI1 and ESM1 were determined using quantitative real-time PCR, immunohistochemistry, Western blot, and functional experiments. ESM1 was highly expressed in EC and was associated with the poor prognosis of patients. ESM1 silencing suppressed the viability, proliferation, invasion, and angiogenesis of EC cells, down-regulated expressions of PCNA, N-cadherin, Vimentin, VEGFR-1, VEGFR2, and EGFR, but upregulated E-cadherin level, while ESM1 overexpression did oppositely. Moreover, SPI1 bound to ESM1. Overexpressed SPI1 promoted the expression of ESM1 and induced malignant phenotype (viability, proliferation, and invasion), which were countervailed by ESM1 silencing. Collectively, ESM1 induced by SPI1 promotes the malignant phenotype of EC.

Identification of prognostic long intergenic non-coding RNAs as competing endogenous RNAs with KRAS mutations in colorectal cancer

Colorectal cancer (CRC) is recognized as a common type of human cancer, and KRAS mutations are correlated with poor CRC survival outcomes. The evaluation and prediction of CRC results remain challenging. In the present study, RNA sequencing and clinical data from The Cancer Genome Atlas were used to identify KRAS mutation-related prognostic long intergenic non-coding RNAs (lincRNAs) in CRC. Significantly dysregulated lincRNAs and independent prognostic lincRNAs with KRAS mutations in CRC were identified. Two lincRNAs with KRAS mutations, LINC00265 and AL390719.2, were selected as key prognostic lincRNAs for both 10- and 5-year survival rates. In addition, competing endogenous (ce)RNA models were constructed to comprehensively assess the oncogenic performance of the two key lincRNAs. The ceRNA models suggested that LINC00265 and AL390719.2 are critical for the cell cycle and cancer pathways. Finally, reverse transcription-quantitative PCR was used to validate the ceRNA models in 12 pairs of CRC tissue samples. These prognostic lincRNAs may provide novel biomarkers for the prognostic prediction of CRC. The ceRNA model may also demonstrate the underlying mechanism of these lincRNAs in CRC.

A Novel Transcription Factor-Based Prognostic Signature in Endometrial Cancer: Establishment and Validation

Background

Endometrial cancer (EC) is a common malignancy of the female reproductive system worldwide. Increasing evidence has suggested that many transcription factors are aberrantly expressed in various cancers. This study aimed to develop a transcription factor-based prognostic signature for EC.

Methods

Gene expression data and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression and Multivariate Cox regression analysis was used to construct a prognostic signature. Then, the efficacy of the prognostic signature was validated in a training cohort, testing cohort and then the entire cohort. Correlations between clinical features and the model were also analyzed, and a nomogram based on the multivariate Cox analysis was developed. Furthermore, we verified the effect of a key transcription factor, E2F1, on biological functions of EC in vitro.

Results

We developed a nine-transcription factor (MSX1, HOXB9, E2F1, DLX4, BNC2, DLX2, PDX1, POU3F2, and FOXP3) prognostic signature. Compared with those in the low-risk group, patients in the high-risk group had worse clinical outcomes. The area under the curve (AUC) of this prognostic signature for 5-year survival was 0.806 in the training cohort, 0.710 in the testing cohort and 0.761 in the entire cohort. Gene set enrichment analysis (GSEA) revealed a correlation between the prognostic signature and various cancer signaling pathways, and a hub transcription factor regulatory network was constructed. The prognostic signature was confirmed to have independent predictive value. Finally, a nomogram based on the prognostic signature and clinical independent prognostic factors was also established and performed well according to the calibration curves. Further, knockdown of E2F1 inhibited invasion and metastasis of EC cells.

Conclusion

Our study developed and validated a transcription factor-based prognostic signature that accurately predicts prognosis of EC patients. Moreover, E2F1 may represent a potential target for the treatment of EC.

Identification of biomarkers correlated with diagnosis and prognosis of endometrial cancer using bioinformatics analysis

Endometrial cancer (EC) is one of the most common malignancies in the female genital system, characterized by high mortality and recurrence rates. This study attempted to screen key genes and potential prognostic biomarkers for EC using bioinformatics analysis. Twenty-seven normal endometrial tissues and 135 EC samples were collected from four Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein-protein interaction (PPI) network. Finally, we assessed the prognostic values and molecular mechanism of the potential prognostic genes using the Kaplan-Meier curve and Gene Set Enrichment Analysis (GSEA). As a result, 28 upregulated and 94 downregulated genes were determined after gene integration of these four GEO data sets. Gene Ontology analysis indicated that DEGs were mainly involved in transcriptional regulation and cell proliferation. The Kyoto Encyclopedia of Gene and Genome pathway analysis primarily related to transcriptional misregulation and apoptosis. Moreover, the PPI analysis revealed 10 hub genes (JUN, UBE2I, GATA2, WT1, PIAS1, FOXL2, RUNXI, EZR, TCF4, and NR2F2) with a high degree of connectivity, among them, the expression tendency of nine genes except UBE2I were consistent with messenger RNA level from The Cancer Genome Atlas data. Furthermore, only FOXL2, TCF4, and NR2F2 were significantly correlated with prognosis of EC patients, and their low expression associated biological pathways were enriched in the cell cycle and fatty acid metabolism. In conclusion, this study identified three key genes as biomarkers and potential therapeutic targets of EC on the basis of integrated bioinformatics analysis. The findings will improve our comprehension of the molecular mechanisms underlying the pathogenesis and prognosis of EC.

[Advancement of E2F1 in Common Tumors]

细胞周期相关转录因子E2F1（E2F transcription factor 1）是细胞周期相关转录因子E2F家族成员之一，主要参与包括细胞周期进展、DNA修复、DNA复制、细胞分化，增殖和凋亡等多种细胞过程。E2F1在全身多种肿瘤组织和细胞中呈高表达，起着促癌基因的作用，E2F1表达上调与肿瘤的发生、发展、转移及预后密切相关。因此，E2F1有望成为肿瘤治疗的新靶点。本文就E2F1在目前常见肿瘤中的最新研究进展做一综述。

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.