Identification of prognosis markers for endometrial cancer by integrated analysis of DNA methylation and RNA-Seq data

Identification of novel biomarkers and potential molecular targets for uterine cancer using network-based approach

A better understanding of incidences at the cellular level in uterine cancer is necessary for its effective treatment and favourable prognosis. Till date, it lacks appropriate molecular target-based treatment because of unknown molecular mechanisms that proceed to cancer and no drug has shown the required results of treatment with less severe side effects. Uterine Cancer is one of the top five cancer diagnoses and among the ten most common death-causing cancer in the United States of America. There is no FDA-approved drug for it yet. Therefore, it became necessary to identify the molecular targets for molecular targeted therapy of this widely prevalent cancer type. For this study, we used a network-based approach to the list of the deregulated (both up and down-regulated) genes taking adjacent p-Value ≤ 0.05 as significance cut off for the mRNA data of uterine cancer. We constructed the protein-protein interaction (PPI) network and analyzed the degree, closeness, and betweenness centrality-like topological properties of the PPI network. Then we traced the top 30 genes listed from each topological property to find the key regulators involved in the endometrial cancer (ECa) network. We then detected the communities and sub-communities from the PPI network using the Cytoscape network analyzer and Louvain modularity optimization method. A set of 26 (TOP2A, CENPE, RAD51, BUB1, BUB1B, KIF2C, KIF23, KIF11, KIF20A, ASPM, AURKA, AURKB, PLK1, CDC20, CDKN2A, EZH2, CCNA2, CCNB1, CDK1, FGF2, PRKCA, PGR, CAMK2A, HPGDS, and CDCA8) genes were found to be key genes of ECa regulatory network altered in disease state and might be playing the regulatory role in complex ECa network. Our study suggests that among these genes, KIF11 and H PGDS appeared to be novel key genes identified in our research. We also identified these key genes interactions with miRNAs.

Clinicopathological role of Cyclin A2 in uterine corpus endometrial carcinoma: Integration of tissue microarrays and ScRNA-Seq

BACKGROUND

The comprehensive expression level and potential molecular role of Cyclin A2 (CCNA2) in uterine corpus endometrial carcinoma (UCEC) remains undiscovered.

METHODS

UCEC and normal endometrium tissues from in-house and public databases were collected for investigating protein and messenger RNA expression of CCNA2. The transcription factors of CCNA2 were identified by the Cistrome database. The prognostic significance of CCNA2 in UCEC was evaluated through univariate and multivariate Cox regression as well as Kaplan-Meier curve analysis. Single-cell RNA-sequencing (scRNA-seq) analysis was performed to explore cell types in UCEC, and the AUCell algorithm was used to investigate the activity of CCNA2 in different cell types.

RESULTS

A total of 32 in-house UCEC and 30 normal endometrial tissues as well as 720 UCEC and 165 control samples from public databases were eligible and collected. Integrated calculation showed that the CCNA2 expression was up-regulated in the UCEC tissues (SMD = 2.43, 95% confidence interval 2.23∼2.64). E2F1 and FOXM1 were identified as transcription factors due to the presence of binding peaks on transcription site of CCNA2. CCNA2 predicted worse prognosis in UCEC. However, CCNA2 was not an independent prognostic factor in UCEC. The scRNA-seq analysis disclosed five cell types: B cells, T cells, monocytes, natural killer cells, and epithelial cells in UCEC. The expression of CCNA2 was mainly located in B cells and T cells. Moreover, CCNA2 was active in T cells and B cells using the AUCell algorithm.

CONCLUSION

CCNA2 was up-regulated and mainly located in T cells and B cells in UCEC. Overexpression of CCNA2 predicted unfavorable prognosis of UCEC.

A new methodology to reveal potential nucleic acid modifications associated with the risk of endometrial cancer through dispersive solid-phase extraction coupled with UHPLC-QE-Orbitrap-MS/MS and HPLC-UV

Nucleic acid modifications have attracted increasing attention in recent years since they have been found to be related to a number of diseases including cancer. Previous studies have shown that the early development of endometrial cancer (EC) is often accompanied by changes in methylation levels of related genes, and the expression of related proteins that regulate reactive oxygen species (ROS) shows significant differences in EC cells and tissues. However, it has not been reported whether nucleic acid modifications related to methylation or ROS can serve as biomarkers for EC. Accurate quantification of these nucleic acid modifications still has challenges because their amounts in urine are very low and the interferences in urine are complicated. In this study, a novel dispersive solid-phase extraction (DSPE) method based on chitosan-carbon nanotube-Al2O3 (CS-CNT-Al2O3) has been established for the analysis of 5-hydroxymethyluracil (5 mU), 5-methyl-2'-deoxycytidine (5-mdC), 5-hydroxymethyl-2'-deoxycytidine (5-hmdC), 5-formyl-2'-deoxycytidine (5-fdC), and 8-hydroxy-2'-deoxyguanosine (8-OHdG) in EC patient urine samples coupled with UHPLC-QE-Orbitrap-MS/MS and HPLC-UV. Firstly, the synthesis of the CS-CNT-Al2O3 nanocomposite was conducted by a sono-coprecipitation method and was characterized by scanning electron microscope (SEM), energy dispersive spectrometer (EDS), and Fourier transform infrared (FTIR). Under the optimal extraction conditions of DSPE, we successfully quantified 5 mU, 5-mdC, 5-hmdC, 5-fdC, and 8-OHdG in urine samples from 37 EC patients and 39 healthy controls. The results showed that there were significant differences in the levels of 5-mdC, 5-hmdC, 5-fdC, and 8-OHdG in EC patients compared to the healthy control group. The receiver operator characteristic (ROC) curve analysis was carried out to evaluate the potential of 5-mdC, 5-hmdC, 5-fdC, and 8-OHdG to distinguish EC patients from healthy volunteers. The area under the curve (AUC) for 5-mdC, 5-hmdC, 5-fdC, and 8-OHdG was 0.7412, 0.667, 0.8438, and 0.7981, respectively. It indicated that 5-mdC, 5-hmdC, 5-fdC, and 8-OHdG had certain potential in distinguishing between EC patients and healthy volunteers and they could act as potential non-invasive biomarkers for early diagnosis of EC. Moreover, the present study would stimulate investigations of the effects of nucleic acid modifications on the initiation and progression of EC.

UBE2C serves as a prognosis biomarker of uterine corpus endometrial carcinoma via promoting tumor migration and invasion

The biological functions of ubiquitin-conjugating enzymes E2 (UBE2) family members in uterine corpus endometrial carcinoma (UCEC) remains unclear. Our study aimed to systematically analyze the expression patterns, prognostic value, biological functions and molecular regulatory mechanisms of UBE2 family in UCEC. Among nine screened UBE2 family members associated with UCEC, UBE2C was the most significantly overexpressed gene with poor prognosis. High expression levels of UBE2C in UCEC was correlated with stages, histological subtypes, patient's menopause status and TP53 mutation. Three molecules (CDC20, PTTG1 and AURKA), were identified as the key co-expression proteins of UBE2C. The generic alterations (mutation, amplification) and DNA hypomethylation might contribute to UBE2C's high expression in UCEC. Furthermore, in vitro experiments showed that the interference of UBE2C inhibited the migration and invasion of endometrial cancer cells, while partially impact cell proliferation and didn't impact the expression of epithelial-mesenchymal transition (EMT) markers. Using comprehensive bioinformatics analysis and in vitro experiments, our study provided a novel insight into the oncogenic role of UBE2 family, specifically UBE2C in UCEC. UBE2C might serve as an effective biomarker to predict poor prognosis and a potential therapeutic target in clinical practice.

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.

Analysis of endometrial carcinoma TCGA reveals differences in DNA methylation in tumors from Black and White women

OBJECTIVE

Racial disparities exist in cancer patients both in incidence and death rates. In endometrial cancer, Black patients are reported to have higher incidence of aggressive endometrial cancer subtypes and higher death rates than White women. To date, diagnostic and prognostic biomarkers associated with race-specific methylation driven genes have yet to be identified. The objective of this study was to explore DNA methylation patterns in endometrial tumor samples from White and Black women.

METHODS

Differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) were identified in White tumor samples compared to Black tumor samples using Endometrial Carcinoma (EC) methylation and clinical data from The Cancer Genome Atlas (TCGA). Survival analysis was performed using survival R package and results were visualized using Kaplan-Meier plots. To access the correlation between changes in methylation and gene expression, we downloaded raw RNA-sequencing by Expectation-Maximization (RSEM) counts data from The Cancer Genome Atlas (TCGA) using TCGABiolinks package (v2.18.0).

RESULTS

Our analysis revealed 704 differentially methylated CpGs in tumors from Black and White women. These differentially methylated genes showed strong negative correlation with gene expression and statistically significant enrichment in regulatory regions such as DNase I hypersensitivity sites (DHSs) and transcription factor binding sites (TFBSs). Increased variability in methylation occurred in genes such as the insulin signaling pathway in Black tumor samples.

CONCLUSION

By using two independent statistical method based on means (DMR, DMCs) and variances (DVCs) on the endometrial carcinoma TCGA data, we showed that endometrial tumors from Black women are hypomethylated and more hypervariable than tumors from White women. In-depth investigation of these methylation driven markers can aid in successful management of endometrial cancer disparities and improved overall survival in Black and White populations.

Current Progress and Perspectives of CDC20 in Female Reproductive Cancers

The cancers of the cervix, endometrium, ovary, and breast are great threats to women's health. Cancer is characterized by the uncontrolled proliferation of cells and deregulated cell cycle progression is one of the main causes of malignancy. Agents targeting cell cycle regulators may have potential anti-tumor effects. CDC20 (cell division cycle 20 homologue) is a co-activator of the anaphase-promoting complex/cyclosome (APC/C) and thus acts as a mitotic regulator. In addition, CDC20 serves as a subunit of the mitotic checkpoint complex (MCC) whose function is to inhibit APC/C. Recently, higher expression of CDC20 has been reported in these cancers and was closely associated with their clinicopathological parameters, indicating CDC20 a potential target for cancer treatment that is worth further study. In the present review, we summarized current progress and put forward perspectives of CDC20 in female reproductive cancers.

New insights in ferroptosis: Potential therapeutic targets for the treatment of ischemic stroke

Stroke is a common disease in clinical practice, which seriously endangers people's physical and mental health. The neurovascular unit (NVU) plays a key role in the occurrence and development of ischemic stroke. Different from other classical types of cell death such as apoptosis, necrosis, autophagy, and pyroptosis, ferroptosis is an iron-dependent lipid peroxidation-driven new form of cell death. Interestingly, the function of NVU and stroke development can be regulated by activating or inhibiting ferroptosis. This review systematically describes the NVU in ischemic stroke, provides a comprehensive overview of the regulatory mechanisms and key regulators of ferroptosis, and uncovers the role of ferroptosis in the NVU and the progression of ischemic stroke. We further discuss the latest progress in the intervention of ferroptosis as a therapeutic target for ischemic stroke and summarize the research progress and regulatory mechanism of ferroptosis inhibitors on stroke. In conclusion, ferroptosis, as a new form of cell death, plays a key role in ischemic stroke and is expected to become a new therapeutic target for this disease.

Identification of DNA methylation-regulated differentially expressed genes in RA by integrated analysis of DNA methylation and RNA-Seq data

OBJECTIVE

To identify novel DNA methylation-regulated differentially expressed genes (MeDEGs) in RA by integrated analysis of DNA methylation and RNA-Seq data.

METHODS

The transcription and DNA methylation profiles of 9 RA and 15 OA synovial tissue were generated by RNA-Seq and Illumina 850K DNA methylation BeadChip. Gene set enrichment analysis (GSEA) and Weighted gene co-expression network analysis (WGCNA) were used to analyze methylation-regulated expressed genes by R software. The differentially expressed genes (DEGs), differentially methylated probes (DMPs), differentially methylated genes (DMGs) were analyzed by DESeq and ChAMP R package. The functional correlation of MeDEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction (PPI) network of MeDEGs was constructed by STRING and Reactome FI Cytoscape Plugin. Correlation analysis between methylation level and mRNA expression was conducted with R software.

RESULTS

A total of 17,736 genes, 25,578 methylated genes and 755,852 methylation probes were detected. A total of 16,421 methylation-regulated expressed genes were obtained. The GSEA showed that these genes are associated with activation of immune response, adaptive immune response, Inflammatory response in C5 (ontology gene sets). For KEGG analysis, these genes are associated with rheumatoid arthritis, NF-kappa B signaling pathway, T cell receptor signaling pathway. The WGCNA showed that the turquoise module exhibited the strongest correlation with RA (R = 0.78, P = 1.27 × 10^- 05), 660 genes were screened in the turquoise module. A total of 707 MeDEGs were obtained. GO analysis showed that MeDEGs were enriched in signal transduction, cell adhesion for BP, enriched in plasma membrane, integral component of membrane for CC, and enriched in identical protein binding, calcium ion binding for MF. The KEGG pathway analysis showed that the MeDEGs were enriched in calcium signaling pathway, T cell receptor signaling pathway, NF-kappa B signaling pathway, Rheumatoid arthritis. The PPI network containing 706 nodes and 882 edges, and the enrichment p value < 1.0 × 10^- 16. With Cytoscape, based on the range of more than 10 genes, a total of 8 modules were screened out. Spearman correlation analysis showed RGS1(cg10718027), RGS1(cg02586212), RGS1(cg10861751) were significantly correlated with RA.

CONCLUSIONS

RGS1 can be used as novel methylated biomarkers for RA.

Comprehensive bioinformatic analyses of lncRNA-mediated ceRNA network for uterine corpus endometrial carcinoma

Background

Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through a lncRNA-mediated ceRNA network.

Methods

The transcriptome profiles and corresponding clinical profiles of UCEC dataset were retrieved from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identified via "Edge R" package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor infiltrating immune cell (TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers.

Results

In the CPTAC dataset of UCEC, a ceRNA network comprised of 36 miRNAs, 123 lncRNAs and 124 targeted mRNAs was established, and 8 of 123 prognostic-related Differentially Expressed long noncoding RNAs (DElncRNAs) were identified. While in the TCGA dataset, a ceRNA network comprised of 38 miRNAs, 83 lncRNAs and 110 targeted mRNAs was established, and 2 of 83 prognostic-related DElncRNAs were identified. After filtered by risk grouping and Cox regression analysis, 10 prognostic-related lncRNAs including LINC00443, LINC00483, C2orf48, TRBV11-2, MEG-8 were identified. In addition, 33 survival-related Differentially Expressed messenger RNA (DEmRNAs) in two ceRNA networks were further validated in the Human Protein Atlas Portal (HPA) database. Finally, six lncRNA/miRNA/mRNA axes were established to elucidate prognostic regulatory roles in UCEC.

Conclusions

Several prognostic lncRNAs are identified and prognostic model of lncRNA-mediated ceRNA network is constructed, which promotes the understanding of UCEC development mechanisms and potential therapeutic targets.

Developing a novel DNA methylation risk score for survival and identification of prognostic gene mutations in endometrial cancer: a study based on TCGA data

Background

Few studies have focused on DNA methylation in endometrial cancer. The aim of our study is identify its role in endometrial cancer prognosis.

Methods

A publicly available dataset was retrieved from The Cancer Genome Atlas. For validation of expression alteration due to methylation, RNA sequencing data were obtained from other independent cohorts. MethSurv was used to search for candidate CpG probes, which were then filtered by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression analyses to identify final set of CpG probes for overall survival. A methylation-based risk model was developed and receiver operating characteristic analysis with area under curve was used for evaluation. Patients were divided into high- and low-risk groups using an optimal cut-off point. Comprehensive bioinformatic analyses were conducted to identify hub genes, key transcription factors, and enriched cancer-related pathways. Kaplan–Meier curve was used for survival analysis.

Results

A 5-CpG signature score was established. Its predictive value for 5-year overall survival was high, with area under curve of 0.828, 0.835 and 0.816 for the training, testing and entire cohorts. cg27487839 and cg12885678 had strong correlation with their gene expression, XKR6 and PTPRN2, and lower PTPRN2 expression was associated with poorer survival in both The Cancer Genome Atlas and the validation datasets. Low-risk group was associated with significantly better survival. Low-risk group harboured more mutations in hub genes and key transcription factors, and mutations in SP1 and MECP2 represented favourable outcome.

Conclusion

We developed a methylation-based prognostic stratification system for endometrial cancer. Low-risk group was associated with better survival and harboured more mutations in the key regulatory genes.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

A ten-gene methylation signature as a novel biomarker for improving prediction of prognosis and indicating gene targets in endometrial cancer

Endometrial cancer (EC) is a common female reproductive tumor worldwide. Nonetheless, the pathogenesis of EC still remains ambiguous and associated epigenetic mechanism still to be explored. The goal of this study is to investigate whether gene methylation signature is associated with overall survival (OS) for EC patients. In this study, a 10-gene methylation risk model was built and the OS in high- and low-risk groups was significant different. The area under the ROC curve (AUC) of this model was 0.856 at 5 years survival. The nomogram could accurately predict the OS in EC patients, with concordance index and AUC at 5 year survival reached 0.796 and 0.792, respectively. Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan-Meier survival curve also proved to be significantly different (p < 0.01). WGCNA revealed a key gene group for the model and further bioinformatics analysis indicated 6 genes as the hub genes in the module. Knockdown of MMP12 inhibited the proliferation, invasion and metastasis of EC cells. After all, a methylation signature and a nomogram based on this signature were constructed, and they could both predict survival in patients with EC. Moreover, WGCNA model identified MMP12 as a potential target for the treatment of EC.

Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.

Construction of miRNA-mRNA Regulatory Network and Prognostic Signature in Endometrial Cancer

Introduction

This bioinformatic study confirmed a new miRNA-mRNA regulatory network and a prognostic signature in endometrial cancer (EC).

Materials and Methods

We downloaded RNA-seq and miRNA-seq data of EC from the TCGA database, then used EdegR package to screen differentially expressed miRNAs and mRNAs (DE-miRNAs and DE-mRNAs). Then, we constructed a regulatory network of EC-associated miRNAs and hub genes by Cytoscape, and determined the expression of unexplored miRNAs in EC tissues and normal adjacent tissues by quantitative Real-Time PCR (qRT-PCR). A prognostic signature model and a predictive nomogram were constructed. Finally, we explored the association between the prognostic model and the immune cell infiltration.

Results

A total of 11,531 DE-mRNAs and 236 DE-miRNAs, as well as 275 and 118 candidate DEGs for upregulated and downregulated DE-miRNAs were screened out. The miRNA-mRNA network included 5 downregulated and 13 upregulated DE-miRNAs. qRT-PCR proved that the expression levels of miRNA-18a-5p, miRNA-18b-5p, miRNA-449c-5p and miRNA-1224-5p and their target genes (NR3C1, CTGF, MYC, and TNS1) were consistent with our predictions. Univariate and multivariate Cox proportional hazards regression analyses of the hub genes revealed a significant prognostic value of NR3C1, EZH2, AND GATA4, and these genes were closely related to eight types of immune infiltration cells.

Conclusion

We identified three genes as candidate biomarkers for EC, which may provide a theoretical basis for targeted therapy.

TCGA dataset screening for genes implicated in endometrial cancer using RNA-seq profiling

The molecular basis of the mechanism and the potential biomarkers of endometrial cancer (EC) remain to be studied. In the present study, we hypothesized that the comprehensive characterization of transcriptional changes in EC could help achieve this aim. By taking advantage of RNA-seq data from The Cancer Genome Atlas, we determined the profile of differently expressed genes (DEGs) between EC tumor tissues and normal samples. On this basis, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways enrichment analyses. The interacting partners for each of the DEGs were explored and a protein-protein interaction network was constructed. Consequently, 10 hub genes were identified and their association with mortality in EC patients was investigated. The genes, AURKA, CENPA, and KIF2C, were found to be potential biomarkers for EC with a significant prognostic effect. Our work provided a basis for EC studies in both biological and clinical settings.

Sex-biased DNA methylation in papillary thyroid cancer

Background: Women have a higher risk of developing papillary thyroid cancer (PTC) than men. DNA methylation is known to differ between men and women. Materials & methods: Using the human methylation 450 BeadChip and RNA-sequence, we profiled the genome-wide DNA methylation patterns of papillary thyroid carcinoma patients and para-carcinoma tissue. Results: We first identified 398 different expression genes (DEGs) between males and females PTC. Then we analyzed the relationship between differentially methylated positions (DMPs) and DEGs at gene promoter regions and identified 39 genes and explored DMP-DEGs' correlation with immune cell infiltration and tumor purity. We also analyzed the relationship between genomic regions and enhancers. Conclusion: Our study identified 39 DMP-DEGs providing some new insights into the mechanisms of methylation-mediated gender differences in PTC.

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.

Identification of a five-gene signature for predicting the progression and prognosis of stage I endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is often diagnosed at an early clinical stage based on abnormal vaginal bleeding. However, the prognosis of UCEC is poor. The present study was conducted to identify novel tumor grade-related genes with the potential to predict the prognosis and progression of UCEC. A total of three gene expression microarray datasets were downloaded from the Gene Expression Omnibus database, and one RNA-sequencing dataset with corresponding clinical information of patients with UCEC was obtained from The Cancer Genome Atlas database. In summary, 1,447 differentially expressed genes (DEGs) were identified between endometrial cancerous tissues and normal endometrial tissues. Weighted gene co-expression network analysis was performed to assess the associations between DEGs and clinical traits. In total, five genes were found to be highly associated with the tumorigenesis and prognosis of UCEC. Among them, BUB1 mitotic checkpoint serine/threonine kinase B, cyclin B1, cell-division cycle protein 20 and non-SMC condensing I complex subunit G were involved in cell cycle regulation pathways, and DLG-associated protein 5 was involved in the Notch receptor 3 signaling pathway based on functional enrichment analyses. Of the five genes, four were highly expressed in endometrial cancerous tissues compared with normal endometrial tissues at the protein level. In addition, the higher expression of these genes predicted a higher tumor grade and worse overall survival. In conclusion, the present study revealed a 5-gene signature that can be used to predict the progression of UCEC.

Identification of core genes in the progression of endometrial cancer and cancer cell-derived exosomes by an integrative analysis

Endometrial cancer is one of the most prevalent tumors of the female reproductive system causing serious health effects to women worldwide. Although numerous studies, including analysis of gene expression profile and cellular microenvironment have been reported in this field, pathogenesis of this disease remains unclear. In this study, we performed a system bioinformatics analysis of endometrial cancer using the Gene Expression Omnibus (GEO) datasets (GSE17025, GSE63678, and GSE115810) to identify the core genes. In addition, exosomes derived from endometrial cancer cells were also isolated and identified. First, we analyzed the differentially expressed genes (DEGs) between endometrial cancer tissues and normal tissues in clinic samples. We found that HAND2-AS1, PEG3, OGN, SFRP4, and OSR2 were co-expressed across all 3 datasets. Pathways analysis showed that several pathways associated with endometrial cancer, including "p53 signaling pathway", "Glutathione metabolism", "Cell cycle", and etc. Next, we selected DEGs with highly significant fold change and co-expressed across the 3 datasets and validated them in the TCGA database using Gene Expression Profiling Interactive Analysis (GEPIA). Finally, we performed a survival analysis and identified four genes (TOP2A, ASPM, EFEMP1, and FOXL2) that play key roles in endometrial cancer. We found up-regulation of TOP2A and ASPM in endometrial cancer tissues or cells, while EFEMP1 and FOXL2 were down-regulated. Furthermore, we isolated exosomes from the culturing supernatants of endometrial cancer cells (Ishikawa and HEC-1-A) and found that miR-133a, which regulates expression of FOXL2, were present in exosomes and that they could be delivered to normal endometrial cells. The common DEGs, pathways, and exosomal miRNAs identified in this study might play an important role in progression as well as diagnosis of endometrial cancer. In conclusion, our results provide insights into the pathogenesis and risk assessment of endometrial cancer. Even so, further studies are required to elucidate on the precise mechanism of action of these genes in endometrial cancer.

A 14-Methylation-Driven Differentially Expressed RNA as a Signature for Overall Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma

DNA methylation has been implicated as an important mechanism for the development of uterine corpus endometrial carcinoma (UCEC), indicating that methylation-driven genes may be potential biomarkers for survival prediction. In this study, we aimed to identify a new prognostic methylation signature for UCEC based on differentially expressed genes (DEGs) and long noncoding RNAs (lncRNAs) (DELs). Sample-matched RNA-sequencing and methylation-array data were downloaded from The Cancer Genome Atlas database, by analysis of which a total of 269 DEGs and 4 DELs were identified to be methylation driven. Least absolute shrinkage and selection operator analysis screened that 14 methylation-driven genes were significantly associated with overall survival (OS) and thus were used as a signature to establish a prognostic risk model. Based on the median threshold, the patients were divided into the low-risk and the high-risk groups, which showed significantly different survival periods under the Kaplan-Meier curve. The area under receiver operating characteristic curve (AUC) was 0.934, 0.919, and 0.952 for the training, validation, and entire cohort, respectively. Stratification analysis showed that the established risk model may add prognostic values to conventional clinical factors (age, neoplasm histologic grade, and clinical stage). A nomogram was constructed based on the risk model and clinical parameters, with the AUC of 0.978 and c-index of 0.8079. Database for Annotation, Visualization, and Integrated Discovery (DAVID) function enrichment and Human Protein Atlas (HPA) protein expression validation showed 5 of these 14 genes may be especially important for UCEC (hypermethylated lowly expressed: CCBE1, FOXL2, PHLDB2, and DTNA; hypomethylated highly expressed: CCNE1). Comparison with breast cancer in the methylation level indicated ABCA12, CCNE1, and CLRN3 may be specific methylation-driven genes for UCEC. LncRNA HCG11 may function by coexpressing with DTNA. In conclusion, this 14-DNA methylation signature combined with clinical factors may a potentially effective biomarker in predicting OS for UCEC patients.

Expression of Stromal Progesterone Receptor and Differential Methylation Patterns in the Endometrium May Correlate with Response to Progesterone Therapy in Endometrial Complex Atypical Hyperplasia

Progesterone therapy is a viable treatment for complex atypical hyperplasia (CAH) and endometrial adenocarcinoma, though reliable molecular determinants of response are not available. To explore if analysis of pre-therapy endometrial biopsies could yield biomarkers of response to progesterone, patients with CAH or adenocarcinoma undergoing treatment with progestins were included in this cross-sectional study. Immunohistochemistry for progesterone receptor (PR) was performed. Manual PR expression scores (PRES) were first calculated for biopsies by counting PR-positive nuclei in 12 sensitive vs 9 resistant samples. Significant differences in manual PRES were detected in the stroma (p < 0.01) and total endometrium (p < 0.01) for sensitive vs resistant patients. Manual PRES in the stroma had the highest accuracy in segregating sensitive vs resistant patients (96%). Differences in epithelial PRES were not significant. To validate these findings, a correlation between manual PRES and visual PRES was performed in the 21 patients. An additional 11 patients were analyzed to test if visual PRES would be predictive of response to progesterone. Visual PRES in epithelia and stroma in the 32 specimens was calculated. Significant differences in visual PRES were detected in the stroma for sensitive vs resistant samples (p < 0.01), while differences in epithelial and total endometrium were not significant. Whole genome bisulfite sequencing was performed on DNA isolated using pre-therapy biopsies from 6 sensitive and 6 resistant patients in this cohort. Differentially methylated regions were identified in the stroma and epithelium when evaluating sensitive vs resistant samples. Pathways involved in cell adhesion demonstrated the greatest difference in methylation in these samples.

Prediction of crucial epigenetically‑associated, differentially expressed genes by integrated bioinformatics analysis and the identification of S100A9 as a novel biomarker in psoriasis

Psoriasis is one of the most common immune-mediated inflammatory diseases of the skin. The identification of the pivotal molecular mechanisms responsible for the disease pathogenesis may lead to the development of novel therapeutic options. The present study aimed to identify pivotal differentially expressed genes (DEGs) and methylated DEGs in psoriasis. The raw data from gene microarrays were obtained from the Gene Expression Omnibus database. The data were processed using packages in Bioconductor. In total, 352 upregulated and 137 downregulated DEGs were identified. The upregulated DEGs were primarily enriched in the 'innate immune defense' response and the 'cell cycle'. The down-regulated DEGs were primarily enriched in 'cell adhesion' and 'tight junction pathways'. A total of 95 methylated DEGs were identified, which were significantly enriched in the 'interleukin (IL)-17 signaling pathway' and the 'response to interferon'. Based on a comprehensive evaluation of all algorithms in cytoHubba, the key epigenetic-associated hub genes (S100A9, SELL, FCGR3B, MMP9, S100A7, IL7R, IRF7, CCR7, IFI44, CXCL1 and LCN2) were screened out. In order to further validate these genes, the present study constructed a model of imiquimod (IMQ)-induced psoriasiform dermatitis using mice. The levels of these hub genes were increased in the IMQ group. The knockdown of methylation-regulating enzyme ten-eleven translocation (TET) 2 expression in mice attenuated the expression levels of S100A9, SELL, IL7R, MMP9, CXCL1 and LCN2. Furthermore, the hydroxymethylated level of S100A9 was highly expressed in the IMQ group and was significantly decreased by TET2 deficiency in mice. On the whole, using an integrative system bioinformatics approach, the present study identified a series of characteristic enrichment pathways and key genes that may serve as potential biomarkers in psoriasis.