Identification of endometrial cancer methylation features using combined methylation analysis methods

DNA methylation of selected tumor suppressor genes in endometrial hyperplasia

AIMS

To investigate DNA methylation of specific gene promoters in endometrial hyperplasia compared to normal endometrial tissue.

MATERIALS AND METHODS

To search for epigenetic events, methylation-specific multiplex ligation-dependent probe amplification was employed to compare the methylation status of 64 tissue samples with atypical endometrial hyperplasia, 60 tissue samples with endometrial hyperplasia without atypia, and 40 control tissue samples with normal endometrium.

RESULTS

Differences in DNA methylation among the groups were found in PTEN, CDH13, and MSH6 promoters (PTEN: atypical hyperplasia 32%, benign hyperplasia 6.8%, normal endometrium 10%; P=0.004; CDH13: atypical hyperplasia, 50%; benign hyperplasia, 43%; normal endometrium 8.1%; P=0.003; MSH6 atypical hyperplasia 84%, benign hyperplasia, 62%; normal endometrium, 52%; P=0.008.) Higher rates of CDH13 promoter methylation were identified in the groups with both forms of endometrial hyperplasia when compared to the control group (atypical hyperplasia, P=0.003, benign hyperplasia, P=0.0002). A higher rate of DNA methylation of the PTEN and MSH6 promoters was observed in samples with atypical endometrial hyperplasia than in samples with benign endometrial hyperplasia (PTEN: P=0.02; MSH6: P=0.01) and samples with normal endometrial tissue (PTEN, P=0.04; MSH6, P=0.006).

CONCLUSION

DNA methylation of CDH13, PTEN, and MSH6 appear to be involved in the development of endometrial hyperplasia.

Wakefield M. The role of aberrant DNA methylation in cancer initiation and clinical impacts

Epigenetic alterations, including aberrant DNA methylation, are now recognized as bone fide hallmarks of cancer, which can contribute to cancer initiation, progression, therapy responses and therapy resistance. Methylation of gene promoters can have a range of impacts on cancer risk, clinical stratification and therapeutic outcomes. We provide several important examples of genes, which can be silenced or activated by promoter methylation and highlight their clinical implications. These include the mismatch DNA repair genes MLH1 and MSH2, homologous recombination DNA repair genes BRCA1 and RAD51C, the TERT oncogene and genes within the P15/P16/RB1/E2F tumour suppressor axis. We also discuss how these methylation changes might occur in the first place - whether in the context of the CpG island methylator phenotype or constitutional DNA methylation. The choice of assay used to measure methylation can have a significant impact on interpretation of methylation states, and some examples where this can influence clinical decision-making are presented. Aberrant DNA methylation patterns in circulating tumour DNA (ctDNA) are also showing great promise in the context of non-invasive cancer detection and monitoring using liquid biopsies; however, caution must be taken in interpreting these results in cases where constitutional methylation may be present. Thus, this review aims to provide researchers and clinicians with a comprehensive summary of this broad, but important subject, illustrating the potentials and pitfalls of assessing aberrant DNA methylation in cancer.

erarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

Genome-wide DNA methylation profiling of stomach cancer in the ethnic population of Mizoram, North East India

Stomach cancer is the fifth most common cancer in terms of prevalence and incidence and the fourth leading cause of mortality in men and women worldwide. It is well-established that aberrant DNA methylation in cells can lead to carcinogenesis. The primary objective of our study was to investigate the aberrant DNA methylation status of genes associated with stomach cancer with a particular reference to the ethnic population of Mizoram, North East India. The site-level analysis identified 2883 CpG sites differentially methylated, representing ~922 genes. Out of which 476 Differentially Methylated Positions (DMPs) were promoter-associated, 452 DMPs were hypermethylated, and 24 were hypomethylated. The region-level analysis identified 462 Differentially Methylated Regions (DMRs) corresponding to ~320 genes, of which ~281 genes were hypermethylated and ~ 40 genes were hypomethylated. TCGA analysis showed that some of the genes had been previously implicated in other cancers including stomach cancer. Five hypermethylated genes were selected as candidate genes for further investigations and they have shown to be novel and could serve as candidate hypermethylation biomarkers for stomach cancer in this particular ethnic group.

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.

Methylation of the PTENP1 pseudogene as potential epigenetic marker of age-related changes in human endometrium

The processed pseudogene PTENP1 is involved in the regulation of the expression of the PTEN and acts as a tumor suppressor in many types of malignances. In our previous study we showed that PTENP1 methylation is present not only in tumor, but also in normal endometrium tissues of women over 45 years old. Here we used methylation-specific PCR to analyze methylation status of CpG island located near promoter region of PTENP1 in malignant and non-malignant endometrium tissues collected from 236 women of different age groups. To confirm our results, we also analyzed RNA sequencing and microarray data from 431 women with endometrial cancer from TCGA database. We demonstrated that methylation of PTENP1 is significantly increased in older patients. We also found an age-dependent increase in the level of PTENP1 expression in endometrial tissue. According to our data, PTENP1 methylation elevates the level of the pseudogene sense transcript. In turn, a high level of this transcript correlates with a more favorable prognosis in endometrial cancer. The data obtained suggested that PTENP1 methylation is associated with age-related changes in normal and hyperplastic endometrial tissues. We assumed that age-related increase in PTENP1 methylation and subsequent elevation of its expression may serve as a protective mechanism aimed to prevent malignant transformation of endometrial tissue in women during the perimenopause, menopause, and postmenopause periods.

Some Molecular and Cellular Stress Mechanisms Associated with Neurodegenerative Diseases and Atherosclerosis

Chronic stress is a combination of nonspecific adaptive reactions of the body to the influence of various adverse stress factors which disrupt its homeostasis, and it is also a corresponding state of the organism's nervous system (or the body in general). We hypothesized that chronic stress may be one of the causes occurence of several molecular and cellular types of stress. We analyzed literary sources and considered most of these types of stress in our review article. We examined genes and mutations of nuclear and mitochondrial genomes and also molecular variants which lead to various types of stress. The end result of chronic stress can be metabolic disturbance in humans and animals, leading to accumulation of reactive oxygen species (ROS), oxidative stress, energy deficiency in cells (due to a decrease in ATP synthesis) and mitochondrial dysfunction. These changes can last for the lifetime and lead to severe pathologies, including neurodegenerative diseases and atherosclerosis. The analysis of literature allowed us to conclude that under the influence of chronic stress, metabolism in the human body can be disrupted, mutations of the mitochondrial and nuclear genome and dysfunction of cells and their compartments can occur. As a result of these processes, oxidative, genotoxic, and cellular stress can occur. Therefore, chronic stress can be one of the causes forthe occurrence and development of neurodegenerative diseases and atherosclerosis. In particular, chronic stress can play a large role in the occurrence and development of oxidative, genotoxic, and cellular types of stress.

Establishment and validation of a prognostic nomogram based on a novel five-DNA methylation signature for survival in endometrial cancer patients

Background

This study aimed to explore the prognostic role of DNA methylation pattern in endometrial cancer (EC) patients.

Methods

Differentially methylated genes (DMGs) of EC patients with distinct survival from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylated genes as biomarkers for EC prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to construct a risk score model. A nomogram was built based on analysis combining the risk score model with clinicopathological signatures together, and then verified in the validation cohort and patients in our own center.

Results

In total, 157 DMGs were identified between different prognostic groups. Based on the LASSO analysis, five genes (GBP4, OR8K3, GABRA2, RIPPLY2, and TRBV5‐7) were screened for the establishment of risk score model. The model outperformed in prognostic accuracy at varying follow‐up times (AUC for 3 years: 0.824, 5 years: 0.926, and 7 years: 0.853). Multivariate analysis identified four independent risk factors including menopausal status (HR = 3.006, 95%CI: 1.062–8.511, p = 0.038), recurrence (HR = 2.116, 95%CI: 1.061–4.379, p = 0.046), lymph node metastasis (LNM, HR = 3.465, 95%CI: 1.225–9.807, p = 0.019), and five‐DNA methylation risk model (HR = 3.654, 95%CI: 1.458–9.161, p = 0.006) in training cohort. The performance of the nomogram was good in the training (AUC = 0.828), validation (AUC = 0.866) and the whole cohorts (AUC = 0.843). 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). The subgroup analysis in different stratifications indicated that the accuracy was high in different subgroups for age, histological type, tumor grade, and clinical stage (all p < 0.01).

Conclusions

Briefly, our work established and verified a five‐DNA methylation risk model, and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of EC patients for clinicians.

Estrogen-ERα signaling and DNA hypomethylation co-regulate expression of stem cell protein PIWIL1 in ERα-positive endometrial cancer cells

Background

We previously identified PIWIL1 as an oncogene involved in endometrial carcinogenesis. However, the mechanism of Piwil1 mediated regulation of tumorigenesis remains poorly understood.

Methods

The expression levels of target genes in endometrial cancer cells were detected by quantitative reverse transcription-PCR (RT-qPCR) and western blotting. Up- or down-regulation of ERα or PIWIL1 was achieved by transient transfection with expressing plasmids or short hairpin RNA (shRNA). Dual-luciferase reporter assays and chromatin immunoprecipitation (ChIP) were used to demonstrate the ERα bound to the half estrogen response element (half-ERE) located in PIWIL1 promoter. The expression of PIWIL1 and ERα in endometrial carcinoma tissues were investigated using immunohistochemistry and RT-qPCR. The proliferation ability of cancer cells were evaluated by MTT. Methylation status of the PIWIL1 promoter was detected by bisulfite sequencing PCR (BSP).

Results

In the present study, we found that PIWIL1 mediated E₂-stimulated cancer cell proliferation. In ERα-positive endometrial cancer cells, we demonstrated that estrogen-ERα signaling significantly up-regulated the expression of PIWIL1, which was mediated by binding of the ERα onto the PIWIL1 promoter. Furthermore, we found that a half-ERE in the PIWIL1 promoter was essential for ERα binding. The PIWIL1 promoter was hypomethylated in ERα-positive endometrial cancer cells. Treatment with 5-aza-deoxycytidine (5-aza-dC) could up-regulate PIWIL1 expression.

Conclusions

These findings uncover a novel molecular mechanism by which estrogen-ERα signaling and DNA hypomethylation co-regulate PIWIL1 expression. These findings provide novel insights into the hormonal regulation of PIWIL1 in endometrial cancer and the PIWIL1’s role in estrogen-stimulated endometrial carcinogenesis.

GenomeForest: An Ensemble Machine Learning Classifier for Endometriosis

Endometriosis is a complex and high impact disease affecting 176 million women worldwide with diagnostic latency between 4 to 11 years due to lack of a definitive clinical symptom or a minimally invasive diagnostic method. In this study, we developed a new ensemble machine learning classifier based on chromosomal partitioning, named GenomeForest and applied it in classifying the endometriosis vs. the control patients using 38 RNA-seq and 80 enrichment-based DNA-methylation (MBD-seq) datasets, and computed performance assessment with six different experiments. The ensemble machine learning models provided an avenue for identifying several candidate biomarker genes with a very high F1 score; a near perfect F1 score (0.968) for the transcriptomics dataset and a very high F1 score (0.918) for the methylomics dataset. We hope in the future a less invasive biopsy can be used to diagnose endometriosis using the findings from such ensemble machine learning classifiers, as demonstrated in this study.

Genome-wide DNA methylation profile of early-onset endometrial cancer: its correlation with genetic aberrations and comparison with late-onset endometrial cancer

The present study was performed to clarify the significance of DNA methylation alterations during endometrial carcinogenesis. Genome-wide DNA methylation analysis and targeted sequencing of tumor-related genes were performed using the Infinium MethylationEPIC BeadChip and the Ion AmpliSeq Cancer Hotspot Panel v2, respectively, for 31 samples of normal control endometrial tissue from patients without endometrial cancer and 81 samples of endometrial cancer tissue. Principal component analysis revealed that tumor samples had a DNA methylation profile distinct from that of control samples. Gene Ontology enrichment analysis revealed significant differences of DNA methylation at 1034 CpG sites between early-onset endometrioid endometrial cancer (EE) tissue (patients aged ≤40 years) and late-onset endometrioid endometrial cancer (LE) tissue, which were accumulated among 'transcriptional factors'. Mutations of the CTNNB1 gene or DNA methylation alterations of genes participating in Wnt signaling were frequent in EEs, whereas genetic and epigenetic alterations of fibroblast growth factor signaling genes were observed in LEs. Unsupervised hierarchical clustering grouped EE samples in Cluster EA (n = 22) and samples in Cluster EB (n = 12). Clinicopathologically less aggressive tumors tended to be accumulated in Cluster EB, and DNA methylation levels of 18 genes including HOXA9, HOXD10 and SOX11 were associated with differences in such aggressiveness between the two clusters. We identified 11 marker CpG sites that discriminated EB samples from EA samples with 100% sensitivity and specificity. These data indicate that genetically and epigenetically different pathways may participate in the development of EEs and LEs, and that DNA methylation profiling may help predict tumors that are less aggressive and amenable to fertility preservation treatment.

Downregulation of SMOC2 expression in papillary thyroid carcinoma and its prognostic significance

Secreted Protein Acidic and Rich in Cysteine (SPARC)-related modular calcium-binding protein-2 (SMOC2), a secreted matricellular protein, is reported to be involved in various processes related to cancer progression such as regulating the cell cycle, angiogenesis, and invasion. However, its expression and prognostic significance in papillary thyroid carcinomas (PTCs) remains unknown. Using immunohistochemistry, we evaluated the expression profile of SMOC2 and its prognostic value in a large cohort of PTCs. Real time-PCR analysis with fresh-frozen tissues showed that SMOC2 mRNA expression in PTCs was substantially lower than the expression in matched non-cancerous thyroid tissues, consistent with the results from thyroid cancer cell lines. Immunohistochemical analysis demonstrated that SMOC2 was normally present in thyroid follicular epithelial cells and the expression level was maintained in nodular hyperplasia. However, SMOC2 expression was significantly lower in lymphocytic thyroiditis and follicular tumors including follicular adenomas and carcinomas. In particular, 38% of PTCs exhibited a complete loss of SMOC2 expression, which was associated with the presence of BRAF (V600E) mutation. Moreover, SMOC2 further declined during lymph node metastasis in PTCs. DNA methylation chip analysis revealed one hypermethylated CpG site in the promoter region of SMOC2 gene, suggesting an epigenetic regulation of SMOC2 in PTCs. Remarkably SMOC2 positivity was associated with improved recurrence-free survival along with female sex, tumor size, and the N stage. However, SMOC2 was not identified as an independent prognostic marker in multivariate analyses. Taken together, SMOC2 expression is significantly down-regulated in PTCs and SMOC2 positivity is closely associated with better clinical outcomes, suggesting that SMOC2 can be a prognostic marker in PTC patients.

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data

Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.

Molecular Modifiers of Hormone Receptor Action: Decreased Androgen Receptor Expression in Mismatch Repair Deficient Endometrial Endometrioid Adenocarcinoma

Endometrial endometrioid carcinoma is related to estrogen excess and expression of estrogen and progesterone receptors. Epidemiological evidence suggests that exposure to elevated androgens, as in polycystic ovarian syndrome, increases the risk of endometrial cancer. Factors impacting androgen receptor (AR) expression are not well studied. Mismatch repair (MMR) deficiency due to MLH1 gene methylation is one of the most common molecular alterations in endometrial cancer, occurring in 15% to 20% of cases. MLH1 methylation can be associated with decreased expression of other genes, so we examined the effect of MMR status on AR expression. As NF-κB is known to induce AR, this transcription factor was also examined. Three hundred forty-four unselected endometrial carcinomas were evaluated for DNA MMR. Loss of expression of MLH1 with MLH1 methylation was defined as MMR deficient, and positive expression of MMR proteins was defined as MMR intact. A case-control cohort of 96 grade 2 endometrioid carcinomas was studied from this set (47 MMR deficient, 49 MMR intact). Cases were matched for histotype, grade, and age. AR and NF-κB immunohistochemical expression were evaluated by 2 different scoring systems (CAP/ASCO and Allred) used for estrogen receptor. Despite higher levels of NF-κB, MMR deficiency was associated with a significantly lower mean percentage of AR expression. The MMR deficient group had more variable AR expression, with more cases scoring on the lower end of the spectrum. These findings have implications for clinical trials of AR antagonists in gynecologic cancers.

Identification of DNA methylation associated gene signatures in endometrial cancer via integrated analysis of DNA methylation and gene expression systematically

Objective

Endometrial cancer (EC) is a common gynecologic cancer worldwide. However, the pathogenesis of EC has not been epigenetically elucidated. Here, this study aims to describe the DNA methylation profile and identify favorable gene signatures highly associated with aberrant DNA methylation changes in EC.

Methods

The data regarding DNA methylation and gene expression were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially methylated CpG sites (DMCs), differentially methylated regions (DMRs), and differentially expressed genes (DEGs) were identified, and the relationship between the 2 omics was further analyzed. In addition, weighted CpG site co-methylation network (WCCN) was constructed followed by an integrated analysis of DNA methylation and gene expression data.

Results

Four hundred thirty-one tumor tissues and 46 tissues adjacent tumor of EC patients were analyzed. One thousand one hundred thirty-five DMCs (merging to 10 DMRs), and 1,488 DEGs were obtained between tumor and normal groups, respectively. One hundred forty-eight DMCs-DEGs correlated pairs and 13 regional DMCs-DEGs pairs were obtained. Interestingly, we found that some hub genes in 2 modules among 8 modules of WCCN analysis were down-regulated in tumor samples. Furthermore, protocadherins (PCDHs) clusters, DDP6, TNXB, and ZNF154 were identified as novel deregulated genes with altered methylation in EC.

Conclusion

Based on the analysis of DNA methylation in a systematic view, the potential long-range epigenetic silencing (LRES) composed of PCDHs was reported in ECs for the first time. PCDHs clusters, DDP6, and TNXB were firstly found to be associated with tumorigenesis, and may be novel candidate biomarkers for EC.

An integrated lncRNA, microRNA and mRNA signature to improve prognosis prediction of colorectal cancer

Although the outcome of patients with colorectal cancer (CRC) has improved significantly, prognosis evaluation still presents challenges due to the disease heterogeneity. Increasing evidences revealed the close correlation between aberrant expression of certain RNAs and the prognosis. We envisioned that combined multiple types of RNAs into a single classifier could improve postoperative risk classification and add prognostic value to the current stage system. Firstly, differentially expressed RNAs including mRNAs, miRNAs and lncRNAs were identified by two different algorithms. Then survival and LASSO analysis was conducted to screen survival-related DERs and build a multi-RNA-based classifier for CRC patient stratification. The prognostic value of the classifier was self-validated in the TCGA CRC cohort and further validated in an external independent set. Finally, survival receiver operating characteristic analysis was used to assess the performance of prognostic prediction. We found that the multi-RNA-based classifier consisted by 12 mRNAs, 1miRNA and 1 lncRNA, which could divide the patients into high and low risk groups with significantly different overall survival (training set: HR 2.54, 95%CI 1.67-3.87, p<0.0001; internal testing set: HR 2.54, 95%CI 1.67-3.87, p<0.0001; validation set: HR 5.02, 95% CI 2.2–11.6; p=0·0002). In addition, the classifier is not only independent of clinical features but also with a similar prognostic ability to the well-established TNM stage (AUC of ROC 0.83 versus 0.74, 95% CI = 0.608-0.824, P =0.0878). Furthermore, combination of the multi-RNA-based classifier with clinical features was a more powerful predictor of prognosis than either of the two parameters alone. In conclusion, the multi-RNA-based classifier may have important clinical implications in the selection of patients with CRC who are at high risk of mortality and add prognostic value to the current stage system.