The association between CDH1 promoter methylation and patients with ovarian cancer: a systematic meta-analysis

Multi-Organ Nutrigenomic Effects of Dietary Grapes in a Mouse Model

As a whole food, the potential health benefits of table grapes have been widely studied. Some individual constituents have garnered great attention, particularly resveratrol, but normal quantities in the diet are meniscal. On the other hand, the grape contains hundreds of compounds, many of which have antioxidant potential. Nonetheless, the achievement of serum or tissue concentrations of grape antioxidants sufficient to mediate a direct quenching effect is not likely, which supports the idea of biological responses being mediated by an indirect catalytic-type response. We demonstrate herein with Hsd:ICR (CD-1® Outbred, 18-24 g, 3-4 weeks old, female) mice that supplementation of a semi-synthetic diet with a grape surrogate, equivalent to the human consumption of 2.5 servings per day for 12 months, modulates gene expression in the liver, kidney, colon, and ovary. As might be expected when sampling changes in a pool of over 35,000 genes, there are numerous functional implications. Analysis of some specific differentially expressed genes suggests the potential of grape consumption to bolster metabolic detoxification and regulation of reactive oxygen species in the liver, cellular metabolism, and anti-inflammatory activity in the ovary and kidney. In the colon, the data suggest anti-inflammatory activity, suppression of mitochondrial dysfunction, and maintaining homeostasis. Pathway analysis reveals a combination of up- and down-regulation in the target tissues, primarily up-regulated in the kidney and down-regulated in the ovary. More broadly, based on these data, it seems logical to conclude that grape consumption leads to modulation of gene expression throughout the body, the consequence of which may help to explain the broad array of activities demonstrated in diverse tissues such as the brain, heart, eye, bladder, and colon. In addition, this work further supports the profound impact of nutrigenomics on mammalian phenotypic expression.

Dynamic changes of DNA methylation induced by benzo(a)pyrene in cancer

Benzo(a)pyrene (BaP), the earliest and most significant carcinogen among polycyclic aromatic hydrocarbons (PAHs), has been found in foods, tobacco smoke, and automobiles exhaust, etc. Exposure to BaP induced DNA damage directly, or oxidative stress-related damage, resulting in cell apoptosis and carcinogenesis in human respiratory system, digestive system, reproductive system, etc. Moreover, BaP triggered genome-wide epigenetic alterations by methylation, which might cause disturbances in regulation of gene expression, and thereby induced cancer. It has been proved that BaP reduced genome-wide DNA methylation, and activated proto-oncogene by hypomethylation in the promoter region, but silenced tumor suppressor genes by promoter hypermethylation, resulting in cancer initiation and progression. Here we summarized the changes in DNA methylation in BaP exposure, and revealed the methylation of DNA plays a role in cancer development.

Dual Hyper-Graph Regularized Supervised NMF for Selecting Differentially Expressed Genes and Tumor Classification

Non-negative matrix factorization (NMF) is a dimensionality reduction technique based on high-dimensional mapping. It can learn part-based representations effectively. In this paper, we propose a method called Dual Hyper-graph Regularized Supervised Non-negative Matrix Factorization (HSNMF). To encode the geometric information of the data, the hyper-graph is introduced into the model as a regularization term. The advantage of hyper-graph learning is to find higher order data relationship to enhance data relevance. This method constructs the data hyper-graph and the feature hyper-graph to find the data manifold and the feature manifold simultaneously. The application of hyper-graph theory in cancer datasets can effectively find pathogenic genes. The discrimination information is further introduced into the objective function to obtain more information about the data. Supervised learning with label information greatly improves the classification effect. Furthermore, the real datasets of cancer usually contain sparse noise, so the L_2,1-norm is applied to enhance the robustness of HSNMF algorithm. Experiments under The Cancer Genome Atlas (TCGA) datasets verify the feasibility of the HSNMF method.

Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification

Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the priori label information of different classes. In this paper, a novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification. The advantage of hyper-graph learning is to capture local spatial information in high dimensional data. This method incorporates a hyper-graph regularization constraint to consider the higher order data sample relationships. The application of hyper-graph theory can effectively find pathogenic genes in cancer datasets. Besides, the label information is further incorporated in the objective function to improve the discriminative ability of the decomposition matrix. Supervised learning with label information greatly improves the classification effect. We also provide the iterative update rules and convergence proofs for the optimization problems of HCNMF. Experiments under The Cancer Genome Atlas (TCGA) datasets confirm the superiority of HCNMF algorithm compared with other representative algorithms through a set of evaluations.

Germline mutations in Thai patients with nonmucinous epithelial ovarian cancer

BACKGROUND

Genetic testing is widely recommended for all epithelial ovarian cancer (EOC) patients. However, an increased probability of identifying germline mutations has been reported in selected patients with risk factors such as a family history or personal history of cancer and high-grade serous carcinoma (HGSC) subtype. HGSC has been reported to be the most common subtype of EOC worldwide (approximately 70%). However, this subtype is less prevalent in Thai patients (reported as only 20%). The difference in the distribution of various subtypes of EOC may reflect the incidence of germline mutations in Thai EOC patients.

AIM

To evaluate the frequencies of germline mutations in EOC patients and to compare the frequencies in those with and without clinical risk factors for hereditary ovarian cancer.

METHODS

This cross-sectional study included 112 nonmucinous EOC patients who underwent primary surgery at our tertiary care hospital. Clinical risk factors for hereditary ovarian cancer were defined as follows: Age below 40 years, a significant family history of cancer, synchronous ovarian and endometrial cancer, and HGSC. Comprehensive germline mutations were detected by next-generation sequencing.

RESULTS

Of a total of 112 patients, 82 (73.2%) patients had ≥ 1 risk factor and 30 (26.8%) patients had no risk factors. Germline mutations were detected in 26 patients: 20 (17.8%) patients had BRCA1/2 mutations, but 6 (5.4%) patients had mutations in other genes, including 1 in MLH1, 1 in MSH2, 1 in RAD51C, 2 in ATM and 1 in CDH1. Germline mutations were only detected in patients with risk factors (26 of 82, 31.7%), not in patients without risk factors (P < 0.001). A significant family history of cancer and HGSC were the only two significant risk factors associated with a higher proportion of germline mutations (56.3% vs 10% for those with and without a history of cancer, respectively, 40.8% vs 9.5% for those with and without HGSC). Germline BRCA mutations were detected in 38.8% of patients with HGSC but in only 1.6% of those with non-HGSC. An age below 40 years, personal history of breast cancer, and synchronous ovarian and endometrial cancer were not significant factors (14.3% vs 23.5%, 33.3% vs 21%, 22.2% vs 22.3%).

CONCLUSION

Approximately one-third of EOC patients with risk factors had germline mutations. Almost all germline BRCA mutations were found in patients with the HGSC subtype. Selected patients with HGSC and a family history of cancer should be initially considered for genetic analysis in Thailand.

Sparse Graph Regularization Non-Negative Matrix Factorization Based on Huber Loss Model for Cancer Data Analysis

Non-negative matrix factorization (NMF) is a matrix decomposition method based on the square loss function. To exploit cancer information, cancer gene expression data often uses the NMF method to reduce dimensionality. Gene expression data usually have some noise and outliers, while the original NMF loss function is very sensitive to non-Gaussian noise. To improve the robustness and clustering performance of the algorithm, we propose a sparse graph regularization NMF based on Huber loss model for cancer data analysis (Huber-SGNMF). Huber loss is a function between L₁-norm and L₂-norm that can effectively handle non-Gaussian noise and outliers. Taking into account the sparsity matrix and data geometry information, sparse penalty and graph regularization terms are introduced into the model to enhance matrix sparsity and capture data manifold structure. Before the experiment, we first analyzed the robustness of Huber-SGNMF and other models. Experiments on The Cancer Genome Atlas (TCGA) data have shown that Huber-SGNMF performs better than other most advanced methods in sample clustering and differentially expressed gene selection.

High Resolution Based Quantitative Determination of Methylation Status of CDH1 and VIM Gene in Epithelial Ovarian Cancer

BACKGROUND

DNA promoter methylation is widely explored epigenetic phenomena, known to effect gene expression and further perturbation in cellular homeostasis. Myriad of studies have leveraged promoter methylation and its potential as biomarker for various types of cancer. Aim of present study is to investigate promoter methylation of CDH1 and VIM gene and etiology of epithelial ovarian cancer (EOC).

METHODS

Most of previous studies were qualitative; we have quantitatively assessed methylation levels in 50 EOC cases and control each through high recognition melt (HRM) technique.

RESULTS

At 10 % cutoff for CDH1 94% of EOC cases were found to be methylated with mean methylation of 45±13.8, whereas for control mean methylation was found to be 7.3±3.7 amongst 16 % methylation positive control samples. For VIM methylation was detected in 96% of cases with mean of 50.44±11.7 in EOC and in 12% methylation positive samples for control mean methylation was 6.24±4.3.

CONCLUSION

In short HRM based DNA methylation can serve as a robust and sensitive diagnostic method for promoter methylation detection and as a biomarker for early epithelial ovarian cancer detection.

Aberrant Methylation Status of Tumour Suppressor Genes in Ovarian Cancer Tissue and Paired Plasma Samples

Ovarian cancer is a highly heterogeneous disease and its formation is affected by many epidemiological factors. It has typical lack of early signs and symptoms, and almost 70% of ovarian cancers are diagnosed in advanced stages. Robust, early and non-invasive ovarian cancer diagnosis will certainly be beneficial. Herein we analysed the regulatory sequence methylation profiles of the RASSF1, PTEN, CDH1 and PAX1 tumour suppressor genes by pyrosequencing in healthy, benign and malignant ovarian tissues, and corresponding plasma samples. We recorded statistically significant higher methylation levels (p < 0.05) in the CDH1 and PAX1 genes in malignant tissues than in controls (39.06 ± 18.78 versus 24.22 ± 6.93; 13.55 ± 10.65 versus 5.73 ± 2.19). Higher values in the CDH1 gene were also found in plasma samples (22.25 ± 14.13 versus 46.42 ± 20.91). A similar methylation pattern with positive correlation between plasma and benign lesions was noted in the CDH1 gene (r = 0.886, p = 0.019) and malignant lesions in the PAX1 gene (r = 0.771, p < 0.001). The random forest algorithm combining methylation indices of all four genes and age determined 0.932 AUC (area under the receiver operating characteristic (ROC) curve) prediction power in the model classifying malignant lesions and controls. Our study results indicate the effects of methylation changes in ovarian cancer development and suggest that the CDH1 gene is a potential candidate for non-invasive diagnosis of ovarian cancer.

Gene promoter methylation and cancer: An umbrella review

Gene promoter methylation is a common epigenetic event, taking place in the early phase of tumorigenesis, which has a great potential as a diagnostic and prognostic cancer biomarker. In this umbrella review, we provide an overview on the association between gene-promoter methylation of protein-coding genes and cancer risk based on currently available meta-analyses data on gene promoter methylation. We searched MEDLINE via PubMed and the Cochrane Database of Systematic Reviews for meta-analyses that examine the association between gene-promoter methylation and cancer, published until January 2019 in English. We used AMSTAR to assess the quality of the included studies and applied a set of pre-specified criteria to evaluate the magnitude of each association. We provide a comprehensive overview of 80 unique combinations between 22 different genes and 18 cancer outcomes, all of which indicated a positive association between promoter hypermethylation and cancer. In total, the 70 meta-analyses produced significant results under a random-effects model with odds ratios that ranged from 1.94 to 26.60, with the summary effect being in favor of the unmethylated group in all cases. Three of the strong evidence associations involve RASSF1 methylation on bladder cancer risk (OR = 18.46; 95% CI: 12.69-26.85; I2 = 0%), MGMT methylation on NSCLC (OR = 4.25; 95% CI: 2.83-6.38; I2 = 22.4%) and RARB methylation on prostate cancer (OR = 6.87; 95% CI: 4.68-10.08; I2 = 0%). Meta-analyses showed a moderate quality, AMSTAR score ranging from 4 to 9 (Mdn = 8; IQR: 7.0 to 8.0). As primary studies and meta-analyses on the subject accumulate, more genetic loci may be found to be highly associated with specific cancer types and hence the biomarker sets will become wider.

Long non-coding RNA H19 is responsible for the progression of lung adenocarcinoma by mediating methylation-dependent repression of CDH1 promoter

Lung adenocarcinoma is a common histologic type of lung cancer with a high death rate globally. Increasing evidence shows that long non-coding RNA H19 (lncRNA H19) and CDH1 methylation are involved in multiple tumours. Here, we tried to investigate whether lncRNA H19 or CDH1 methylation could affect the development of lung adenocarcinoma. First, lung adenocarcinoma tissues were collected to detect CDH1 methylation. Then, the regulatory mechanisms of lncRNA H19 were detected mainly in concert with the treatment of overexpression of lncRNA H19, siRNA against lncRNA H19, overexpression of CDH1 and demethylating agent A-5az in lung adenocarcinoma A549 cell. The expression of lncRNA H19 and epithelial-mesenchymal transition (EMT)-related factors as well as cell proliferation, sphere-forming ability, apoptosis, migration and invasion were detected. Finally, we observed xenograft tumour in nude mice so as to ascertain tumorigenicity of lung adenocarcinoma cells. LncRNA H19 and methylation of CDH1 were highly expressed in lung adenocarcinoma tissues. A549 cells with silencing of lncRNA H19, overexpression of CDH1 or reduced CDH1 methylation by demethylating agent 5-Az had suppressed cell proliferation, sphere-forming ability, apoptosis, migration and invasion, in addition to inhibited EMT process. Silencing lncRNA H19 could reduce methylation level of CDH1. In vivo, A549 cells with silencing lncRNA H19, overexpression of CDH1 or reduced CDH1 methylation exhibited low tumorigenicity, reflected by the smaller tumour size and lighter tumour weight. Taken together, this study demonstrates that silencing of lncRNA H19 inhibits EMT and proliferation while promoting apoptosis of lung adenocarcinoma cells by inhibiting methylation of CDH1 promoter.

CDH1, DLEC1 and SFRP5 methylation panel as a prognostic marker for advanced epithelial ovarian cancer

AIM

To investigate the CDH1, DLEC1 and SFRP5 gene methylation panel for advanced epithelial ovarian carcinoma (EOC).

MATERIALS & METHODS

One hundred and seventy-seven advanced EOC specimens were evaluated by methylation-specific PCR. We also used The Cancer Genome Atlas dataset to evaluate the panel.

RESULTS

The presence of two or more methylated genes was significant in recurrence (hazard ratio [HR]: 1.91 [1.33-2.76]; p = 0.002) and death (HR: 1.96 [1.26-3.06]; p = 0.006) in our cohort. In The Cancer Genome Atlas dataset, the presence of two or three methylated genes was significant in death (HR: 1.59 [1.15-2.18]; p = 0.0047) and close to the significance level in recurrence (HR: 1.37 [0.99-1.88]; p = 0.058).

CONCLUSION

The CDH1, DLEC1 and SFRP5 methylation panel is a potential prognostic biomarker for advanced EOC.

Promoter methylation analysis of CDH1 and p14ARF genes in patients with urothelial bladder cancer

BACKGROUND/AIM

Urothelial bladder cancer arises from the accumulation of multiple epigenetic and genetic changes. We aimed to investigate the specificity and sensitivity of gene-specific promoter methylation of CDH1 and p14ARF genes in the early diagnosis of bladder cancer and compare those with other diagnostic tests in our population.

PATIENTS AND METHODS

In the current study, 65 patients with urothelial bladder cancer and 35 controls without any history of cancer were recruited. Methylation profiles of CDH1 and p14ARF genes from tumor and urine samples were determined by methylation-specific polymerase chain reaction method.

RESULTS

Methylation of CDH1 and p14ARF genes in tumor samples was 95.4% and 78.5%, respectively. The methylation frequencies were found to be 68.8% for CDH1 gene and 72.9% for p14ARF gene in urine samples. Sensitivities of CDH1, p14ARF and urine cytology were found to be 67.4%, 72.1% and 34.9%, respectively, while their specificities were 93.9%, 63.6% and 93.9%, respectively.

CONCLUSION

Aberrant promoter methylation of CDH1 and p14ARF genes can be used to detect urothelial bladder cancer. In low-grade tumors, when compared with urine cytology, combined methylation analysis of CDH1 and p14ARF genes may not increase the sensitivity to identify malignant cells in urine samples.

Relationship between RAS Association Domain Family Protein 1A Promoter Methylation and the Clinicopathological Characteristics in Patients with Ovarian Cancer: A Systematic Meta-Analysis

BACKGROUND

To investigate the relationship between RAS association domain family protein 1A (RASSF1A) promoter methylation and the clinical features, and the survival of ovarian cancer patients.

METHODS

A comprehensive literature search was conducted in the PubMed, Embase, EBSCO, and Cochrane Library databases. The overall ORs with their 95% CIs were calculated in this meta-analysis.

RESULTS

Finally 17 relevant publications with 1,108 ovarian cancer samples were available for the current meta-analysis. RASSF1A promoter methylation had a significantly higher level in ovarian cancer than in low malignant potential (LMP) tumors. No significant relationship was observed between RASSF1A promoter methylation and the clinicopathological characteristics in ovarian cancer. Two studies reported that RASSF1A promoter methylation was not correlated with the survival of patients with ovarian cancer.

CONCLUSIONS

Our findings suggest that the use of RASSF1A promoter methylation could distinguish ovarian cancer and LMP tumors. -RASSF1A promoter methylation may not be correlated with the clinical features and the survival of ovarian cancer patients. More studies with large sample sizes are essential in the future.

An updated meta-analysis of the prognostic value of decreased E-cadherin expression in ovarian cancer

Decreased epithelial cadherin (E-cadherin) expression is hypothesized to be related to poor prognosis of ovarian cancer, but the predictive value is still inconsistent. We conducted an updated meta-analysis with a total of 16 studies enrolling 1720 patients to estimate the prognostic value of decreased E-cadherin expression in ovarian cancer. Reduced expression of E-cadherin was significantly associated to poor overall survival (HR = 1.74, 95% CI: 1.40–2.17) and progression-free survival (HR = 1.45, 95% CI: 1.12–1.86) with a large heterogeneity for overall survival. In addition, we found that decreased expression of E-cadherin was significantly correlated with International Federation of Gynecology and Obstetrics grade (HR = 3.74, 95% CI: 2.24–6.23), E-cadherin membranous (HR = 1.47, 95% CI: 1.01–2.14), pathologic grade (HR = 1.41, 95% CI: 1.01–1.97), residual tumor size (HR = 2.72, 95% CI: 1.99–3.72), and surgery (HR = 3.21, 95% CI: 1.19–8.67). Our finding suggests that decreased E-cadherin expression may be a predictor of poor ovarian cancer prognosis.

An epigenetic signature of adhesion molecules predicts poor prognosis of ovarian cancer patients

DNA methylation is a promising biomarker for cancer. The epigenetic effects of cell adhesion molecules may affect the therapeutic outcome and the present study examined their effects on survival in ovarian cancer. We integrated methylomics and genomics datasets in The Cancer Genome Atlas (n = 391) and identified 106 highly methylated adhesion-related genes in ovarian cancer tissues. Univariate analysis revealed the methylation status of eight genes related to progression-free survival. In multivariate Cox regression analysis, four highly methylated genes (CD97, CTNNA1, DLC1, HAPLN2) and three genes (LAMA4, LPP, MFAP4) with low methylation were significantly associated with poor progression-free survival. Low methylation of VTN was an independent poor prognostic factor for overall survival after adjustment for age and stage. Patients who carried any two of CTNNA1, DLC1 or MFAP4 were significantly associated with poor progression-free survival (hazard ratio: 1.59; 95% confidence interval: 1.23, 2.05). This prognostic methylation signature was validated in a methylomics dataset generated in our lab (n = 37, hazard ratio: 16.64; 95% confidence interval: 2.68, 103.14) and in another from the Australian Ovarian Cancer Study (n = 91, hazard ratio: 2.43; 95% confidence interval: 1.11, 5.36). Epigenetics of cell adhesion molecules is related to ovarian cancer prognosis. A more comprehensive methylomics of cell adhesion molecules is needed and may advance personalized treatment with adhesion molecule-related drugs.

Functional significance of the long non-coding RNA RP11-169D4.1 as a metastasis suppressor in laryngeal squamous cell carcinoma by regulating CDH1

The present study investigated the expression profile and the function of RP11-169D4.1 and explored its potential mechanisms in laryngeal squamous cell carcinoma. The biological function of RP11-169D4.1 was examined using the MTT assay, flow cytometric analysis, wound healing and transwell assays. The relationship between RP11-169D4.1 and miR-205-5p was discovered by Argonaute 2 protein immunoprecipitation. The target gene of RP11-169D4.1 was CDH1 which was assessed by Pearson's correlation analysis, RT-PCR and western blot assay. We demonstrated that RP11-169D4.1 expression was markedly decreased in LSCC tissues and cell lines. The overexpression of RP11-169D4.1 inhibited the proliferation, migration and invasion of LSCC cell lines as well as promoted apoptosis. We further verified that miR-205-5p had binding sites with RP11‑169D4.1 and that RP11-169D4.1 could regulate the expression of CDH1. Ectopic transfection of RP11-169D4.1 led to a significant reduction in the downstream signaling molecule AKT in LSCC cells. The long non-coding RNA RP11-169D4.1 may serve as a tumor suppressor and a promising therapeutic target in laryngeal cancer, which could inhibit the process of EMT by regulating CDH1.

Association between P16INK4a Promoter Methylation and Ovarian Cancer: A Meta-Analysis of 12 Published Studies

Background

Ovarian cancer is the primary cause of death in women diagnosed with gynecological malignancies worldwide. Absence of early symptoms prevents prompt diagnosis or successful therapeutic intervention. P16^INK4a is a well-known tumor suppressor gene (TSG). Aberrant methylation of TSG promoter is an important epigenetic silencing mechanism leading to ovarian cancer progression. Studies have reported differences in methylation frequencies of the p16^INK4a promoter between ovarian cancer and the corresponding control group. However, the association between p16^INK4a promoter methylation and ovarian cancer remains unclear and controversial. Therefore, a meta-analysis was conducted to clarify the relationship between p16^INK4a promoter methylation and ovarian cancer.

Methods

PubMed, Web of Science, EMBASE and CNKI were searched to identify eligible studies for the evaluation of the association between p16^INK4a promoter methylation and ovarian cancer. Odds ratio (ORs) and 95% confidence intervals (95%CI) were calculated to determine the strength of association between p16^INK4a promoter methylation and ovarian cancer.

Results

A total of 612 ovarian cancer patients and 289 controls from 12 eligible studies were included in the meta-analysis. Overall, a significant association was observed between p16^INK4a methylation status and ovarian cancer risk using a fixed-effects model (OR = 2.02, 95% CI = 1.39–2.94).

Conclusion

The results of our meta-analysis show that aberrant methylation of p16^INK4a promoter was significantly associated with ovarian cancer. It may represent a promising molecular marker to monitor the disease and provides new insights into the treatment of human ovarian cancer.