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.

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Background: We describe a DNA methylation assay, named MPap test, using cervical scraping as an alternative technique for endometrial cancer detection. Methods: A multicenter hospital-based, two-stage validation study was conducted to validate the cancer detection performance of the MPap test. The MPap value was determined from the DNA methylation status of two genes (BHLHE22, CDO1) and combined with two other clinical variables (age, BMI). The cutoff threshold of the MPap value was established in stage 1 and validated in stage 2. A total of 592 women with abnormal uterine bleeding were enrolled from five medical centers throughout Taiwan. Results: In stage 1, the sensitivity, specificity, and positive and negative predictive values of the MPap test for detecting endometrial cancer were 92.9%, 71.5%, 39.8%, and 98.0%, respectively. These values were validated in stage 2, being 92.5%, 73.8%, 40.2%, and 98.1%. Moreover, MPap outperformed transvaginal ultrasound in sensitivity and negative predictive values for detecting endometrial cancer. When we applied the algorithm for triage of endometrial cancer detection by MPap in the Taiwan National Health Insurance dataset, we found that it may reduce invasive procedures by 69~73%. Conclusions: MPap may provide a feasible alternative for endometrial cancer detection and can be considered as a triage test to reduce unnecessary invasive procedures.

Window of implantation (WOI) genes have been comprehensively identified at the single cell level. DNA methylation changes in cervical secretions are associated with in vitro fertilization embryo transfer (IVF-ET) outcomes. Using a machine learning (ML) approach, we aimed to determine which methylation changes in WOI genes from cervical secretions best predict ongoing pregnancy during embryo transfer. A total of 2708 promoter probes were extracted from mid-secretory phase cervical secretion methylomic profiles for 158 WOI genes, and 152 differentially methylated probes (DMPs) were selected. Fifteen DMPs in 14 genes (BMP2, CTSA, DEFB1, GRN, MTF1, SERPINE1, SERPINE2, SFRP1, STAT3, TAGLN2, TCF4, THBS1, ZBTB20, ZNF292) were identified as the most relevant to ongoing pregnancy status. These 15 DMPs yielded accuracy rates of 83.53%, 85.26%, 85.78%, and 76.44%, and areas under the receiver operating characteristic curves (AUCs) of 0.90, 0.91, 0.89, and 0.86 for prediction by random forest (RF), naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (KNN), respectively. SERPINE1, SERPINE2, and TAGLN2 maintained their methylation difference trends in an independent set of cervical secretion samples, resulting in accuracy rates of 71.46%, 80.06%, 80.72%, and 80.68%, and AUCs of 0.79, 0.84, 0.83, and 0.82 for prediction by RF, NB, SVM, and KNN, respectively. Our findings demonstrate that methylation changes in WOI genes detected noninvasively from cervical secretions are potential markers for predicting IVF-ET outcomes. Further studies of cervical secretion of DNA methylation markers may provide a novel approach for precision embryo transfer.

Intraperitoneal metastasis is a challenging clinical scenario in epithelial ovarian cancer (EOC). As they are distinct from hematogenous metastasizing tumors, epithelial ovarian cancer cells primarily disseminate within the peritoneal cavity to form superficially invasive carcinomas. Unfavorable pharmacokinetics for peritoneal tumors and gut toxicity collectively lead to a narrow therapeutic window and therefore limit the opportunities for a favorable clinical outcome. New insights into tumor metastasis in the peritoneal microenvironment are keenly awaited to develop new therapeutic strategies. Epithelial ovarian cancer stem cell (OCSC) seeding is considered to be a critical component of the peritoneal spread. Using a unique and stepwise process of the OCSC differentiation model may provide insight into the intraperitoneal metastasis. The transcriptome and epigenome of OCSC differentiation were characterized by expression array and MethylCap-Seq. The TCGA, AOCS, and KM-Plotter databases were used to evaluate the association between survival outcomes and the methylation/expression levels of candidate genes in the EOC datasets. The STRING database was used to investigate the protein–protein interaction (PPI) for candidates and their associated genes. The infiltration level of immune cells in EOC patients and the association between clinical outcome and OCSCs differentiation genes were estimated using the TIDE and TIME2.0 algorithms. We established an EOC differentiation model using OCSCs. After an integrated transcriptomics and methylomics analysis of OCSCs differentiation, we revealed that the genes associated with earlier OCSC differentiation were better able to reflect the patient’s outcome. The OCSC differentiation genes were involved in regulating metabolism shift and the suppressive immune microenvironment. High GPD1 expression with high pro-tumorigenic immune cells (M2 macrophage, and cancer associated fibroblast) had worst survival. Moreover, we developed a methylation signature, constituted by GNPDA1, GPD1, GRASP, HOXC11, and MSLN, that may be useful for prognostic prediction in EOC. Our results revealed a novel role of epigenetic plasticity OCSC differentiation and suggested metabolic and immune intervention as a new therapeutic strategy.