Ovarian high‐grade serous carcinoma (HGSC) is the most lethal gynecological malignancy. Prevailing evidences suggest that drug resistance and recurrence of ovarian HGSC are caused by the presence of ...cancer stem cells. Therefore, targeting cancer stems is appealing, however, all attempts to date, have failed. To circumvent this limit, we analyzed differential transcriptomes at early differentiation of ovarian HGSC stem cells and identified the developmental transcription factor GATA3 as highly expressed in stem, compared to progenitor cells. GATA3 expression associates with poor prognosis of ovarian HGSC patients, and was found to recruit the histone H3, lysine 27 (H3K27) demethylase, UTX, activate stemness markers, and promote stem‐like phenotypes in ovarian HGSC cell lines. Targeting UTX by its inhibitor, GSKJ4, impeded GATA3‐driven stemness phenotypes, and enhanced apoptosis of GATA3‐expressing cancer cells. Combinations of gemcitabine or paclitaxel with GSKJ4, resulted in a synergistic cytotoxic effect. Our findings provide evidence for a new role for GATA3 in ovarian HGSC stemness, and demonstrate that GATA3 may serve as a biomarker for precision epigenetic therapy in the future.
What's new?
Cancer stem cells (CSCs) routinely evade conventional cancer therapies and fuel tumor regrowth. However, while CSC targeting is an appealing therapeutic strategy, studies are needed to better understand CSC differentiation. Here, in multipotent CSCs from ovarian high‐grade serous carcinomas (HGSCs), complexes consisting of the stemness regulator GATA3 and the histone demethylase UTX were found to maintain cancer stemness via epigenetic activation of c‐MYC, CD44, and NANOG. GATA3 was further identified as an independent risk factor in early‐stage ovarian HGSC. The results suggest that GATA3 is a prognostic marker in ovarian tumorigenesis and that targeting GATA3/UTX is a promising therapeutic approach.
Pyruvate kinase M2 (PKM2) regulates glycolysis and oxidative phosphorylation; however, the role of PKM2 in ovarian cancer remains largely unknown. We investigated whether ovarian cancer metabolism ...could provide insight into the development of therapeutic strategies. We performed immunohistochemical staining for PKM2 on a tissue microarray for multivariate analysis. It revealed that patients exhibiting higher PKM2 expression were significantly associated with malignancy groups (p < 0.001) and pathogenesis models (p < 0.001), had poor progression-free survival rates (p = 0.01) as compared with patients exhibiting lower PKM2 levels, and yielded a hazard ratio of death of 2.02 (95% confidence interval: 0.70-5.85). In cell lines, PKM2 inhibitor significantly inhibited the glycolytic rate according to cellular glucose consumption (p < 0.001). We also utilized Seahorse assays to assess metabolism-related cell-specific factors and the impact of PKM2 inhibitors. Energy shifts as per Seahorse analysis showed attenuation of the extracellular acidification rate (p < 0.05) and no significant difference in oxygen-consumption rate in SKOV3 cells. Treatment with PKM2 inhibitor suppressed ovarian cancer growth and cell migration in vitro and inhibited tumor growth without significant toxicity in a xenograft study. PKM2 inhibition disturbed Warburg effects and inhibited ovarian cancer cell growth. Targeting PKM2 may constitute a promising therapy for patients with ovarian cancer, and clinical trials involving shikonin are warranted.
Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system ...that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majority of Americans. The US has the highest health expenditure per capita of all western and developed countries; however, most Americans don’t tap into the benefits of preventive healthcare. It is estimated that only 8% of Americans undergo routine preventive screenings. On a national level, very few states (15 out of the 50) have above-average preventive healthcare metrics. In literature, many studies focus on the cure of diseases (research areas such as drug discovery and disease prediction); whilst a minority have examined data-driven preventive measures—a matter that Americans and policy makers ought to place at the forefront of national issues. In this work, we present solutions for preventive practices and policies through Machine Learning (ML) methods. ML is morally neutral, it depends on the data that train the models; in this work, we make the case that Big Data is an imperative paradigm for healthcare. We examine disparities in clinical data for US patients by developing correlation and imputation methods for data completeness. Non-conventional patterns are identified. The data lifecycle followed is methodical and deliberate; 1000+ clinical, demographical, and laboratory variables are collected from the Centers for Disease Control and Prevention (CDC). Multiple statistical models are deployed (Pearson correlations, Cramer’s V, MICE, and ANOVA). Other unsupervised ML models are also examined (K-modes and K-prototypes for clustering). Through the results presented in the paper, pointers to preventive chronic disease tests are presented, and the models are tested and evaluated.
Using DNA methylation biomarkers in cancer detection is a potential direction in clinical testing. Some methylated genes have been proposed for cervical cancer detection; however, more reliable ...methylation markers are needed. To identify new hypermethylated genes in the discovery phase, we compared the methylome between a pool of DNA from normal cervical epithelium (n = 19) and a pool of DNA from cervical cancer tissues (n = 38) using a methylation bead array. We integrated the differentially methylated genes with public gene expression databases, which resulted in 91 candidate genes. Based on gene expression after demethylation treatment in cell lines, we confirmed 61 genes for further validation. In the validation phase, quantitative MSP and bisulfite pyrosequencing were used to examine their methylation level in an independent set of clinical samples. Fourteen genes, including ADRA1D, AJAP1, COL6A2, EDN3, EPO, HS3ST2, MAGI2, POU4F3, PTGDR, SOX8, SOX17, ST6GAL2, SYT9, and ZNF614, were significantly hypermethylated in CIN3+ lesions. The sensitivity, specificity, and accuracy of POU4F3 for detecting CIN3+ lesions were 0.88, 0.82, and 0.85, respectively. A bioinformatics function analysis revealed that AJAP1, EDN3, EPO, MAGI2, and SOX17 were potentially implicated in β‐catenin signaling, suggesting the epigenetic dysregulation of this signaling pathway during cervical cancer development. The concurrent methylation of multiple genes in cancers and in subsets of precancerous lesions suggests the presence of a driver of methylation phenotype in cervical carcinogenesis. Further validation of these new genes as biomarkers for cervical cancer screening in a larger population‐based study is warranted.
What's New?
The identification of novel genes that are hypermethylated in cancer and precancerous lesions is needed in order to achieve a better sensitivity and specificity in cervical cancer screening. Using a genome‐wide approach, here the authors identified 14 genes that were frequently hypermethylated in CIN3+ and might thus become useful biomarkers in future molecular cervical cancer screening. A bioinformatics function analysis revealed that five of these genes were potentially implicated in β‐catenin signaling, suggesting the epigenetic dysregulation of Wnt signaling during cervical cancer development. The concurrent hypermethylation of multiple genes also suggests the involvement of a CpG island methylator phenotype.
DNA methylation alteration, such as global hypomethylation and localized hypermethylation, within the promoters of tumor suppressor genes, is an important risk factor in cervical cancer. The ...potential use of DNA methylation detection, in cervical cancer screening or triage of mildly abnormal cytology, has recently been demonstrated. In particular, PAX1 DNA methylation testing was approved as an adjunct to cytology, in Taiwan, and is now undergoing registration trials in China. However, the function of PAX1 in cancer biology remains largely unknown. Here, we show that PAX1 inhibits malignant phenotypes upon oncogenic stress. Specifically, PAX1 expression inhibited the phosphorylation of multiple kinases, after challenges with oncogenic growth factors such as EGF and IL-6. Analogously, PAX1 activated a panel of phosphatases, including DUSP1, 5, and 6, and inhibited EGF/MAPK signaling. PAX1 also interacted with SET1B, increasing histone H3K4 methylation and DNA demethylation of numerous phosphatase-encoding genes. Furthermore, hypermethylated PAX1 associated with poor prognosis in cervical cancer. Taken together, this study reveals, for the first time, the functional relevance of PAX1 in cancer biology, and further supports the prospect of targeting multifold oncogenic kinase cascades, which jointly contribute to multiresistance, via epigenetic reactivation of PAX1.
Precision medicine requires markers for therapeutic guidance. The purpose of this study was to determine whether epithelial ovarian cancer (EOC) epigenetics can lead to the identification of ...biomarkers for precision medicine. Through integrative methylomics, we discovered and validated the epigenetic signature of NEFH and HS3ST2 as an independent prognostic factor for type II EOC in our dataset (n = 84), and two independent methylomics datasets (total n = 467). Integrated transcriptomics dataset (n = 1147) and tissue microarrays (n = 54) of HS3ST2 also related to high‐methylation statuses and the EOC prognosis. Mechanistic explorations of HS3ST2 have assessed responses to oncogenic stimulations such as IL‐6, EGF, and FGF2 in cancer cells. The combination of HS3ST2 and various oncogenic ligands also confers the worse outcome. 3‐O‐sulfation of heparan sulfate by HS3ST2 makes ovarian cancer cells intrinsically sensitive to oncogenic signals, which sheds new light on the application of HS3ST2 as a companion diagnostic for targeted therapy using kinase inhibitors or therapeutic antibodies.
What's new?
Most targeted therapies for epithelial ovarian cancer are ineffective. For precision medicine to be effective, companion diagnostic tests are needed. Through integrative epigenomics, here the authors discovered and validated the epigenetic signature of NEFH/HS3ST2 as a stratifying prognostic factor. Mechanistic investigations of HS3ST2 revealed inhibitory effects on various oncogenic signals and malignant phenotypes. Low HS3ST2 and overexpression of oncogenic ligands synergized to confer a poor outcome. Furthermore, 3‐O‐sulfation of heparan sulfate by HS3ST2 made ovarian cancer cells intrinsically sensitive to oncogenic signals, suggesting the application of HS3ST2 as a companion diagnostic for targeted therapy using kinase inhibitors or therapeutic antibodies.
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.
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