Stougaardudsen5495
Endometriosis is one of the most frequent gynecological diseases in reproductive age women, but its etiology is not completely understood. Endometriosis is characterized by progesterone resistance, which has been explained in part by a decrease in the expression of the intracellular progesterone receptor in the ectopic endometrium. Progesterone action is also mediated by nongenomic mechanisms via membrane progesterone receptors (mPRs) that belong to the class II members of the progesterone and adipoQ receptor (PAQR) family. The aim of the present study was to evaluate the expression at mRNA and protein levels of mPR members in the eutopic and ectopic endometrium of women with endometriosis. Total RNA and total protein were isolated from control endometrium (17 samples), eutopic endometrium (17 samples), and ectopic endometrium (9 samples). The expression of PAQR7 (mPRα), PAQR8 (mPRβ), and PAQR6 (mPRδ) at mRNA and protein levels was evaluated by RT-qPCR and Western blot, whereas PAQR5 (mPRγ) gene expression was evaluated by RT-qPCR. Statistical analysis between comparable groups was performed using one-way ANOVA followed by Tukey's multiple comparisons test with a confidence interval of 95 %. The analysis of gene expression showed that PAQR7 and PAQR5 expression was lower in both eutopic and ectopic endometrium as compared to the endometrium of women without endometriosis, whereas the expression of PAQR8 and PAQR6 was only reduced in eutopic endometrium. Furthermore, mPRα and mPRβ protein content was decreased in the ectopic endometrium of women with endometriosis. Our results demonstrate a decrease in the expression and protein content of mPRs in eutopic and ectopic endometrium of patients with endometriosis, which could contribute to the progesterone resistance observed in patients with this disease.
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. Recent researches have demonstrated that m6A methylation regulators play a key role in various cancers, such as gastric cancer and colon adenocarcinoma. Several m6A methylation regulators are reported to predict the prognosis of HCC. LDC7559 in vitro Therefore, there is a need to further identify the predictive value of m6A methylation regulators in HCC.
We utilized The Cancer Genome Atlas (TCGA) database to obtain the gene expression profile of m6A RNA methylation regulators and clinical information for patients with HCC. Besides, we identified two clusters of HCC with various clinical factors by consensus clustering analysis. Then the least absolute shrinkage and selection operator (LASSO) and the Cox regression analysis were applied to construct a prognostic signature.
Except for ZC3H13 and METTL14, a majority of the thirteen m6A RNA methylation regulators were significantly overexpressed in HCC specimens. HCC patients were classified into two groups (cluster 1 and cluster 2). The cluster 1 was with a significantly worse prognosis than cluster 2, and most of the 13 known m6A RNA methylation regulators were upregulated in cluster 1. Besides, we developed a prognostic signature consisting of YTHDF2, YTHDF1, METTL3, KIAA1429, and ZC3H13, which could successfully differentiate high-risk patients. More importantly, univariate and multivariate Cox regression analysis indicated that the signature-based risk score was an independent prognostic factor for patients with HCC.
Our study showed these five m6A RNA methylation regulators can be used as practical and reliable prognostic tools of HCC, which might have potential value for therapeutic strategies.
Our study showed these five m6A RNA methylation regulators can be used as practical and reliable prognostic tools of HCC, which might have potential value for therapeutic strategies.[This corrects the article DOI 10.1155/2019/4370258.].Missing observations are always a challenging problem that we have to deal with in diseases that require follow-up. In hospital records for vesicoureteral reflux (VUR) and recurrent urinary tract infection (rUTI), the number of complete cases is very low on demographic and clinical characteristics, laboratory findings, and imaging data. On the other hand, deep learning (DL) approaches can be used for highly missing observation scenarios with its own missing ratio algorithm. In this study, the effects of multiple imputation techniques MICE and FAMD on the performance of DL in the differential diagnosis were compared. The data of a retrospective cross-sectional study including 611 pediatric patients were evaluated (425 with VUR, 186 with rUTI, 26.65% missing ratio) in this research. CNTK and R 3.6.3 have been used for evaluating different models for 34 features (physical, laboratory, and imaging findings). In the differential diagnosis of VUR and rUTI, the best performance was obtained by deep learning with MICE algorithm with its values, respectively, 64.05% accuracy, 64.59% sensitivity, and 62.62% specificity. FAMD algorithm performed with accuracy = 61.52, sensitivity = 60.20, and specificity was found out to be 61.00 with 3 principal components on missing imputation phase. DL-based approaches can evaluate datasets without doing preomit/impute missing values from datasets. Once DL method is used together with appropriate missing imputation techniques, it shows higher predictive performance.
This study is designed to clarify that miR-1258 targets E2F1 to regulate the proliferation and cell cycle of breast cancer (BC) cells and consequently suppress the progression of BC.
Bioinformatics analysis was used to analyze the differentially expressed genes in BC. The expression of miR-1258 and E2F1 mRNA in BC cell lines and immortalized breast epithelial cell lines were detected by qRT-PCR. The proliferation and growth activity of BC cells were detected by MTT and colony formation assays. The apoptosis and cell cycle of BC cells were detected by flow cytometry and the targeting relationship between miR-1258 and E2F1 was identified by dual-luciferase assay.
The expression of miR-1258 was decreased while that of E2F1 was increased in BC cells. Overexpression of miR-1258 and silencing E2F1 could inhibit the cell proliferation and growth, block cells in the G0/G1 phase, and promote cell apoptosis. Besides, miR-1258 inhibited cell proliferation and growth, block cells in the G0/G1 phase, and promote cell apoptosis by downregulating E2F1.
