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PGC-1? and ERR? in patients with endometrial cancer: a translational study for predicting myometrial invasion.

ABSTRACT: BACKGROUND:PGC-1? and ERR? are closely related to tumor formation and progression. However, the mechanism underlying the involvement of PGC-1?/ERR? in regulating invasion and migration in endometrial cancer remains to be explored. RESULTS:Elevated levels of PGC-1? and ERR? were associated with advanced myometrial invasion, and PGC-1? and Vimentin expression was related to the depth of myometrial invasion in premenopausal endometrial cancer. Silencing of PGC-1? reduced ERR? activation and inhibited epithelial-mesenchymal-transition phenotypes, resulting in significant inhibition of invasion and migration. Overexpression of ERR? led to enhanced PGC-1? expression and increased activity of TFEB, promoting epithelial-mesenchymal-transition in endometrial cancer cells. CONCLUSIONS:PGC-1? and ERR? induce the epithelial-mesenchymal-transition therefore invasion and migration in endometrial cancer, and may be novel biomarkers to predict the risk of advanced myometrial invasion. METHODS:PGC-1?, ERR?, and vimentin expression was analyzed in tissue microarrays using immunohistochemistry. PGC-1? and ERR? expression in endometrial cancer cell lines was investigated using quantitative PCR and western blotting analyses after infection with lentivirus-mediated small interfering RNA (siRNA) targeting PGC-1? (siRNA-PGC-1?) or overexpressing ERR?. E-cadherin and vimentin levels were determined using western blotting and cell immunouorescence analyses. Cell migration and invasiveness were evaluated using scratch and trans-well chamber assays.

SUBMITTER: Chen L

PROVIDER: S-EPMC7521515 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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PGC-1α and ERRα in patients with endometrial cancer: a translational study for predicting myometrial invasion.

Chen LiLi L Mao XiaoDan X Huang MeiMei M Lei HuiFang H Xue LiFang L Sun PengMing P

Aging 20200913 17

<h4>Background</h4>PGC-1α and ERRα are closely related to tumor formation and progression. However, the mechanism underlying the involvement of PGC-1α/ERRα in regulating invasion and migration in endometrial cancer remains to be explored.<h4>Results</h4>Elevated levels of PGC-1α and ERRα were associated with advanced myometrial invasion, and PGC-1α and Vimentin expression was related to the depth of myometrial invasion in premenopausal endometrial cancer. Silencing of PGC-1α reduced ERRα activat ...[more]

PMID: 32920551

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Project description:The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01-1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11-3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61-6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23-2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53-2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS-based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.

Dataset Information

PGC-1? and ERR? in patients with endometrial cancer: a translational study for predicting myometrial invasion.

Publications

PGC-1α and ERRα in patients with endometrial cancer: a translational study for predicting myometrial invasion.

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