High-intermediate risk endometrial cancer: moving toward a molecularly based risk assessment profile

Linoleic acid exhibits anti-proliferative and anti-invasive activities in endometrial cancer cells and a transgenic model of endometrial cancer

Emerging evidence has provided considerable insights into the integral function of reprogramming fatty acid metabolism in the carcinogenesis and progression of endometrial cancer. Linoleic acid, an essential fatty acid with the highest consumption in the Western diet regimen, has shown pro-tumorigenic or anti-tumorigenic effects on tumor cell growth and invasion in multiple types of cancer. However, the biological role of linoleic acid in endometrial cancer remains unclear. In the present study, we aimed to investigate the functional impact of linoleic acid on cell proliferation, invasion, and tumor growth in endometrial cancer cells and in a transgenic mouse model of endometrial cancer. The results showed that Linoleic acid significantly inhibited the proliferation of endometrial cancer cells in a dose-dependent manner. The treatment of HEC-1A and KLE cells with linoleic acid effectively increased intracellular reactive oxygen species (ROS) production, decreased mitochondrial membrane potential, caused cell cycle G1 arrest, and induced intrinsic and extrinsic apoptosis pathways. The anti-invasive ability of linoleic acid was found to be associated with the epithelial-mesenchymal transition process in both cell lines, including the decreased expression of N-cadherin, snail, and vimentin. Furthermore, treatment of Lkb1fl/flp53fl/fl transgenic mice with linoleic acid for four weeks significantly reduced the growth of endometrial tumors and decreased the expression of VEGF, vimentin, Ki67, and cyclin D1 in tumor tissues. Our findings demonstrate that linoleic acid exhibits anti-proliferative and anti-invasive activities in endometrial cancer cell lines and the Lkb1fl/flp53fl/fl mouse model of endometrial cancer, thus providing a pre-clinical basis for future dietary interventions with linoleic acid in endometrial cancer.

Integration of pretreatment tumor markers in a nomogram model for prognostic prediction of FIGO stage I endometrial cancer: A multi-institutional cohort study

OBJECTIVE

Traditionally, the prognosis of patients with FIGO stage I endometrial cancer is determined by clinicopathological risk factors. In this study, we assessed the potential contribution of pretreatment carcinoembryonic antigen (CEA) and carbohydrate antigen-125 (CA-125) levels to estimating the prognosis of these patients and aimed to develop and validate a prognostic nomogram.

METHODS

This retrospective study included patients with FIGO stage I endometrial cancer who underwent treatment between January 2009 and December 2021 in the four institutes of Chang Gung Memorial Hospital. To identify optimal cutoff values of CEA and CA-125 for predicting survival, receiver operating characteristic (ROC) curves were generated, the Kaplan-Meier method was used to estimate survival, and a Cox regression model was used to analyze the independent prognostic factors. Finally, a nomogram and calibration curve were constructed to predict patient survival probability.

RESULTS

Of the 1559 patients evaluated, the optimal cutoff values of CEA and CA-125 were 1.44 ng/mL (area under the ROC curve [AUC] 0.601) and 39.77 U/mL (AUC 0.503), respectively. Multivariate Cox regression analysis showed that pretreatment CEA (hazard ratio [HR] 2.11, 95% confidence interval [95% CI] 1.35-3.28), CA-125 (HR 2.07, 95% CI 1.31-3.27), age >70 years (HR 12.54, 95% CI 5.05-31.11), myometrial invasion >50% (HR 1.69, 95% CI 1.03-2.73), non-endometrioid histology (HR 1.83, 95% CI 1.14-2.95), high-grade tumor (HR 2.41, 95% CI 1.46-3.97), and lymphovascular space invasion (HR 2.32, 95% CI 1.26-4.25) were significant variables associated with overall survival. These factors were used to construct the nomogram model, which showed good concordance and accuracy.

CONCLUSIONS

Integration of pretreatment CEA and CA-125 in a prognostic nomogram is feasible. Our prediction model has the potential to assist clinicians in guiding appropriate clinical practice.