In recent years, the biological role of changes in physical factors in carcinogenesis and progression has attracted increasing attention. Matrix stiffness, also known as ECM stress, is a critical physical factor of tumor microenvironment and remains alternating during carcinogenesis as a result of ECM remodeling through activation of cancer-associated fibroblasts and extracellular collagen accumulation, crosslinking and fibrosis. Different content and density of extracellular collagen in ECM endows matrix with varying stiffness. Physical signals induced by matrix stiffness are transmitted to tumor cells primarily by the integrins receptor family and trigger a series of mechanotransduction that result in changes in tumor cell morphology, proliferative capacity, and invasive ability. Importantly, accumulating evidence revealed that changes in matrix stiffness in tumor tissues greatly control the sensitivity of tumor cells in response to chemotherapy, radiotherapy, and immunotherapy through integrin signaling, YAP signaling, and related signaling pathways. Here, the present review analyzes the current research advances on matrix stiffness and tumor cell behavior with a view to contributing to tumor cell growth and treatment, with the hope of improving the understanding of the biological role of matrix stiffness in tumors.

Cervical cancer is the second most common gynecological malignant tumor endangering the health of women worldwide. Despite advances in the therapeutic strategies available to treat cervical cancer, the long-term prognosis of patients with recurrent and metastatic cervical cancer remains unsatisfactory. In recent years, immune checkpoint inhibitors (ICIs) have shown encouraging efficacy in the treatment of cervical cancer. ICIs have been approved for use in both first- and second-line cervical cancer therapies. This review summarizes the current knowledge of ICIs and the application of ICIs in clinical trials for the treatment of cervical cancer.

Reprogramming of fatty acid metabolism promotes cell growth and metastasis through a variety of processes that stimulate signaling molecules, energy storage, and membrane biosynthesis in endometrial cancer. Oleic acid is one of the most important monounsaturated fatty acids in the human body, which appears to have both pro- and anti-tumorigenic activities in various pre-clinical models. In this study, we evaluated the potential anti-tumor effects of oleic acid in endometrial cancer cells and the LKB1^fl/flp53^fl/fl mouse model of endometrial cancer. Oleic acid increased lipogenesis, inhibited cell proliferation, caused cell cycle G1 arrest, induced cellular stress and apoptosis, and suppressed invasion in endometrial cancer cells. Targeting of diacylglycerol acyltransferases 1 and 2 effectively increased the cytotoxicity of oleic acid. Moreover, oleic acid significantly increased the expression of wild-type PTEN, and knockdown of PTEN by shRNA partially reversed the anti-proliferative and anti-invasive effects of oleic acid. Inhibition of the AKT/mTOR pathway by ipatasertib effectively increased the anti-tumor activity of oleic acid in endometrial cancer cells. Oleic acid treatment (10 mg/kg, daily, oral) for four weeks significantly inhibited tumor growth by 52.1% in the LKB1^fl/flp53^fl/fl mice. Our findings demonstrated that oleic acid exhibited anti-tumorigenic activities, dependent on the PTEN/AKT/mTOR signaling pathway, in endometrial cancer.

Purpose: This study aimed to investigate the association of metabolic factors with endometrial atypical hyperplasia and endometrial cancer, and to develop a nomogram model to predict the risk of developing endometrial cancer. Patients and Methods: We collected data of patients with endometrial atypical hyperplasia and endometrial cancer as the case group and then selected patients with simple hyperplasia or polypoid hyperplasia of the endometrium during the same period as the control group using the age-matched method. Laboratory results of metabolic factors were retrieved from the clinical data of the two groups of patients. Multivariable logistic regression analysis was used to determine the risk factors associated with endometrial malignant hyperplasia and to develop a nomogram prediction model of risk factors associated with endometrial malignant hyperplasia. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using the C-index, calibration plot, and decision curve analysis. Results: Predictors included in the column line graph model were age, hypertension, diabetes, BMI, uric acid, and hyperlipidemia. We calculated the C-index of the model and performed bootstrap validation. Our nomogram model had good discriminatory power and was well calibrated. Decision curve analysis was also used to guide the practical application of this column line graph model. Conclusion: The development of endometrial malignant hyperplasia is significantly associated with metabolic factors: BMI > 25, hyperuricemia, and hyperlipidemia are the main risk factors. Hypertension, hyperglycemia and elevated CA199 were also associated with the development of endometrial malignant hyperplasia. The nomogram prediction model based on physical examination and laboratory testing developed in this study can be used as a rapid method for predicting the risk of endometrial malignancy development and screening for risk factors in a population of women with metabolism-related high-risk factors.

Introduction: Endometriosis (EM) is an aggressive, pleomorphic, and common gynecological disease. Its clinical presentation includes abnormal menstruation, dysmenorrhea, and infertility, which seriously affect the patient's quality of life. However, the pathogenesis underlying EM and associated regulatory genes are unknown.Methods: Telomere-related genes (TRGs) were uploaded from TelNet. RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to identify the pathways involved in the action of the signature genes. Finally, the CAMP database was used to screen drugs for potential use in EM treatment.Results: Fifteen total genes were screened as EM–telomere differentially expressed genes. Further screening by machine learning obtained six genes as characteristic predictive of EM. Immuno-infiltration analysis of the telomeric genes showed that expressions including macrophages and natural killer cells were significantly higher in cluster A. Further enrichment analysis showed that the differential genes were mainly enriched in biological pathways like cell cycle and extracellular matrix. Finally, the Connective Map database was used to screen 11 potential drugs for EM treatment.Discussion: TRGs play a crucial role in EM development, and are associated with immune infiltration and act on multiple pathways, including the cell cycle. Telomere signature genes can be valuable predictive markers for EM.