Journal of Ovarian Research最新文献

Background: Epithelial ovarian cancer (EOC) is an alarming malignancy with frequent relapse and resistance to chemotherapy. Understanding the mechanism related to these phenotypes is urgent. Here, we investigated the roles of inflammasomes, NLRP1 and NLRP3, and pyroptosis in EOC progression and resistance to treatment.

Methods: Cell viability under cisplatin (CDDP) treatment was measured with diphenyltetrazolium bromide (MTT), and the IC50 values were calculated for the A2780, ACRP, and OVCAR3 cell lines. The levels of cytokines (interleukin-1β, IL-6, and TNFα) present in the supernatant were measured via ELISA. Caspase-1 activation was detected through the Caspase-Glo® 1 Assay. The cell death profile was determined via flow cytometry using Annexin V/PI staining, and the formation of pores in the cell membrane was measured using PI. A wound healing assay was used to investigate the effects of treatment with CDDP combined with a caspase-1 inhibitor (Ac-YVAD-CHO) on cell migration.

Results: IC50 values indicate increasing CDDP resistance across the following cell lines: A2780 (10.41 μM), ACRP (35.92 μM), and OVCAR3 (43.52 μM). Cytokine secretion and Caspase-1 activation were greater in treated cells than in untreated cells. CDDP-treated cells exhibited increased lytic cell death and pore formation. Caspase-1 inhibition during treatment with CDDP reduced wound closure, indicating reduced cell migration.

Conclusion: Our findings suggest that inflammasome activation and pyroptosis are mechanisms associated with ovarian cancer chemoresistance to CDDP, contributing to the devastating scenario of this disease. Targeting the NLRP1 and NLRP3 pathways could represent a promising strategy to improve OC treatment.

Ovarian cancer remains one of the most lethal gynecologic malignancies, often marked by late-stage diagnosis and resistance to conventional therapies. Poly (ADP-ribose) polymerase (PARP) inhibitors have significantly advanced treatment, particularly in tumors with homologous recombination deficiencies, such as BRCA1/2 mutations. However, their clinical benefit is limited in homologous recombination-proficient or BRCA wild-type tumors, necessitating the development of combination strategies to broaden therapeutic efficacy. The PI3K/AKT/mTOR signaling cascade, a key regulator of cell survival, proliferation, and DNA damage response, is frequently dysregulated in ovarian cancer and has emerged as a critical modulator of PARP inhibitor sensitivity. This review comprehensively examines preclinical and clinical evidence supporting the rationale for co-targeting the PI3K/AKT/mTOR axis to enhance the antitumor effects of PARP inhibitors. Natural and synthetic inhibitors of this pathway, as well as advanced nanotechnology-based delivery systems, have shown potential in overcoming intrinsic and acquired resistance to PARP inhibition. Furthermore, emerging data from biomarker-driven clinical trials highlight the importance of molecular stratification in optimizing treatment outcomes. Integrating PI3K/AKT/mTOR inhibition with PARP blockade represents a promising strategy to expand the therapeutic reach of PARP inhibitors and improve clinical outcomes in ovarian cancer.

Purpose: Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder, with dysregulated lipid metabolism and immune dysfunction. However, it remains unclear whether immune phenotypes mediate the relationship between lipidomes and PCOS.

Methods: A two-step Mendelian Randomization analysis was employed to explore the causal relationship between plasma lipidomes and PCOS and to investigate the mediating role of immune cells. A total of 179 plasma lipidomes and 731 immune phenotypes were analyzed. We used single nucleotide polymorphisms (SNPs) associated with plasma lipidome levels as instrumental variables and applied statistical methods, including the inverse-variance weighted approach, to assess potential causal relationships.The function of immune phenotypes in regulating the relationship between lipids and PCOS was evaluated through mediation analysis.

Results: Ten lipid-immune pathways mediating the association between plasma lipidomes and PCOS were identified. Elevated levels of phosphatidylcholines and triacylglycerols increased the risk of PCOS by modulating immune markers such as HLA DR on B cells and CD28 on regulatory T cells. Conversely, phosphatidylinositol (18:1_18:2) demonstrated a protective effect against PCOS through CD33 on myeloid-derived suppressor cells. Six specific plasma lipidomes were causally linked to PCOS risk, including phosphatidylcholine (18:1_20:4) and triacylglycerol (50:4), which increased risk, and phosphatidylinositol (18:1_18:2), which lowered risk. Additionally, 31 immune phenotypes were identified as causally associated with PCOS, with 27 increasing risk and 4 offering protective effects.

Conclusion: This study provides evidence that immune phenotypes mediate the relationship between plasma lipidomes and PCOS. These findings highlight the potential of targeting both lipid metabolic processes and immune pathways as novel therapeutic strategies for managing PCOS.

Background: Effective management of borderline ovarian tumors (BOTs) requires timely identification of patients at high risk of recurrence. Previous studies suggest that artificial neural networks can improve the prediction of BOT recurrence compared to traditional models, though concerns about their validity persist due to insufficient external validation. We aimed to evaluate the predictive performance of a time-dependent artificial neural network, conduct comprehensive temporal and spatial external validations to address this critical limitation.

Methods: Clinical data were collected from patients diagnosed with BOT at Shengjing Hospital of China Medical University between January 2014 and August 2023, including 76 cases of recurrence and 584 cases of non-recurrence. Using the Synthetic Minority Oversampling Technique (SMOTE), we balanced the groups at a 1:1 ratio (total sample size, N = 1168). Random sampling was used to divide the data into a training set (70%) and an internal validation set (30%). Temporal (same center; May 2011-December 2013) and spatial (different center; April 2011-April 2019) external validation sets were established. The training set data were input into the input layer of the neural network, high-level features were extracted via neurons in the hidden layer, and the model output was generated from the output layer. The model's sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, time-dependent area under the receiver operating characteristic curve (tdAUC), and integrated Brier score (IBS) were evaluated using the internal validation set, the temporal external validation set, and the spatial external validation set, respectively.

Results: A neural multi-task logistic regression model (N-MTLR) was constructed based on 34 features from the training set after correlation screening, with 9 variables selected for the neural network prediction model. The prediction model consisted of three functional layers with 128, 64, and 32 neurons, respectively, totaling 224 neurons. The optimal parameters for the final model were set as follows: initialization method of glorot_uniform, Dropout rate of 30%, L2 regularization parameter of 1e-2, optimizer of Adam, and learning rate of 1e-4. The N-MTLR model produced higher predictive performance including AUC, accuracy, specificity, PPV and NPV than the Cox-regression model for all survival endpoints at the 2-, the 4- and 7-year time points. Both temporal and spatial external validation results indicated that the model had moderate predictive performance and certain clinical application value.

Conclusions: The N-MTLR neural network enables superior nonlinear modeling of BOT recurrence risk, exhibits excellent temporal and spatial generalizability, which supports precise risk stratification for clinical decision-making.

Ovarian cancer is currently the gynaecological malignancy with the highest mortality rate, with a five-year survival rate of less than 50%. Combination chemotherapy regimens primarily based on platinum remain the main treatment for advanced ovarian cancer. However, while most patients initially respond sensitively to chemotherapy drugs, approximately 80% of patients develop resistance to chemotherapy drugs after repeated cycles of chemotherapy. Chemotherapy resistance is a key reason for treatment failure in patients with advanced ovarian cancer or recurrent ovarian cancer. There are many reasons for chemotherapy resistance in ovarian cancer patients, with tumor microenvironment emerging as a key focus. This "neighbor" has previously been regarded as a bystander during tumor initiation and growth. Through technological advances and deeper research, tumor microenvironment is now recognized as a critical active contributor to cancer progression, as well as chemotherapy resistance. Therefore, this paper reviews the research progress on the cellular and non-cellular components in the tumor microenvironment that contribute to chemotherapy resistance in ovarian cancer. Furthermore, we reviewed the impact of chemotherapy resistance from the perspectives of tumor hypoxia and tumor energy metabolism. Summarized the latest strategies for targeted tumor microenvironment therapy. With the aim of improving the prognosis of ovarian cancer patients, reversing chemotherapy resistance, and identifying drug treatment targets in the tumor microenvironment, this study provides new insights.