As a kind of gynecological tumor, ovarian cancer is not as common as cervical cancer and breast cancer, but its malignant degree is higher. Despite the increasingly mature treatment of ovarian cancer, the five-year survival rate of patients is still less than 50%. Based on the concept of synthetic lethality, poly (ADP- ribose) polymerase (PARP) inhibitors target tumor cells with defects in homologous recombination repair(HRR), the most significant being the target gene Breast cancer susceptibility genes(BRCA). PARP inhibitors capture PARP-1 protein at the site of DNA damage to destroy the original reaction, causing the accumulation of PARP-DNA nucleoprotein complexes, resulting in DNA double-strand breaks(DSBs) and cell death. PARP inhibitors have been approved for the treatment of ovarian cancer for several years and achieved good results. However, with the widespread use of PARP inhibitors, more and more attention has been paid to drug resistance and side effects. Therefore, further research is needed to understand the mechanism of PARP inhibitors, to be familiar with the adverse reactions of the drug, to explore the markers of its efficacy and prognosis, and to deal with its drug resistance. This review elaborates the use of PARP inhibitors in ovarian cancer.

Background: Ovarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis.Purpose: We aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers.Methods: A total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis.Results: ML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001).Conclusions: Based on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.

Ovarian cancer (OvCa) is the most lethal gynaecology malignancies worldwide. Neutrophil extracellular traps (NETs), net‐like protein structures produced by activated neutrophils and DNA‐histone complexes, have a central role in tumours, though haven't been fully explored in OvCa. We obtained transcriptome data from TCGA‐OvCa database (n = 376) as training, ICGC‐OvCa database (n = 111) as validation and GTEx database (n = 180) as controls. Through LASSO‐COX Regression analysis, we identified an eight‐gene signature among 87 NETs‐related genes, which was significantly related to poor prognosis in both TCGA‐OvCa and ICGC‐OvCa cohorts (Log‐rank p‐value = 0.0003 and 0.0014). Next, we constructed and validated a prognostic nomogram, consist of NETs‐related signature and clinical features (C‐index = 0.82). We evaluated 22 typical immune cell infiltration through CIBERSORT analysis, which implied upregulation of memory CD4 + T cells, follicular helper T cells and neutrophils in high‐risk group. Additionally, we predicted therapy sensitivity through TIDE algorithm, indicating that high NETs‐riskscore exhibited more sensitivity towards Sorafenib and less sensitivity towards immunotherapy. We initially reported that RAC2 upregulation was associated with NETs formation and poor prognosis (p‐value < 0.05) through IHC analysis of tissue microarrays (n = 125). Conclusively, NETs‐related signature was reliable for OvCa prognosis prediction and therapy assessment. Especially, RAC2 was predominantly related to NETs formation, thus providing hints towards anti‐tumour mechanism of NETs in OvCa.

Purpose The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried to develop a model to predict the FIGO stage of OC through machine learning algorithms with patients’ pretreatment clinical, positron emission tomography scan (PET/CT) metabolic, and radiomics features. Methods We enrolled OC patients who underwent PET/CT scans and divided them into two cohorts according to their FIGO stage. Then we manually delineated the volume of interest (VOI) and calculated PET metabolic features. Other PET/CT radiomics features were extracted by Python. We developed 11 prediction models to predict stages based on four groups of features and conducted three experiments to verify the meaning of PET/CT features. We also redesigned experiments to demonstrate the stage prediction performance in ovarian clear cell carcinoma (OCCC) and mucinous ovarian cancer (MCOC). Results 183 OC patients were enrolled in this study, and we obtained 137 features from four groups of data. The best model was an adaptive ensemble with an area under the curve (AUC) value of 0.819. Our proposed models presented the best result of 0.808 in terms of AUC in OCCC and MCOC patients’ groups. Conclusion Through artificial intelligence (AI) algorithms, the PET/CT metabolic and radiomics features combined with clinical features could improve the accuracy of staging prediction.

Background: Ovarian cancer (OC) remains the most common cause of gynecological cancer-related death, and about 70% of these deaths are from advanced high-grade serous ovarian cancer (HGSOC). Cysteine-rich secretory protein 3 (CRISP3) is related to various human diseases; however, the roles and mechanisms of CRISP3 in HGSOC remain unclear. Methods: The clinical significance of CRISP3 in patients with OC was analyzed using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. CRISP3 expression in OC tissues was validated by RNA-sequencing (RNA-seq), quantitative PCR, and immunohistochemistry. Furthermore, we explored the effect of CRISP3 expression modulation on the biological behavior of HGSOC through CCK-8, EdU, and Transwell assays in vitro, and the differences in CRISP3 during the progression of HGSOC in vivo. We utilized RNA-seq, GSEA and Western blotting to detect CRISP3’s regulatory mechanisms. Finally, we employed data from the IMvigor210 cohort and TCGA to assess the correlation of CRISP3 with clinical response to immunotherapy, and the landscape of immune cell infiltration. Results: CRISP3 expression was markedly reduced in HGSOC. In vitro studies demonstrated that CRISP3 knockdown significantly enhanced proliferation, migration, and invasion of HGSOC cells, whereas its overexpression suppressed these malignant phenotypes. Moreover, CRISP3 expression was found to be downregulated during OC progression in vivo. Mechanistically, CRISP3 acted as a tumor suppressor through the PI3K/AKT signaling pathway to inhibit the progression and metastasis of HGSOC. Additionally, we observed an association between CRISP3 expression and CD8⁺ T cell, macrophage, neutrophil and Th1 cell infiltration. Conclusions: We demonstrate that CRISP3 suppresses tumorigenesis in HGSOC by regulating the PI3K/AKT pathway, and that alterations in its expression correlate with disease progression, supporting its utility as a biomarker.

Immunotherapy has long played a pivotal role in cancer treatment, and antigen-presenting cell (APC)-based immunotherapy represents a promising strategy, though its full potential remains to be realized. This therapy stimulates T cell activation and proliferation through APCs, producing therapeutic effects. However, the use of natural APCs in immunotherapy faces several limitations, including inefficient T cell expansion, suboptimal targeting, and high treatment costs. Under such circumstances, artificial antigen-presenting cells (aAPCs) have garnered increasing interest. This review provides an overview of recent advancements in aAPCs research, systematically classifies aAPCs and critically evaluates their advantages and limitations. The underlying mechanisms of aAPCs, including activation and proliferation of T cells, strategies to overcome the immunosuppressive tumor microenvironment (TME), targeted antigen delivery and combination therapy, are discussed in detail. Their clinical applications are also debated. Existing challenges, including the absence of standardized protocols and regulatory frameworks, are highlighted. Furthermore, future directions in aAPCs technology are explored, including but not limited to the development of multifunctional aAPCs and the integration of artificial intelligence (AI) to assist in the design and optimization. Finally, to overcome these limitations, this review advocates for strengthened multidisciplinary collaboration and the adoption of emerging technologies.