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aimed to develop an ovarian cancer-specific predictive framework for clinical use platinum-sensitivity and prognosis using machine learning methods based on multiple biomarkers, including circulating tumor cells (CTCs). Patients and Methods: We enrolled 156 epithelial ovarian cancer (EOC) patients, randomly assigned into the training and validation cohorts. Eight machine learning classifiers, including Random Forest (RF), Support Vector Machine, Gradient Boosting Machine, Conditional RF, Neural Network, Naive Bayes, Elastic Net, and Logistic Regression, were used to derive predictive information from 11 peripheral blood parameters, including CTCs. Through the advanced CanPatrol CTC-enrichment technique, we detect CTCs and classify them into subpopulations: epithelial, mesenchymal, and hybrids. Survival curves were generated by Kaplan–Meier method and calculated through the Log rank test. Results: Machine learning techniques, especially the Random Forest classifier, were superior to conventional regression-based analyses in predicting multiple clinical parameters related to EOC. The values for the receiver operating characteristic (ROC) curve for segregating EOC with advanced clinical stages and platinum-sensitivity were 0.796 (95% CI, 0.727– 0.866) and 0.809 (95% CI, 0.742– 0.876), respectively. Stepwise, we used the unsupervised clustering analysis to identify EOC subgroups with significantly worse overall survival (OS), especially in the advanced-stage group with the p-value of 0.0018 (HR, 2.716; 95% CI, 1.602– 4.605) for progression-free survival (PFS) and 0.0037 (HR, 2.359; 95% CI, 1.752– 6.390) for overall survival (OS). Conclusion: Machine learning systems could provide risk stratification for EOC patients before initial intervention through blood variables, including circulating tumor cells. The predictive algorithms could facilitate personalized treatment options through promising pre-treatment stratification of EOC patients. Trial registration: ChiCTR-DDD-16009601 Registered 25 October 2016. [ABSTRACT FROM AUTHOR]

We aimed to determine the prognosis value of circulating tumor cells (CTCs) undergoing epithelial–mesenchymal transition in epithelial ovarian cancer (EOC) recurrence. We used CanPatrol CTC-enrichment technique to detect CTCs from blood samples and classify subpopulations into epithelial, mesenchymal, and hybrids. To construct nomogram, prognostic factors were selected by Cox regression analysis. Risk stratification was performed through Kaplan–Meier analysis among the training group (n = 114) and validation group (n = 38). By regression screening, both CTC counts (HR 1.187; 95% CI 1.098–1.752; p = 0.012) and M-CTC (HR 1.098; 95% CI 1.047–1.320; p = 0.009) were demonstrated as independent factors for recurrence. Other variables including pathological grade, FIGO stage, lymph node metastasis, ascites, and CA-125 were also selected (p < 0.005) to construct nomogram. The C-index of internal and external validation for nomogram was 0.913 and 0.874. We found significant predictive values for the nomogram with/without CTCs (AUC 0.8705 and 0.8097). Taking CTC counts and M-CTC into separation, the values were 0.8075 and 0.8262. Finally, survival curves of risk stratification based on CTC counts (p = 0.0241), M-CTC (p = 0.0107), and the nomogram (p = 0.0021) were drawn with significant differences. In conclusion, CTCs could serve as a novel factor for EOC prognosis. Nomogram model constructed by CTCs and other clinical parameters could predict EOC recurrence and perform risk stratification for clinical decision-making. Trial registration Chinese Clinical Trial Registry, ChiCTR-DDD-16009601, October 25, 2016. [ABSTRACT FROM AUTHOR]

aimed to investigate the expression pattern and prognostic significance of circ_0078607 in high-grade serous ovarian cancer (HGSOC). Methods: The expression of circ_0078607 was detected by quantitative real-time polymerase chain reaction (qRT-PCR) in 49 cases of HGSOC. Clinical data of patients with HGSOC were retrospectively collected, and those patients were divided according to their expression of circ_0078607. Correlation between circ_0078607 and clinical features as well as the prognosis in patients with HGSOC was analyzed. t-test and chi-square test were used to compare continuous and categorical variables. The Cox hazard regression model was used to assess prognostic factors. Both progression-free survival (PFS) and overall survival (OS) curves were generated by Kaplan–Meier method. Results: The expression of circ_0078607 was significantly downregulated in ovarian cancer tissues compared with adjacent non-cancerous tissues. Besides, patients with low circ_0078607 expression exhibited parameters associated with poor prognosis, including advanced FIGO stage and higher serum CA125 level. Kaplan–Meier survival curve analysis showed that patients with low circ_0078607 expression had shorter PFS and OS. Cox regression analysis showed that low expression of circ_0078607 was a predictor for poor PFS and OS in HGSOC patients. Conclusion: Low expression of circ_0078607 might be an adverse prognostic indicator for HGSOC. [ABSTRACT FROM AUTHOR]

Ovarian cancer is the most lethal gynecological malignancy with high recurrence rates and low survival rates, remaining a disease of high unmet need. Cancer immunotherapy, which harnesses the potential of the immune system to attack tumors, has emerged as one of the most promising treatment options in recent years. As an important form of immunotherapy, dendritic cell (DC)–based vaccines have demonstrated the ability to induce an immune response, while clinical efficacy of DC vaccines remains unsubstantiated as long‐term benefit is only reported in a restricted proportion of patients. Here, a biomimetic nanovaccine derived from DCs is developed through cell membrane coating nanotechnology. This nanovaccine, denoted "mini DC," inherits the ability of antigen presentation and T cells' stimulation from DCs and is shown to elicit enhanced activation of T cells both in vitro and in vivo. In a mouse model of ovarian cancer, mini DCs exhibit superior therapeutic and prophylactic efficacy against cancer including delayed tumor growth and reduced tumor metastasis compared with DC vaccine. These findings suggest that mini DCs may serve as a facile and potent vaccine to boost anticancer immunotherapy. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is the most lethal gynecologic malignance worldwide. Considering its metastasis nature, oncologists shift focus towards circulating tumor cells (CTCs), a progenitor that originates from primary tumor and undergoes morphologic/genetic alterations to enter bloodstream and invade nearby tissues. Mountains of evidence suggested that CTCs could provide deep insights into genomic, transcriptomic, and proteomic profiling of OC metastatic cascades. To pave the way for precision medicine, researchers exert great efforts to develop isolation/detection methodologies and construct CTCs-derived propagation platforms, including traditional cell cultures, patient-derived xenografts (PDXs), and organoids. From bench to bedside, CTCs provide minimally-invasive means to inform early diagnosis, predict prognosis, and guide treatment decisions. This review shined a spotlight on biology, detection technologies, and propagation platforms for CTCs. Of note, we also reviewed clinical applications of CTCs in liquid biopsy-based personalized cancer treatment and critically appraised limitations in routine clinical practice on the path to precision medicine. [ABSTRACT FROM AUTHOR]

After it was discovered approximately 40 years ago, carbohydrate antigen 125 (CA125) became the most widely used and concerning biomarker in ovarian cancer screening. However, there is still controversy about its role in clinical practice. CA125 is not sufficiently reliable in diagnosis to screen for early-stage ovarian cancer. On the other hand, CA125 has been a valuable indicator for evaluating chemotherapeutic efficacy and prognosis. We still do not know much about its biological role, and several studies have indicated that this marker participates in the occurrence and development of ovarian cancer. Currently, an increasing number of scholars have begun to pay attention to CA125-targeted treatment strategies. In the interest of better design and development of anticancer therapies, a renewed and systematic understanding of the roles of CA125 in diagnosis, prediction, and tumorigenesis is warranted. [ABSTRACT FROM AUTHOR]

Centromere proteins (CENPs) are nuclear proteins that are involved in centromere formation and chromosome segregation during mitosis. Some members of CENPs have been extensively studied in the initiation and development of cancers. However, the expression patterns and exact roles of CENPs in ovarian cancer (OC) have not been fully elucidated. In this study, we comprehensively assessed the genetic variation, expression patterns and prognostic value of CENPs in OC by several databases. The mRNA expression levels of CENPA/F/H/L/N/U/W were found to be significantly upregulated in OC and related to worse prognosis. Additionally, function enrichment analysis showed that CENPs were involved in DNA repair and cell division. Meanwhile, immune infiltration analysis elucidated that CENPs were associated with myeloid-derived suppressor cells (MDSCs) and major histocompatibility complex (MHC). These results suggested that CENPs might serve as potential diagnostic and prognostic markers and provide new insights for the development of CENPs-targeted therapeutics for ovarian cancer. • Centromere proteins may serve as potential diagnostic and prognostic markers for ovarian cancer. • Centromere proteins are associated with myeloid-derived suppressor cells and major histocompatibility complex. • Our study is a systematic and comprehensive analysis of the role of CENP genes in ovarian cancer. • Our study regarding centromere proteins provides new insights for the targeted therapeutics for ovarian cancer. [ABSTRACT FROM AUTHOR]