Although ovarian cancer, a gynecological malignancy, has the highest fatality rate, it still lacks highly specific biomarkers, and the differential diagnosis of ovarian masses remains difficult to determine for gynecologists. Our study aimed to obtain ovarian cancer-specific protein candidates from the circulating small extracellular vesicles (sEVs) and develop a protein panel for ovarian cancer screening and differential diagnosis of ovarian masses. In our study, sEVs derived from the serum of healthy controls and patients with cystadenoma and ovarian cancer were investigated to obtain a cancer-specific proteomic profile. In a discovery cohort, 1119 proteins were identified, and significant differences in the protein profiles of EVs were observed among groups. Then, 23 differentially expressed proteins were assessed using the parallel reaction monitoring in a validation cohort. Through univariate and multivariate logistic regression analyses, a novel model comprising three proteins (fibrinogen gamma gene (FGG), mucin 16 (MUC16), and apolipoprotein (APOA4)) was established to screen patients with ovarian cancer. This model exhibited an area under the receiver operating characteristic curve (AUC) of 0.936 (95% CI, 0.888–0.984) with 92.0% sensitivity and 82.9% specificity. Another panel comprising serum CA125, sEV-APOA4, and sEV-CD5L showed excellent performance (AUC 0.945 (95% CI, 0.890–1.000), sensitivity of 88.0%, specificity of 93.3%, and accuracy of 89.2%) to distinguish malignancy from benign ovarian masses. Altogether, our study provided a proteomic signature of circulating sEVs in ovarian cancer. The diagnostic proteomic panel may complement current clinical diagnostic measures for screening ovarian cancer in the general population and the differential diagnosis of ovarian masses.

To develop a novel diagnostic nomogram model to predict malignancy in patients with ovarian masses. In total, 1277 patients with ovarian masses were retrospectively analyzed. Receiver operating characteristic (ROC) analysis was performed to identify valuable predictive factors. Univariate and multivariate logistic regression analyses were used to identify risk factors for ovarian cancer. Subsequently, a predictive nomogram model was developed. The performance of the nomogram model was assessed by its calibration and discrimination in a validation cohort. Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model. Overall, 496 patients (38.8%) had ovarian cancer. Eighteen parameters were significantly different between the malignant and benign groups. Five parameters were identified as being most optimal for predicting malignancy, including age, carbohydrate antigen 125, fibrinogen-to-albumin ratio, monocyte-to-lymphocyte ratio, and ultrasound result. These parameters were incorporated to establish a nomogram model, and this model exhibited an area under the ROC curve (AUC) of 0.937 (95% confidence interval [CI], 0.920-0.954). The model was also well calibrated in the validation cohort and showed an AUC of 0.925 (95%CI, 0.896-0.953) at the cut-off point of 0.298. DCA confirmed that the nomogram model achieved the best clinical utility with almost the entire range of threshold probabilities. The model has demonstrated superior efficacy in predicting malignancy compared to currently available models, including the risk of ovarian malignancy algorithm, copenhagen index, and the risk of malignancy index. More importantly, the nomogram established here showed potential value in identification of early-stage ovarian cancer. The cost-effective and easily accessible nomogram model exhibited favorable accuracy for preoperative prediction of malignancy in patients with ovarian masses, even at early stages.

Endometriosis, a chronic immune-mediated inflammatory disease, remains elusive in its pathogenesis. Given vitamin D (VD)’s pivotal role in modulating innate and adaptive immune responses, we sought to elucidate how VD modulates inflammatory responses in endometriosis. We isolated primary human ectopic endometrial stromal cells (EESCs) from ectopic endometrium of ovarian endometrioma, alongside Ishikawa cells, and subjected them to treatment with lipopolysaccharide (LPS), a potent inducer of inflammation, alongside varying concentrations of 1,25-dihydroxyvitamin D₃ (1,25(OH)₂D₃), the biologically active form of VD, and its analog TEI-9647 (25-dehydro-1α-hydroxyvitamin D₃-26,23-lactone). Our results revealed that 1,25(OH)₂D₃ significantly reversed LPS-induced cell proliferation, migration, and inflammatory factor production in EESCs and Ishikawa cells, and induced apoptosis. Additionally, 1,25(OH)₂D₃ inhibited the expression and nuclear translocation of phosphorylated p65 in LPS-activated EESCs and Ishikawa cells. Furthermore, 1,25(OH)₂D₃ counteracted LPS-induced suppression of VD receptor (VDR)/IκBα and enhancement of Toll-like receptor 4 (TLR4)/pyrin domain (PYD)-containing protein 3 (NLRP3) activation, while the addition of TEI-9647 reversed VD’s regulatory effects on the NF-κB pathway. In vivo experimental results showed that 1,25(OH)₂D₃ significantly inhibited lesion growth, suppressed NF-κB pathway activation, and corresponding inflammatory phenotypes in a rat model of endometriosis. Collectively, these results underscore the potential of 1,25(OH)₂D₃ as a therapeutic target for endometriosis via VDR-dependent endometrial homeostasis regulation, suppressing LPS-mediated inflammatory responses and NF-κB signaling pathway through VDR activation and IκBα stabilization.

Background: Endometriosis (EMS), which affects >10% of women, is characterized by painful symptoms, such as dysmenorrhea. Meanwhile, the gut microbiota is linked to EMS; however, the relationship between the gut microbiota and EMS-related pain remains unclear. Methods: This study conducted 16S rRNA sequencing on fecal samples from 20 patients with EMS and dysmenorrhea, 13 patients with EMS but not dysmenorrhea, and 12 healthy controls. Results: Carbohydrate antigen 125 (CA125) levels were significantly higher in patients with EMS and dysmenorrhea. No significant differences in α- and β-diversity values were observed among the three groups (p > 0.05). However, Proteobacteria and Subdoligranulum showed increased abundance trends in patients with EMS and dysmenorrhea. Acinetobacter and Colidextribacter were less abundant in patients with EMS and dysmenorrhea than in those without dysmenorrhea (p < 0.05). Faecalicoccus pleomorphus and its genus also showed consistent depletion. At the genus level, a correlation analysis of the differential microbes revealed that UCG_005 and UCG_002 were central nodes in the correlation network (p < 0.05). Functional prediction indicated significant enrichment in cofactor and vitamin metabolism pathways in the EMS group with dysmenorrhea (p < 0.05). Several differential microbes, such as Faecalicoccus, Colidextribacter, and Acinetobacter, were negatively correlated with pain severity. Receiver operating characteristic curve analysis identified 10 microbial species with moderate diagnostic potential (area under the curve >0.7) for discriminating patients with EMS and dysmenorrhea, notably Ligilactobacillus. Conclusions: These results highlight distinct microbial alterations and functional pathways associated with EMS-related pain, suggesting potential diagnostic biomarkers for this condition.