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.

Ovarian cancer has the highest mortality rate among gynecologic tumors, with a 5-year survival rate of less than 25%. There is an urgent need for early diagnosis and new drugs to reduce the disease burden of ovarian cancer. The aim of this study was to investigate the effectiveness of SLC11A2 as a therapeutic target and marker for ovarian cancer. Expression data of SLC11A2 were obtained from public databases. Then, the biological functions of SLC11A2 were validated in four ovarian cancer cell lines. Finally, we collected ovarian cancer clinical tissues, serum, and plasma exosomes and used immunohistochemistry, Elisa, and liquid chromatography-mass spectrometry (LC–MS) to validate the test efficacy of SLC11A2. The results showed that ovarian cancers with high SLC11A2 mRNA expression had shorter 5-year PFS and MST. Knockdown of SLC11A2 reduced ovarian cancer migration and increased cisplatin-induced apoptosis. Serum SLC11A2 may help improve the detection rate of ovarian cancer.

The 5-year survival rate of ovarian cancer patients is only 47%, and developing novel drugs for ovarian cancer is needed. Herein, we evaluated if and how SRT2183, a sirtuin-1 activator, impairs the ovarian cancer cells. OVCAR-3 and A2780 cells were treated with SRT2183. Cell viability was measured by cell counting kit-8 assay and clonogenic assay. Apoptosis was determined by flow cytometry with Annexin V and propidium iodide. The level of autophagy was evaluated by western blot and immunofluorescence. The activities of AKT/mTOR/70s6k and MAPK signaling pathway were measured by immunoblot. SRT2183 inhibited the growth of ovarian cancer cells, increased the accumulation of BAX, cleaved-caspase 3 and cleaved-PARP, and decreased the level of anti-apoptotic Bcl-2 and Mcl-1. SRT2183 increased the LC3II level, and enhanced the degradation of p62/SQSTM1. SRT2183 increased the formation of GFP-LC3 puncta and induced the maturation of autophagosome. Interestingly, knockdown of autophagy related 5 and 7 significantly impaired the anti-carcinoma activity of SRT2183, implying that SRT2183 impaired the ovarian cancer cells by inducing autophagy. SRT2183 decreased the accumulation of p-Akt, p-mTOR and p-70s6k, and activated the p38 MAPK signaling pathway. This indicated that Akt/mTOR/70s6k and p38 MAPK signaling pathway might be involved in the SRT2183-mediated autophagy and apoptosis.

Widespread intra-peritoneal metastases is a main feature of high grade serous ovarian carcinoma (HGSOC). Recently, the extent of tumour heterogeneity was used to evaluate the cancer genomes among multi-regions in HGSOC. However, there is no consensus on the effect of tumour heterogeneity on the evolution of the tumour metastasis process in HGSOC. We performed whole-exome sequencing in multiple regions of matched primary and metastatic HGSOC specimens to reveal the genetic mechanisms of ovarian tumourigenesis and malignant progression. 63 samples (including ovarian carcinoma, omentum metastasis, and normal tissues) were used. We analyzed the genomic heterogeneity, traced the subclone dissemination and establishment history and compared the different genetic characters of cancer evolutionary models in HGSOC. We found that HGSOC had substantial intra-tumour heterogeneity (median 54.2, range 0 ∼ 106.7), high inter-patient heterogeneity (P < 0.001), but relatively limited intra-patient heterogeneity (P = 0.949). Two COSMIC mutational signatures were identified in HGSOCs: signature 3 was related to homologous recombination, and signature 1 was associated with aging. Two scenarios were identified by phylogenetic reconstruction in our study: 3 cases (33.3 %) showed star topology, and the other 6 cases (66.7 %) displayed tree topology. Compared with star topology group, more driver events were identified in tree topology group (P < 0.001), and occurred more frequently in early stage than in late stage of clonal evolution (P < 0.001). Moreover, compared with the star topology group, the tree topology group showed higher rate of intra-tumour heterogeneity (P = 0.045). A dualistic classification model was proposed for the classification of HGSOC based on spatial heterogeneity, which may contribute to better managing patients and providing individual treatment for HGSOC patients.

Background: Cancer is a life-threatening disease in humans; yet, cancer genes are frequently reported to be under positive selection. This suggests an evolutionary-genetic paradox in which cancer evolves as a secondary product of selection in human beings. However, systematic investigation of the evolution of cancer driver genes is sparse.Results: Using comparative genomics analysis, population genetics analysis and computational molecular evolutionary analysis, the evolution of 568 cancer driver genes of 66 cancer types were evaluated at two levels, selection on the early evolution of humans (long timescale selection in the human lineage during primate evolution, i.e., millions of years), and recent selection in modern human populations (~ 100,000 years). Results showed that eight cancer genes covering 11 cancer types were under positive selection in the human lineage (long timescale selection). And 35 cancer genes covering 47 cancer types were under positive selection in modern human populations (recent selection). Moreover, SNPs associated with thyroid cancer in three thyroid cancer driver genes (CUX1, HERC2 and RGPD3) were under positive selection in East Asian and European populations, consistent with the high incidence of thyroid cancer in these populations.Conclusions: These findings suggest that cancer can be evolved, in part, as a by-product of adaptive changes in humans. Different SNPs at the same locus can be under different selection pressures in different populations, and thus should be under consideration during precision medicine, especially for targeted medicine in specific populations.