Changes in the Management and Prognosis of Ovarian Cancer Due to the New FIGO and WHO Classifications: A Case Series Observational Descriptive Study. Seven Years of Follow-up

Value of the risk of ovarian malignancy algorithm index in predicting the recurrence of epithelial ovarian cancer

Aim: This study aimed to assess the predictive and diagnostic value of the risk of ovarian malignancy algorithm (ROMA) index for epithelial ovarian cancer (EOC) recurrence. Materials & methods: The clinical features and follow-up data of 159 EOC cases were studied. The ROMA index was calculated by serum CA125 and HE4 levels with menopausal status. Recurrence-free survival was evaluated for an end point. Results: The ROMA was strongly associated with clinical characteristics. The ROMA index above the cutoff value (34.71%) was significantly associated with recurrence-free survival. The ROMA index had a significantly higher sensitivity (90.59%) than CA125 (84.71%) and HE4 (80.80%) for recurrence diagnosis, and its optimal cutoff value was 17.07%. Conclusion: The primary ROMA index is a predictive factor in EOC recurrence and has better performance in the diagnosis of EOC recurrence.

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma

Objective

To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC).

Methods

This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. The area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves were assessed to evaluate the performance of classification models and nomograms in discriminating recurrence. Kaplan-Meier survival analysis was used to evaluate the associations between the Radscore or clinical factors and disease-free survival (DFS).

Results

One clinical factor and seven radiomics signatures were ultimately selected to establish the predictive model for this study. The AUCs for identifying recurrence in the training and validation cohorts were 0.76 (0.68, 0.84) and 0.67 (0.53, 0.81) with the clinical model, 0.78 (0.71, 0.86) and 0.74 (0.61, 0.86) with the multiradiomics model, and 0.83 (0.77, 0.90) and 0.78 (0.65, 0.90) with the combined nomogram, respectively. The DFS was significantly shorter in the high-risk group than in the low-risk group.

Conclusion

By incorporating radiomics Radscores and clinical factors, we created a radiomics nomogram to preoperatively identify patients with HGSOC who have a high risk of recurrence, which may serve as a potential tool to guide personalized treatment.

A prognostic nomogram for women with primary ovarian signet-ring cell carcinoma

Background

Primary ovarian signet-ring cell carcinoma (POSRCC) is a rare subtype of ovarian carcinoma that is characterized by abundant mucin accumulation. POSRCC is aggressive, and the prognostic factors associated with its clinical outcome remain poorly defined. This study aimed to elucidate the clinical characteristics and survival of patients with POSRCC, and to establish an effective prognostic nomogram and risk stratification model to predict the risks associated with patient outcomes.

Methods

Data of patients with POSRCC from the period 1975 to 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable analyses of demographic factors, clinicopathological characteristics, and treatments were conducted to identify significant prognostic parameters. The identified independent variables were integrated to develop a nomogram and risk stratification model. The discrimination and calibration of the nomogram were assessed with the concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration curves.

Results

A total of 172 patients were identified as being eligible to participate in this study. The median overall survival (OS) time was 7 months [95% confidence interval (CI), 4.6-9.4 months]. The 1-, 3-, and 5-year OS rates were 35.5%, 15.3%, and 6%, respectively. A multivariable analysis of the primary patients identified the independent predictors for survival as age at diagnosis, race, marital status, T (primary tumor size) stage, and chemotherapy, which were all incorporated into the nomogram. The C-index was 0.70 (95% CI, 0.66-0.75), which was statistically higher than that of the International Federation of Gynecology and Obstetrics (FIGO) staging system (0.58; 95% CI, 0.53-0.63). ROC curve analysis also showed that the nomogram had good discrimination, with an area under the curve (AUC) of 0.74, 0.62, and 0.71 for 1-, 3-, and 5-year survival, respectively. The calibration curves showed good agreement between the prediction by the nomogram and actual observations. A risk stratification model was further used to classify patients into a low-risk or high-risk group. The median OS time for the low- and high-risk groups was 13.0 months (95% CI, 9.33-16.67) and 2.0 months (95% CI, 1.12-2.89), respectively. Surgery did not significantly prolong survival in either group [low-risk group: hazard ratio (HR), 0.69; 95% CI, 0.45-1.07; P=0.09; high-risk group: HR, 0.55; 95% CI, 0.46-0.67; P=0.18].

Conclusions

The proposed nomogram and risk stratification model showed accurate prognostic prediction for POSRCC. These methods could improve individualized evaluations of survival and therapeutic decisions for patients with POSRCC.

LncRNA NEAT1 promotes proliferation of ovarian cancer cells and angiogenesis of co-incubated human umbilical vein endothelial cells by regulating FGF9 through sponging miR-365: An experimental study

OBJECTIVE

To uncover the function of lncRNA NEAT1 in ovarian cancer (OC) cells and its mechanism.

METHODS

The expression patterns of lncRNA NEAT1 and FGF9 in human OC cells and human ovarian epithelial cells was determined. OC cells were transfected with sh-NEAT1, pcDNA3.1-NEAT1, miR-365 mimic, miR-365 inhibitor or pcDNA3.1-NEAT1 + sh-NEAT1 before cell proliferation rate and cell clone formation rate were measured. After the transfected OC cells were co-cultivated with human umbilical vein endothelial cells (HUVECs), Matrigel angiogenesis assay tested angiogenesis of HUVECs; qRT-PCR and Western blot tested the expressions of vascular endothelial growth factor (VEGF), angiogenin 1 (Ang-1) and matrix metalloproteinase 2 (MMP2). Dual-luciferase reporter assay determined the targeted binding of NEAT1 and FGF9 to miR-365.

RESULTS

LncRNA NEAT1 and FGF9 are over-expressed in OC cells. Knockdown of NEAT1 or FGF9, or over-expression of miR-365 results in decreased proliferation rate and cell clones as well as inhibited angiogenesis and down-regulated expressions of VEGF, Ang-1 and MMP2. Over-expression of NEAT1 or knockdown of miR-365 can reverse the effect caused by FGF9 knockdown. NEAT1 can down-regulate the expression of miR-365 while up-regulating that of FGF9. Dual-luciferase reporter assay determined that NEAT1 competes with FGF9 for binding to miR-365.

CONCLUSION

LncRNA NEAT1 up-regulates FGF9 by sponging miR-365, thus promoting OC cell proliferation and angiogenesis of HUVECs.

Claudin 10 acts as a novel biomarker for the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is one of the most fatal gynecological malignancies in the world and confers a poor 5-year survival rate. The present study was designed to discover novel prognostic markers for patients with OC in order to estimate disease metastasis or recurrence. Based on the large cohorts of transcriptome data from multicenter sources, a comprehensive analysis was performed to explore potential prognostic markers. A total of 269 differentially expressed genes were identified, of which 32 were upregulated and 237 downregulated in OC tissues compared with the corresponding expression in normal tissues. Kaplan-Meier analysis, log-rank test and nomogram analysis were employed to demonstrate that low expression levels of claudin 10 (CLDN10) were associated with a less favorable disease prognosis. The most promising prognostic marker for OC was subsequently selected. Additionally, the prognostic nomogram was constructed in order to assess the 5-year survival rate using CLDN10 expression as a prognostic marker for OC. Furthermore, gene set enrichment analysis and analysis of the tumor-associated competing endogenous RNA network were performed to elucidate the potential biological processes associated with CLDN10 expression. The current results indicated that CLDN10 may influence OC progression via transforming growth factor-β (TGF-β)- or WNT/β-catenin-induced epithelial-to-mesenchymal transition (EMT). The associations among CLDN10, microRNA-486-5p, TGF-β, WNT/β-catenin and EMT should be further investigated in future studies.