Space-time analysis of ovarian cancer mortality rates by age groups in spanish provinces (1989-2015)

The performance of Carbohydrate Antigen 125-Thomsen-nouveau and anti-Müllerian hormone combined with CA125, Human epididymis protein 4 and Risk of Malignancy Algorithm in diagnosis for patients with Epithelial ovarian cancer

OBJECTIVES

We examined the blood concentrations of Carbohydrate Antigen 125-Thomsen-nouveau (CA125-Tn) and anti-Müllerian hormone (AMH) in epithelial ovarian cancer (EOC) patients to evaluate their potential diagnostic utility together with CA125, human epididymis protein 4 (HE4) and Risk of Malignancy Algorithm (ROMA).

DESIGN & METHODS

50 healthy subjects, 45 EOC patients, 22 patients with borderline ovarian tumors (BOT), 21 patients with benign ovarian tumor (BET) and 45 patients with chocolate cyst of ovary (CCO) were studied. Blood levels of CA125, HE4, CA125-Tn and AMH were measured, and the ROMA value was calculated. We compared the differences in the levels of these biomarkers among groups. Additionally, a total of 10 testing strategies were established for comparison to maximize the diagnostic value.

RESULTS

The levels of CA125, HE4, CA125-Tn and ROMA value were significantly higher in EOC group compared with either the disease control (DC) group (BOT group, BET group and CCO group) or healthy control (HC) group (p < 0.001). In addition, they had better discriminatory performance with an area under the receiver operator characteristic curve (AUC) 0.93; 0.93; 0.93; 0.85, respectively (p < 0.001) compared with the AUC value of AMH 0.67 (p < 0.001). Among all 10 testing strategies, both single-positive of ROMA and double-positive of any 2 markers showed better Youden index (0.82, 0.79, respectively) and kappa value (κ) (0.82, 0.81, respectively).

CONCLUSIONS

CA125-Tn and AMH can be treated as useful biomarkers of EOC when combined with CA125, HE4 and ROMA, because when any two biomarkers of them are positive, the value of EOC diagnosis is maximized.

m6A-Related lncRNA Signature Is Involved in Immunosuppression and Predicts the Patient Prognosis of the Age-Associated Ovarian Cancer

Background

Epithelial ovarian cancers are age-associated diseases, usually diagnosed at an advanced stage. lncRNA has been discovered to interplay with N6-methyladenosine (m6A), working in tandem to promote cancer progression and worsening patient outcomes. This study is aimed at investigating the roles and mechanism of m6A-related lncRNA signature on ovarian cancers.

Methods

We retrieved TCGA and CGGA sequencing data to identify m6A-related lncRNA signature and constructed an m6A score (MS) using the LASSO algorithm. A clinical nomogram was then established to predict the overall survival of patients. Subsequently, GSEA analyses were conducted to obtain pathways involved. Expression of HLA genes, 28 tumor-infiltrating lymphocyte infiltration, and anticancer cycle were analyzed the immunological differences between high-MS and low-MS groups. Finally, immune checkpoint gene expressions and IC₅₀ of chemotherapeutic drugs were calculated, and CMap was run to identify the potential compounds and their corresponding mechanisms.

Results

We identified 16 m6A-related lncRNAs and constructed an MS model. The high-MS group showed a poor prognosis. A clinical nomogram consists of MS, and age was constructed and predicted the 1-, 3-, and 5-year survival with high accuracy. GSEA analyses presented downregulated antigen processing and presentation pathways. Immunocyte infiltrating analyses demonstrated that high-MS was associated with high infiltration of Treg cells, macrophages, and low Th1/Th2 rate. Also, high expression of immune checkpoint genes NRP1, TNFSF9, and VSIR was observed in the high-MS group. Finally, the high-MS group also predicted low IC₅₀ of vinorelbine and vorinostat.

Conclusion

This study constructed a robust prediction model for prognostic management and revealed the cross-talk between m6A and immunosuppression. Besides, the m6A lncRNA signature can predict the chemotherapeutic drug response. These will shed light on the development of novel therapeutic strategies and render survival benefits for ovarian patients.

A measurement error Rao–Yu model for regional prevalence estimation over time using uncertain data obtained from dependent survey estimates

The assessment of prevalence on regional levels is an important element of public health reporting. Since regional prevalence is rarely collected in registers, corresponding figures are often estimated via small area estimation using suitable health data. However, such data are frequently subject to uncertainty as values have been estimated from surveys. In that case, the method for prevalence estimation must explicitly account for data uncertainty to allow for reliable results. This can be achieved via measurement error models that introduce distribution assumptions on the noisy data. However, these methods usually require target and explanatory variable errors to be independent. This does not hold when data for both have been estimated from the same survey, which is sometimes the case in official statistics. If not accounted for, prevalence estimates can be severely biased. We propose a new measurement error model for regional prevalence estimation that is suitable for settings where target and explanatory variable errors are dependent. We derive empirical best predictors and demonstrate mean-squared error estimation. A maximum likelihood approach for model parameter estimation is presented. Simulation experiments are conducted to prove the effectiveness of the method. An application to regional hypertension prevalence estimation in Germany is provided.