Shijun Wang

A Novel Nomogram for Predicting Endometrial Malignancy in Postmenopausal Women

ABSTRACT Aim To identify clinical risk factors for endometrial malignancy in postmenopausal women and develop a predictive model for early detection and personalized intervention. Methods This study analyzed 1146 postmenopausal women undergoing diagnostic hysteroscopy. Inclusion required: confirmed menopause (age ≥ 40) with recent endometrial thickness measurement, successful hysteroscopy, histopathological verification, and complete records. Exclusions involved incomplete data, type II carcinoma, hormonally active tumors, or prior hysteroscopy indications. Demographics, clinical features, comorbidities, imaging data, and biomarkers were analyzed. Histology was confirmed via standard pathology. Risk factors were identified through univariate and multivariate logistic regression. The resultant predictive nomogram for endometrial malignancy risk stratification underwent rigorous validation using: (1) receiver operating characteristic curve analysis (discriminative power); (2) calibration plotting (prediction accuracy); and (3) decision curve analysis (clinical net benefit). Results Among 1146 postmenopausal women undergoing diagnostic hysteroscopy, histopathological analysis identified 69 cases (6.0%) of endometrial carcinoma (EC) and 15 cases (1.3%) of atypical endometrial hyperplasia, with the remaining cases (92.7%) demonstrating benign pathology. Multivariate analysis identified seven independent risk factors for EC: elevated fibrinogen and D‐dimer levels, hypertriglyceridemia, decreased high‐density lipoprotein, postmenopausal bleeding, ultrasonography blood‐flow signals, and increased endometrial thickness. The predictive nomogram incorporating these parameters demonstrated outstanding diagnostic performance, with area under the curve values of 0.955 in the training cohort and 0.960 in the validation cohort, indicating excellent discriminative ability for EC risk stratification. Conclusion We developed and validated a novel 7‐indicator prediction model for assessing endometrial malignancy risk in postmenopausal women undergoing hysteroscopy biopsy.

The Association between Five Genetic Variants in MicroRNAs (rs2910164, rs11614913, rs3746444, rs11134527, and rs531564) and Cervical Cancer Risk: A Meta‐Analysis

The objective of this study was to conduct a meta‐analysis to systematically summarize and investigate the association of miRNA‐124 rs531564, miRNA‐218 rs11134527, miRNA‐146a rs2910164, miRNA‐196a2 rs11614913, and miRNA‐499 rs3746444 polymorphisms with cervical cancer. A systematic review was performed to identify relevant studies using Embase and PubMed databases. A chi‐square‐based Q‐test combined with the inconsistency index (I2) was used to check the heterogeneity between studies. A total of six case‐control studies on rs2910164 and rs11614913, 4 studies on rs3746444 and rs11134527, and three studies on rs531564 were included. No evidence of association was found between miR‐146a rs2910164, miR‐196a2 rs11614913, miRNA‐499 rs3746444, and miR‐218 rs11134527 polymorphisms and cervical cancer risk in all the genetic models. The miR‐124 rs531564 polymorphism was associated with a statistically increased risk of cervical cancer in a homozygote model (CC vs. GG: OR = 2.87, 95% CI: 1.40‐5.91, PH = 0.887), dominant model (GC/CC vs. GG: OR = 1.38, 95% CI: 1.07‐1.80, PH = 0.409), and recessive model (CC vs. GC/GG: OR = 2.26, 95% CI: 1.58‐3.23, PH = 0.979). However, this finding should be interpreted with caution for limited samples and heterogeneity. Large‐scale and well‐designed studies are needed to validate our result.