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Published on: August 1, 2018
Comparison of Long Non-Coding RNA Expressions in Endometrial Polyp and Endometrial Cancer Cases
Cagla Bahar Bulbul1, Ayla Solmaz Avcikurt2, Cagla Kayabasi2
1Department of Obstetrics & Gynecology, Balikesir Ataturk City and Research Hospital, 10100 Balikesir, Türkiye.
Abstract:
Aim: This study compared the expression of four long non-coding RNAs (lncRNAs)-XIST, UCA1, MALAT1, and ANRIL-in endometrial polyps (EP), endometrial cancer (EC), and normal endometrium to assess their diagnostic and prognostic potential. Materials and Methods: In this prospective study, 150 women undergoing endometrial biopsy between August 2021 and April 2024 were included (50 EP, 50 EC, 50 controls). RNA was extracted from FFPE tissue, converted to cDNA, and analyzed using SYBR Green-based qRT-PCR with U6 snRNA as reference. Statistical analysis included ANOVA/Kruskal-Wallis, logistic regression, and ROC analysis; p < 0.05 and fold change ≥±2 were considered significant. Results: The mean age was significantly higher in EC than in EP and controls (p < 0.05), with BMI also elevated (p = 0.006). UCA1 expression was upregulated in EP compared with controls (p = 0.008) but markedly downregulated in EC (p < 0.0005). XIST, MALAT1, and ANRIL showed upward trends in EC without independent statistical significance. Logistic regression identified age and UCA1 as the only independent predictors. Diagnostic accuracy was high: EC vs. control AUC = 0.98; EP vs. control AUC = 0.86; EC vs. EP AUC = 0.87. Age predicted malignancy, while high UCA1 was associated with EP and low UCA1 with EC. Discussion: Age and UCA1 expression were the strongest discriminators between lesion types. UCA1's dual, context-dependent role-promoting benign proliferation in EP and decreasing in EC-suggests potential biomarker utility. Other lncRNAs aligned with oncogenic functions but lacked independent predictive value. Combining molecular and clinical parameters could improve risk stratification and early detection, warranting validation in larger cohorts.
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