Predictive Significance of Glycosyltransferase-Related lncRNAs in Endometrial Cancer: A Comprehensive Analysis and Experimental Validation

Xiaoyu Shen ¹, Yan Liu ¹, Jun Zhang ¹

⁰Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.

Acs Omega +

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March 10, 2025

View abstract on PubMed

Summary

This study identifies four key glycosyltransferase-related long noncoding RNAs (GTRLs) that predict prognosis in endometrial cancer (EC). The developed model aids in predicting patient outcomes and guiding treatment decisions for EC.

Area Of Science

Oncology
Molecular Biology
Genomics

Background

Glycosylation is vital for cellular functions, and aberrant glycosyltransferase expression is linked to cancer.
Long noncoding RNAs (lncRNAs) regulate gene expression, but their role in endometrial cancer (EC) via glycosyltransferases is poorly understood.

Purpose Of The Study

To develop a novel prognostic model for endometrial cancer (EC) based on glycosyltransferase-related long noncoding RNAs (GTRLs).
To investigate the association of GTRLs with immune infiltration, tumor mutation burden, and chemosensitivity in EC.

Main Methods

Univariate Cox regression, Lasso, and multivariate stepwise Cox regression were used to identify prognostic GTRLs.
Kaplan-Meier survival analysis, ROC curves, and nomograms were employed for prognostic assessment.
In vitro experiments and analysis of TCGA-UCEC data were performed to validate the model and specific GTRLs.

Main Results

Four GTRLs were identified as significant independent prognostic factors for EC.
The GTRL-based risk score effectively predicted patient prognosis and outperformed clinical variables.
The low-risk group showed increased immune infiltration, decreased tumor purity, higher tumor mutation burden (TMB), and varied chemosensitivity.

Conclusions

The novel GTRL prognostic model accurately predicts EC patient outcomes and can guide therapeutic strategies.
Specific GTRLs (AC090617.5 and AP001107.9) influence EC cell proliferation, migration, and chemosensitivity.
This model offers potential for personalized treatment decisions in endometrial cancer.

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