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Updated: Jun 9, 2025
A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants
Published on: October 10, 2022
Unraveling the biological functions of UCEC: Insights from a prognostic signature model
Qi Zhu1, Wulin Shan1, Xiaoyu Li1
1The First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui 230031, China.
Background:
Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecological tumor with a bleak prognosis. Anomalous glycosylation plays a pivotal role in tumorigenesis. Currently, there is a lack of prognostic signatures based on glycosylation-related genes for UCEC. Thus, our research aims to construct a predictive model and validate the correlation between relevant genes and biological functions.
Methods:
Using the TCGA database, we developed prognostic models and explored their relationships with survival outcomes. We further selected key genes to verify their expression in tissues and assess their impact on cellular behavior.
Results:
The clinical prognosis of the high-risk group was significantly worse than that of the low-risk group. The nomogram model accurately predicted UCEC patient prognosis. Additionally, we identified OLFML1 as a unique signature gene that can inhibit UCEC progression and reduce radiation resistance in vitro.
Conclusions:
Our model, which is based on glycosylation-related genes in UCEC, effectively identifies high-risk patients and provides valuable prognostic information. In addition, OLFML1 acts as a tumor suppressor in UCEC and enhances radiosensitivity, suggesting a new potential target for improving therapeutic efficacy.
