Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Endometrial cancer (UCEC) is one of the most common malignant tumors in gynecology, and early diagnosis is crucial for its treatment. Currently, there is a lack of early screening tests specific to UCEC, and treatment advances are limited. It is crucial to identify more sensitive biomarkers for screening, diagnosis, and predicting UCEC. Previous studies have shown that UBE2T is involved in the development of various tumors such as breast cancer and liver cancer, but research on the role of UBE2T in UCEC is limited. METHODS: Using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UALCAN databases, we analyzed the differential expression of UBE2T mRNA and protein in endometrial cancer (UCEC), along with its clinical relevance. A total of 113 clinical samples were collected, and immunohistochemistry and Western blot analysis were employed to validate bioinformatics analysis results. Volcano plots were generated using UBE2T and its differentially expressed genes, and a protein-protein interaction (PPI) network was constructed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and immune infiltration analysis were used to predict the functional role of UBE2T in UCEC progression. Correlation between UBE2T expression and patient survival was analyzed using TCGA data, and Kaplan-Meier survival curves were plotted. RESULTS: UBE2T is significantly overexpressed in UCEC and correlates with poor prognosis. Its overexpression is closely associated with mitosis, cell cycle regulation, and histological grade in UCEC patients. CONCLUSION: UBE2T is highly expressed in UCEC and suppresses anti-tumor immune responses in UCEC patients. It serves as a key participant in UCEC progression, associated with a range of adverse outcomes, and holds potential as a clinical diagnostic and prognostic biomarker.
Authors: Longyun Wang, Mengqi Wang, Zeyu Wang, Kai Wang, Bowei Zhao, Yue Wang, Jingying Zheng, Shuang Zhang
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,490
Award: G4813902573
OpenAlex ID: W4403148242