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- W4283659985 abstract "Background: Advanced and recurrent endometrial cancer EC remains controversial. Immunotherapy will play a landmark role in cancer treatment, and alternative splicing (AS) of messenger RNA (mRNA) may offer the potential of a broadened target space. Methods: We downloaded the clinical information and mRNA expression profiles from The Cancer Genome Atlas (TCGA) database. Hub genes were extracted from 11 AS-related genes to analyze the correlation between clinical parameters and the tumor-immune microenvironment. We also analyzed the correlations between the copy numbers, gene expressions of hub genes, and immune cells. The correlation between the risk score and the six most important checkpoint genes was also investigated. The ESTIMATE algorithm was finally performed on each EC sample based on the high- and low-risk groups. Results: The risk score was a reliable and stable independent risk predictor in the Uterine Corpus Endometrial Carcinoma (UCEC) cohort. CYB561|42921|AP and FOLH1|15817|ES were extracted. The expression of CYB561 and FOLH1 decreased gradually with the increased grade and International Federation of Gynecology and Obstetrics (FIGO) stage (p < 0.05). Gene copy number changes in CYB561 and FOLH1 led to the deletion number of myeloid DC cells and T cell CD8+. Low expression of both CYB561 and FOLH1 was associated with poor prognosis (p < 0.001). The checkpoint genes, CTLA-4 and PDCD1, exhibited a negative correlation with the risk score of AS in UCEC. Conclusion: AS-related gene signatures were related to the immune-tumor microenvironment and prognosis. These outcomes were significant for studying EC's immune-related mechanisms and exploring novel prognostic predictors and precise therapy methods." @default.
- W4283659985 created "2022-06-29" @default.
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- W4283659985 date "2022-06-28" @default.
- W4283659985 modified "2023-10-18" @default.
- W4283659985 title "Identification of Alternative Splicing-Related Genes CYB561 and FOLH1 in the Tumor-Immune Microenvironment for Endometrial Cancer Based on TCGA Data Analysis" @default.
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- W4283659985 doi "https://doi.org/10.3389/fgene.2022.770569" @default.
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