ArticleFrontiers in oncology2021
Identification of Tumor Microenvironment-Related Alternative Splicing Events to Predict the Prognosis of Endometrial Cancer.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The potential impact of RNA splicing abnormalities on immune regulation in endometrial cancer.Cell death & disease · 2025Review
- Immune cell infiltration as a prognostic factor in endometrial cancer: a meta-analysis.American journal of cancer research · 2025Article
- Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer.NAR genomics and bioinformatics · 2024Article
- Alternative splicing ofJournal of gynecologic oncology · 2023Article
- Comprehensive characterization of 11 prognostic alternative splicing events in ovarian cancer interacted with the immune microenvironment.Scientific reports · 2022Article
- Identification of Alternative Splicing-Related Genes CYB561 and FOLH1 in the Tumor-Immune Microenvironment for Endometrial Cancer Based on TCGA Data Analysis.Frontiers in genetics · 2022Article
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8 authors.
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Abstract
backgroundEndometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients' survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers.
methodsWe downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment.
resultsThe prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient's ESTIMATE score and the higher the infiltration of immune cells.
conclusionsWe used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC.
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