ArticleGenes2022
Identification of a Novel Epithelial-Mesenchymal Transition-Related Gene Signature for Endometrial Carcinoma Prognosis.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Recent advances in characterizing the immune microenvironment and biomarkers of endometrial carcinoma.Frontiers in immunology · 2026Review
- Expression of E-Cadherin and N-Cadherin in the Endocervix as a Predictive Factor in Patients with Endometrial Cancer.International journal of molecular sciences · 2024Article
- An EMT-based gene signature enhances the clinical understanding and prognostic prediction of patients with ovarian cancers.Journal of ovarian research · 2023Article
- Article
- Comprehensive Analysis of DNA Methylation and Transcriptome to Identify PD-1-Negative Prognostic Methylated Signature in Endometrial Carcinoma.Disease markers · 2022Article
- Construction of an Epithelial-Mesenchymal Transition-Related Model for Clear Cell Renal Cell Carcinoma Prognosis Prediction.Disease markers · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database and the Gene Expression Omnibus databases. To predict patients' overall survival, a 9 EMT-related genes prognosis risk model was built by machine learning algorithm and multivariate Cox regression. Expressions of nine genes were verified by RT-qPCR. Responses to immune checkpoint blockades therapy and drug sensitivity were separately evaluated in different group of patients with the risk model. (3) Endometrial carcinoma patients were assigned to the high- and low-risk groups according to the signature, and poorer overall survival and disease-free survival were showed in the high-risk group. This EMT-related gene signature was also significantly correlated with tumor purity and immune cell infiltration. In addition, eight chemical compounds, which may benefit the high-risk group, were screened out. (4) Conclusions: We identified a novel EMT-related gene signature for predicting the prognosis of EC patients. Our findings provide potential therapeutic targets and compounds for personalized treatment. This may facilitate decision making during endometrial carcinoma treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.