ArticleDisease markers2022
Five EMT-Related Gene Signatures Predict Acute Myeloid Leukemia Patient Outcome.
Article in Disease markers, 2022. 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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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.
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Who cites it
6 citing papers in PubMed.
- Hierarchical lineage tracing reveals diverse pathways of cytarabine resistance.Nature communications · 2026Article
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- Hierarchical Lineage Tracing Reveals Diverse Pathways of AML Treatment Resistance.bioRxiv : the preprint server for biology · 2025Article
- Marine derived macrolide bryostatin 4 inhibits the TGF-β signaling pathway against acute erythroleukemia.Cellular oncology (Dordrecht, Netherlands) · 2024Article
- Epithelial-Mesenchymal Transition in Acute Leukemias.International journal of molecular sciences · 2024Review
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Authors and funding
14 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: The epithelial mesenchymal transition (EMT) gene has been shown to be significantly associated with the prognosis of solid tumors; however, there is a lack of models for the EMT gene to predict the prognosis of AML patients. Methods: First, we downloaded clinical data and raw transcriptome sequencing data from the TCGA database of acute myeloid leukemia (AML) patients. All currently confirmed EMT-related genes were obtained from the dbEMT 2.0 database, and 30% of the TCGA data were randomly selected as the test set. Univariate Cox regression analysis, random forest, and lasso regression were used to optimize the number of genes for model construction, and multivariate Cox regression was used for model construction. Area under the ROC curve was used to assess the efficacy of the model application, and the internal validation set was used to assess the stability of the model. Results: A total of 173 AML samples were downloaded, and a total of 1184 EMT-related genes were downloaded. The results of univariate batch Cox regression analysis suggested that 212 genes were associated with patient prognosis, random forest and lasso regression yielded 18 and 8 prognosis-related EMT genes, respectively, and the results of multifactorial COX regression model suggested that 5 genes, CBR1, HS3ST3B1, LIMA1, MIR573, and PTP4A3, were considered as independent risk factors affecting patient prognosis. The model ROC results suggested that the area under the curve was 0.868 and the internal validation results showed that the area under the curve was 0.815. Conclusion: During this study, we constructed a signature model of five EMT-related genes to predict overall survival in patients with AML; it will provide a useful tool for clinical decision making.
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