ArticleCancer biomarkers : section A of Disease markers2023
Prognostic value of an eighteen-genes panel in acute myeloid leukemia by analyzing TARGET and TCGA databases.
Article in Cancer biomarkers : section A of Disease markers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identification of Prognostic-Related Genes in Acute Myeloid Leukemia: A Study Based on TCGA Data Analysis.Biochemical genetics · 2026Article
- LncRNA SNHG11 expression in acute myeloid leukemia patients and its relationship with the biology of acute myeloid leukemia cells.Clinical and experimental medicine · 2025Article
- Development and validation of a leukemia prognostic model through single-cell RNA sequencing and machine learning approaches.Discover oncology · 2025Article
- Comprehensive analysis of thirteen-gene panel with prognosis value in Multiple Myeloma.Cancer biomarkers : section A of Disease markers · 2023Article
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11 authors.
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Abstract
backgroundAcute myeloid leukemia (AML) has a poor prognosis, and the current 5-year survival rate is less than 30%.
objectiveThe present study was designed to identify the significant genes closely related to AML prognosis and predict the prognostic value by constructing a risk model based on their expression.
methodsUsing bioinformatics (Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, univariate and multivariate Cox regression analysis, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) analysis) to identify a prognostic gene signature for AML. Finally, The Cancer Genome Atlas (TCGA) database was used to validate this prognostic signature.
resultsBased on univariate and multivariate Cox regression analysis, eighteen prognostic genes were identified, and the gene signature and risk score model were constructed. Multivariate Cox analysis showed that the risk score was an independent prognostic factor [hazard ratio (HR) = 1.122, 95% confidence interval (CI) = 1.067-1.180, P< 0.001]. ROC analysis showed a high predictive value of the risk model with an area under the curve (AUC) of 0.705.
conclusionsThis study evaluated a potential prognostic signature with eighteen genes and constructed a risk model significantly related to the prognosis of AML patients.
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