ArticleFrontiers in genetics2020
Novel Gene Signature Reveals Prognostic Model in Acute Myeloid Leukemia.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Identification of Prognostic-Related Genes in Acute Myeloid Leukemia: A Study Based on TCGA Data Analysis.Biochemical genetics · 2026Article
- Gene expression profiling and pathway analysis in acute myeloid leukaemia-normal karyotype patients.PloS one · 2025Article
- Caloric restriction leads to druggable LSD1-dependent cancer stem cells expansion.Nature communications · 2024Article
- A nomogram based on clinical features and molecular abnormalities for predicting the prognosis of patients with acute myeloid leukemia.Translational cancer research · 2023Article
- Prognostic value of an eighteen-genes panel in acute myeloid leukemia by analyzing TARGET and TCGA databases.Cancer biomarkers : section A of Disease markers · 2023Article
- Establishment and validation of a carbohydrate metabolism-related gene signature for prognostic model and immune response in acute myeloid leukemia.Frontiers in immunology · 2022Article
- Novel gene signature reveals prognostic model in acute lymphoblastic leukemia.Frontiers in cell and developmental biology · 2022Article
- Genetic and Epigenetic Signatures in Acute Promyelocytic Leukemia Treatment and Molecular Remission.Frontiers in genetics · 2022Article
- Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis.Frontiers in cardiovascular medicine · 2020Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute myeloid leukemia (AML) is a clonal malignant disease with poor prognosis and a low overall survival rate. Although many studies on the treatment and detection of AML have been conducted, the molecular mechanism of AML development and progression has not been fully elucidated. The present study was designed to pursuit the molecular mechanism of AML using a comprehensive bioinformatics analysis, and build an applicable model to predict the survival probability of AML patients in clinical use.
methodsTo simplify the complicated regulatory networks, we performed the gene co-expression and PPI network based on WGCNA and STRING database using modularization design. Two machine learning methods, A least absolute shrinkage and selector operation (LASSO) algorithm and support vector machine-recursive feature elimination (SVM-RFE), were used to filter the common hub genes by five-fold cross-validation. The candidate hub genes were used to build the predictive model of AML by the cox-proportional hazards analysis, and validated in The Cancer Genome Atlas (TCGA) cohort and ohsu cohort, which were reliable in the experimental verification by qRT-PCR and western blotting in mRNA and protein levels.
resultsThree hub genes, FLT3, CD177 and TTPAL were used to build a clinically applicable model to predict the survival probability of AML patients and divided them into high and low groups. To compare the survival ability of the model with the classical clinical features, we generated the nomogram. The model displayed the most risk points contrast to other clinical characteristics, which was compatible with the data of cox multivariate regression.
conclusionThis study reveal the novel molecular mechanism of AML, and construct a clinical model significantly related to AML patient prognosis. We showed the integrated roles of critical pathways, hub genes associated, which provide potential targets and new research ideas for the treatment and early detection of AML.
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