ArticleOncoTargets and therapy2019
A six-gene-based prognostic model predicts complete remission and overall survival in childhood acute myeloid leukemia.
Article in OncoTargets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 20 citations in OpenAlex.
- Jab1 promotes immune evasion and progression in acute myeloid leukemia models under oxidative stress.The Journal of clinical investigation · 2025Article
- Single-cell sequencing reveals the expansion and diversity of T cell subsets in the bone marrow microenvironment of chronic myeloid leukemia.Genes & diseases · 2025Article
- Identification of the Shared Gene Signatures of HCK, NOG, RNF125 and Biological Mechanism in Pediatric Acute Lymphoblastic Leukaemia and Pediatric Sepsis.Molecular biotechnology · 2025Article
- A Novel Lysosome-Related Gene Signature Predicts Lung Cancer Prognosis: A Bioinformatics-Driven Study.Health science reports · 2024Article
- Higher expression of programmed cell death 4 (PDCD4) in acute myeloid leukemia is associated with better prognosis after chemotherapy.Annals of hematology · 2023Article
- Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.Nature biomedical engineering · 2023Article
- Serum proteomics screening intercellular adhesion molecule-2 improves intermediate-risk stratification in acute myeloid leukemia.Therapeutic advances in hematology · 2022Article
- A Prognostic Model of Seven Immune Genes to Predict Overall Survival in Childhood Acute Myeloid Leukemia.BioMed research international · 2022Article
- CACNA1C is a prognostic predictor for patients with ovarian cancer.Journal of ovarian research · 2021Article
- Normal tissue adjacent to tumor expression profile analysis developed and validated a prognostic model based on Hippo-related genes in hepatocellular carcinoma.Cancer medicine · 2021Article
- Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis.Frontiers in genetics · 2021Article
- Investigation of the underlying genes and mechanism of familial hypercholesterolemia through bioinformatics analysis.BMC cardiovascular disorders · 2020Article
- Gene Panel of Persister Cells as a Prognostic Indicator for Tumor Repopulation After Radiation.Frontiers in oncology · 2020Article
- Article
- Alternative donor peripheral blood stem cell transplantation for the treatment of high-risk refractory and/or relapsed childhood acute leukemia: a randomized trial.Experimental hematology & oncology · 2020Article
- Overexpression of annexin A5 might guide the gemtuzumab ozogamicin treatment choice in patients with pediatric acute myeloid leukemia.Therapeutic advances in medical oncology · 2020Article
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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
objectiveAcute myeloid leukemia (AML) is a malignant clonal disorder. Despite enormous progress in its diagnosis and treatment, the mortality rate of AML remains high. The aim of this study was to identify prognostic biomarkers by using the gene expression profile dataset from public database, and to improve the risk-stratification criteria of survival for patients with AML. MATERIALS AND
methodsThe gene expression data and clinical parameter were acquired from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. A total of 856 differentially expressed genes (DEGs) were obtained from the childhood AML patients classified into first complete remission (CR1) group (n=791) and not CR group (n=249). We performed a series of bioinformatics analysis to screen key genes and pathways, further comprehending these DEGs through Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.
resultsSix genes (
conclusionWe identified a six-gene prognostic signature for risk-stratifying in patients with childhood AML. The risk classification model can be used to predict CR markers and may assist clinicians in providing realize the individualized treatment in this patient population.
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