ArticleMolecular and clinical oncology2020
Gene coexpression network analysis revealed biomarkers correlated with blast cells and survival in acute myeloid leukemia.
Article in Molecular and clinical oncology, 2020. 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.
- Discovery of BEND4 as a novel single-gene prognostic marker and therapeutic target for adverse AML.NPJ precision oncology · 2026Article
- Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis.Disease markers · 2022Article
- In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia.International journal of molecular sciences · 2021Article
- LMTK2 as Potential Biomarker for Stratification between Clinically Insignificant and Clinically Significant Prostate Cancer.Journal of oncology · 2021Article
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6 authors.
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Abstract
Acute myeloid leukemia (AML) is a hematological malignancy with a poorly understood pathogenesis, especially among patients with no known cytogenetic abnormalities. Furthermore, there is a lack of therapeutic gene targets and diagnostic biomarkers for the effective treatment of AML. The present study aimed to identify candidate biomarkers correlated with the clinical prognosis of patients with AML. Leukemic cells from 5 patients with AML exhibiting a normal karyotype, and hematopoietic cells from 5 healthy donors were processed for RNA sequencing (RNA-seq), and the obtained RNA expression profiles were subjected to weighted gene correlation network analysis. A novel group of genes (the red module) were identified to be significantly associated with AML, and this module contained a closely connected network with 147 nodes, which corresponded to 114 mRNAs. Analysis of the correlation between these mRNAs and blast cell percentage, overall survival (OS) and disease-free survival (DFS) using cases from The Cancer Genome Atlas (TCGA) database revealed that
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