ArticleBMC bioinformatics2015
Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Dynamic feedback control of oncogenic tyrosine kinase signaling in acute leukemia.Science signaling · 2026Article
- Succinate receptor 1 restricts hematopoiesis and prevents acute myeloid leukemia progression.Nature communications · 2026Article
- Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees.Heliyon · 2023Article
- Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelligence · 2023Article
- Endogenous IL-1 receptor antagonist restricts healthy and malignant myeloproliferation.Nature communications · 2023Article
- Linear programming based computational technique for leukemia classification using gene expression profile.PloS one · 2023Article
- High-dimensional genomic feature selection with the ordered stereotype logit model.Briefings in bioinformatics · 2022Review
- Tumor necrosis factor‑related apoptosis‑inducing ligand is a novel transcriptional target of runt‑related transcription factor 1.International journal of oncology · 2022Article
- DNA methylation subtypes for ovarian cancer prognosis.FEBS open bio · 2021Article
- Article
- C/EBPα and GATA-2 Mutations Induce Bilineage Acute Erythroid Leukemia through Transformation of a Neomorphic Neutrophil-Erythroid Progenitor.Cancer cell · 2020Article
- Integrative analysis of DNA methylation and gene expression in papillary renal cell carcinoma.Molecular genetics and genomics : MGG · 2020Article
- A gut microbiota score predicting acute graft-versus-host disease following myeloablative allogeneic hematopoietic stem cell transplantation.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2020Article
- Computational Identification of Tumor Suppressor Genes Based on Gene Expression Profiles in Normal and Cancerous Gastrointestinal Tissues.Journal of oncology · 2020Article
- Prognostic role of ACTL10 in Cytogenetic Normal Acute Myeloid Leukemia.Journal of Cancer · 2020Article
- A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.BMC medical genomics · 2019Article
- Data integration by multi-tuning parameter elastic net regression.BMC bioinformatics · 2018Article
- Genomic Amplifications and Distal 6q Loss: Novel Markers for Poor Survival in High-risk Neuroblastoma Patients.Journal of the National Cancer Institute · 2018Article
- A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.BMC bioinformatics · 2017Article
- Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia.Journal of hematology & oncology · 2017Review
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4 authors.
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Abstract
backgroundAcute Myeloid Leukemia (AML) is characterized by various cytogenetic and molecular abnormalities. Detection of these abnormalities is important in the risk-classification of patients but requires laborious experimentation. Various studies showed that gene expression profiles (GEP), and the gene signatures derived from GEP, can be used for the prediction of subtypes in AML. Similarly, successful prediction was also achieved by exploiting DNA-methylation profiles (DMP). There are, however, no studies that compared classification accuracy and performance between GEP and DMP, neither are there studies that integrated both types of data to determine whether predictive power can be improved. APPROACH: Here, we used 344 well-characterized AML samples for which both gene expression and DNA-methylation profiles are available. We created three different classification strategies including early, late and no integration of these datasets and used them to predict AML subtypes using a logistic regression model with Lasso regularization.
resultsWe illustrate that both gene expression and DNA-methylation profiles contain distinct patterns that contribute to discriminating AML subtypes and that an integration strategy can exploit these patterns to achieve synergy between both data types. We show that concatenation of features from both data sets, i.e. early integration, improves the predictive power compared to classifiers trained on GEP or DMP alone. A more sophisticated strategy, i.e. the late integration strategy, employs a two-layer classifier which outperforms the early integration strategy.
conclusionWe demonstrate that prediction of known cytogenetic and molecular abnormalities in AML can be further improved by integrating GEP and DMP profiles.
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