ArticleBMC medical genomics2016
Discovering gene expression signatures responding to tyrosine kinase inhibitor treatment in chronic myeloid leukemia.
Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Non-coding RNAs: Emerging contributors to chemoresistance in chronic myeloid leukemia.Leukemia research reports · 2025Review
- Pre-Clinical Evaluation of the Hypomethylating Agent Decitabine for the Treatment of T-Cell Lymphoblastic Lymphoma.Cancers · 2023Article
- Comparative Gene Expression Analysis Reveals Similarities and Differences of Chronic Myeloid Leukemia Phases.Cancers · 2022Article
- Quantitative proteomics on the cerebrospinal fluid of hydrocephalus in neonatal bacterial meningitis.Frontiers in pediatrics · 2022Article
- The WNT Pathway Is Relevant for the BCR-ABL1-Independent Resistance in Chronic Myeloid Leukemia.Frontiers in oncology · 2019Article
- Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level.PloS one · 2019Article
- Global gene expression profiles of hematopoietic stem and progenitor cells from patients with chronic myeloid leukemia: the effect of in vitro culture with or without imatinib.Cancer medicine · 2017Article
- Differential Expression of Circadian Genes in Leukemia and a Possible Role for Sirt1 in Restoring the Circadian Clock in Chronic Myeloid Leukemia.Journal of circadian rhythms · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTyrosine kinase inhibitor (TKI)-based therapy is a recommended treatment for patients with chronic myeloid leukemia (CML). However, a considerable group of CML patients do not respond well to the TKI therapy. Challenging to overcome this problem, we tried to discover molecular signatures in gene expression profiles to discriminate the responders and non-responders of TKI therapy.
methodsWe collected three microarray datasets of CML patients having total 73 responders and 38 non-responders. Statistical analysis was performed to identify differentially expressed genes (DEGs) as gene signature candidates from integrated microarray datasets. The classification performance of these genes and further selected discriminator gene sets was tested by using random forest and iterative backward variable selection methods.
resultsWe identified a set of genes including CTBP2, NADK, AZU1, CTSH, FSTL1, and HDLBP showing the highest accuracy more than 69.44 % to classify TKI response in CML patients. Interestingly, four genes of them are on the signaling pathway of cell proliferation. This set of genes showed much higher performance than the average performance of other genes in downstream signaling of TKI target, BCR-ABL.
conclusionsIn this study, we could find a set of potential companion diagnostic markers for TKI treatment and, at the same time, the potential of gene expression analysis to enhance the coverage of companion diagnostics.
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