| Title |
Discovering gene expression signatures responding to tyrosine kinase inhibitor treatment in chronic myeloid leukemia
|
|---|---|
| Published in |
BMC Medical Genomics, August 2016
|
| DOI | 10.1186/s12920-016-0194-5 |
| Pubmed ID | |
| Authors |
Kihoon Cha, Yi Li, Gwan-Su Yi |
| Abstract |
Tyrosine 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. We 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. We 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. In 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 24 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 25% |
| Student > Postgraduate | 3 | 13% |
| Student > Master | 3 | 13% |
| Student > Ph. D. Student | 2 | 8% |
| Student > Bachelor | 2 | 8% |
| Other | 3 | 13% |
| Unknown | 5 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 29% |
| Biochemistry, Genetics and Molecular Biology | 5 | 21% |
| Agricultural and Biological Sciences | 2 | 8% |
| Immunology and Microbiology | 2 | 8% |
| Psychology | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 6 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 February 2019.
All research outputs
#6,979,775
of 22,884,315 outputs
Outputs from BMC Medical Genomics
#324
of 1,224 outputs
Outputs of similar age
#115,794
of 355,874 outputs
Outputs of similar age from BMC Medical Genomics
#8
of 19 outputs
Altmetric has tracked 22,884,315 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,224 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 72% of its peers.
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We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.