| Title |
Screening of biomarkers for prediction of response to and prognosis after chemotherapy for breast cancers
|
|---|---|
| Published in |
OncoTargets and therapy, May 2016
|
| DOI | 10.2147/ott.s92350 |
| Pubmed ID | |
| Authors |
Feng Bing, Yu Zhao |
| Abstract |
To screen the biomarkers having the ability to predict prognosis after chemotherapy for breast cancers. Three microarray data of breast cancer patients undergoing chemotherapy were collected from Gene Expression Omnibus database. After preprocessing, data in GSE41112 were analyzed using significance analysis of microarrays to screen the differentially expressed genes (DEGs). The DEGs were further analyzed by Differentially Coexpressed Genes and Links to construct a function module, the prognosis efficacy of which was verified by the other two datasets (GSE22226 and GSE58644) using Kaplan-Meier plots. The involved genes in function module were subjected to a univariate Cox regression analysis to confirm whether the expression of each prognostic gene was associated with survival. A total of 511 DEGs between breast cancer patients who received chemotherapy or not were obtained, consisting of 421 upregulated and 90 downregulated genes. Using the Differentially Coexpressed Genes and Links package, 1,244 differentially coexpressed genes (DCGs) were identified, among which 36 DCGs were regulated by the transcription factor complex NFY (NFYA, NFYB, NFYC). These 39 genes constructed a gene module to classify the samples in GSE22226 and GSE58644 into three subtypes and these subtypes exhibited significantly different survival rates. Furthermore, several genes of the 39 DCGs were shown to be significantly associated with good (such as CDC20) and poor (such as ARID4A) prognoses following chemotherapy. Our present study provided a serial of biomarkers for predicting the prognosis of chemotherapy or targets for development of alternative treatment (ie, CDC20 and ARID4A) in breast cancer patients. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 1 | 14% |
| Student > Bachelor | 1 | 14% |
| Researcher | 1 | 14% |
| Lecturer | 1 | 14% |
| Unknown | 3 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 2 | 29% |
| Physics and Astronomy | 1 | 14% |
| Agricultural and Biological Sciences | 1 | 14% |
| Unknown | 3 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 25 May 2016.
All research outputs
#20,110,957
of 25,584,565 outputs
Outputs from OncoTargets and therapy
#1,438
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Outputs of similar age
#217,927
of 312,290 outputs
Outputs of similar age from OncoTargets and therapy
#56
of 123 outputs
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