| Title |
Identification of novel therapeutic target genes in acquired lapatinib-resistant breast cancer by integrative meta-analysis
|
|---|---|
| Published in |
Tumor Biology, September 2015
|
| DOI | 10.1007/s13277-015-4033-7 |
| Pubmed ID | |
| Authors |
Young Seok Lee, Sun Goo Hwang, Jin Ki Kim, Tae Hwan Park, Young Rae Kim, Ho Sung Myeong, Jong Duck Choi, Kang Kwon, Cheol Seong Jang, Young Tae Ro, Yun Hee Noh, Sung Young Kim |
| Abstract |
Acquired resistance to lapatinib is a highly problematic clinical barrier that has to be overcome for a successful cancer treatment. Despite efforts to determine the mechanisms underlying acquired lapatinib resistance (ALR), no definitive genetic factors have been reported to be solely responsible for the acquired resistance in breast cancer. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets related to breast cancer with ALR, using the R-based RankProd package. From the meta-analysis, we were able to identify a total of 990 differentially expressed genes (DEGs, 406 upregulated, 584 downregulated) that are potentially associated with ALR. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs showed that "response to organic substance" and "p53 signaling pathway" may be largely involved in ALR process. Of these, many of the top 50 upregulated and downregulated DEGs were found in oncogenesis of various tumors and cancers. For the top 50 DEGs, we constructed the gene coexpression and protein-protein interaction networks from a huge database of well-known molecular interactions. By integrative analysis of two systemic networks, we condensed the total number of DEGs to six common genes (LGALS1, PRSS23, PTRF, FHL2, TOB1, and SOCS2). Furthermore, these genes were confirmed in functional module eigens obtained from the weighted gene correlation network analysis of total DEGs in the microarray datasets ("GSE16179" and "GSE52707"). Our integrative meta-analysis could provide a comprehensive perspective into complex mechanisms underlying ALR in breast cancer and a theoretical support for further chemotherapeutic studies. |
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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 41 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 17% |
| Student > Ph. D. Student | 6 | 15% |
| Student > Master | 5 | 12% |
| Student > Bachelor | 4 | 10% |
| Professor | 3 | 7% |
| Other | 9 | 22% |
| Unknown | 7 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 27% |
| Medicine and Dentistry | 6 | 15% |
| Agricultural and Biological Sciences | 5 | 12% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 7% |
| Nursing and Health Professions | 2 | 5% |
| Other | 5 | 12% |
| Unknown | 9 | 22% |
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 13 September 2015.
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#18,426,826
of 22,828,180 outputs
Outputs from Tumor Biology
#1,369
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Outputs of similar age
#192,904
of 267,781 outputs
Outputs of similar age from Tumor Biology
#101
of 234 outputs
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