https://scholars.lib.ntu.edu.tw/handle/123456789/507741
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Huang, C.-C. | en_US |
dc.contributor.author | Tu, S.-H. | en_US |
dc.contributor.author | Lien, H.-H. | en_US |
dc.contributor.author | Huang, C.-S. | en_US |
dc.contributor.author | Huang, C.-J. | en_US |
dc.contributor.author | LIANG-CHUAN LAI | en_US |
dc.contributor.author | Tsai, M.-H. | en_US |
dc.contributor.author | MONG-HSUN TSAI | en_US |
dc.contributor.author | ERIC YAO-YU CHUANG | en_US |
dc.creator | Huang C.-C.;Tu S.-H.;Lien H.-H.;Huang C.-S.;Huang C.-J.;Liang-Chuan Lai;Tsai M.-H.;Chuang E.Y. | - |
dc.date.accessioned | 2020-07-01T08:12:23Z | - |
dc.date.available | 2020-07-01T08:12:23Z | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 0167-6806 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027957781&doi=10.1007%2fs10549-014-3104-6&partnerID=40&md5=6291ba03dcfa3022da05d0ab3e3ae397 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/507741 | - |
dc.description.abstract | Several prognostic signatures have been identified for breast cancer. However, these signatures vary extensively in their gene compositions, and the poor concordance of the risk groups defined by the prognostic signatures hinders their clinical applicability. Breast cancer risk prediction was refined with a novel approach to finding concordant genes from leading edge analysis of prognostic signatures. Each signature was split into two gene sets, which contained either up-regulated or down-regulated genes, and leading edge analysis was performed within each array study for all up-/down-regulated gene sets of the same signature from all training datasets. Consensus of leading edge subsets among all training microarrays was used to synthesize a predictive model, which was then tested in independent studies by partial least squares regression. Only a small portion of six prognostic signatures (Amsterdam, Rotterdam, Genomic Grade Index, Recurrence Score, and Hu306 and PAM50 of intrinsic subtypes) was significantly enriched in the leading edge analysis in five training datasets (n?=?2,380), and that the concordant leading edge subsets (43 genes) could identify the core signature genes that account for the enrichment signals providing prognostic power across all assayed samples. The proposed concordant leading edge algorithm was able to discriminate high-risk from low-risk patients in terms of relapse-free or distant metastasis-free survival in all training samples (hazard ratios: 1.84–2.20) and in three out of four independent studies (hazard ratios: 3.91–8.31). In some studies, the concordant leading edge subset remained a significant prognostic factor independent of clinical ER, HER2, and lymph node status. The present study provides a statistical framework for identifying core consensus across microarray studies with leading edge analysis, and a breast cancer risk predictive model was established. ? 2014, Springer Science+Business Media New York. | - |
dc.publisher | Springer New York LLC | - |
dc.relation.ispartof | Breast Cancer Research and Treatment | - |
dc.subject.classification | [SDGs]SDG3 | - |
dc.subject.other | Article; breast cancer; cancer prognosis; cancer risk; down regulation; gene expression; gene sequence; gene signature; genetic analysis; human; leading edge analysis; microarray analysis; priority journal; risk assessment; upregulation | - |
dc.title | Refinement of breast cancer risk prediction with concordant leading edge subsets from prognostic gene signatures | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1007/s10549-014-3104-6 | - |
dc.identifier.pmid | 25158930 | - |
dc.identifier.scopus | 2-s2.0-85027957781 | - |
dc.relation.pages | 353-370 | - |
dc.relation.journalvolume | 147 | - |
dc.relation.journalissue | 2 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | journal article | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
crisitem.author.dept | Physiology | - |
crisitem.author.dept | Biotechnology | - |
crisitem.author.dept | Center for Biotechnology | - |
crisitem.author.dept | Genome and Systems Biology Degree Program | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Biomedical Electronics and Bioinformatics | - |
crisitem.author.dept | Center for Biotechnology | - |
crisitem.author.dept | Genome and Systems Biology Degree Program | - |
crisitem.author.orcid | 0000-0002-3913-5338 | - |
crisitem.author.orcid | 0000-0001-8777-5818 | - |
crisitem.author.orcid | 0000-0003-2530-0096 | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | College of Bioresources and Agriculture | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | College of Life Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | College of Life Science | - |
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