Concurrent Gene Signatures for Han Chinese Breast Cancers
Journal
PLoS ONE
Journal Volume
8
Journal Issue
10
Pages
e76421
Date Issued
2013
Author(s)
Huang, C.-C.
Tu, S.-H.
Lien, H.-H.
Jeng, J.-Y.
Huang, C.-S.
Huang, C.-J.
Abstract
The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers.We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival.Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. Prognostic comparisons with published gene expression signatures showed a better discerning ability of concurrent genes, many of which were rarely identifiable if expression data were pre-selected by phenotype correlations or variability of individual genes.We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer. ? 2013 Huang et al.
SDGs
Other Subjects
epidermal growth factor receptor 2; estrogen receptor; aged; article; breast cancer; breast carcinogenesis; cancer mortality; cancer patient; cancer prognosis; cancer recurrence; cancer risk; CAPZA1 gene; chromosomal localization; COL20A1 gene; comparative genomic hybridization; controlled study; CSDE1 gene; CSNK1E gene; DENND2D gene; discriminant analysis; disease free survival; ER gene; ethnic group; female; gene; gene expression; gene loss; gene signature; genetic correlation; genetic gain; genetic risk; genetic variability; GPR18 gene; Han Chinese; HBXIP gene; health status; HER2 gene; high risk population; human; human tissue; IKZF1 gene; low risk population; major clinical study; MCOLN2 gene; metastasis; microarray analysis; MRPL20 gene; nucleotide sequence; phenotype; prediction; principal component analysis; RCAN3 gene; recurrence free survival; RWDD3 gene; scoring system; SCRN1 gene; Taiwanese; TRIM45 gene; WARS2 gene; ZMYM6 gene; Aged; Asian Continental Ancestry Group; Breast Neoplasms; China; Comparative Genomic Hybridization; DNA Copy Number Variations; Female; Gene Expression Profiling; Humans; Middle Aged; Neoplasm Grading; Neoplasm Metastasis; Prognosis; Receptor, erbB-2; Receptors, Estrogen; Transcriptome
Type
journal article