Integrated analysis of gene expression and methylation profiles of 48 candidate genes in breast cancer patients
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Gene-specific methylation and expression have shown biological and clinical importance for breast cancer diagnosis and prognosis. Integrated analysis of gene methylation and gene expression may identify genes associated with biology mechanism and clinical outcome of breast cancer and aid in clinical management.
Using high-throughput microfluidic quantitative PCR, we analyzed the expression profiles of 48 candidate genes in 96 Chinese breast cancer patients and investigated their correlation with gene methylation and associations with breast cancer clinical parameters.
Breast cancer-specific gene expression alternation was found in 25 genes with significant expression difference between paired tumor and normal tissues. A total of 9 genes (CCND2, EGFR, GSTP1, PGR, PTGS2, RECK, SOX17, TNFRSF10D, and WIF1) showed significant negative correlation between methylation and gene expression, which were validated in the TCGA database. Total 23 genes (ACADL, APC, BRCA2, CADM1, CAV1, CCND2, CST6, EGFR, ESR2, GSTP1, ICAM5, NPY, PGR, PTGS2, RECK, RUNX3, SFRP1, SOX17, SYK, TGFBR2, TNFRSF10D, WIF1, and WRN) annotated with potential TFBSs in the promoter regions showed negative correlation between methylation and expression. In logistics regression analysis, 31 of the 48 genes showed improved performance in disease prediction with combination of methylation and expression coefficient.
Our results demonstrated the complex correlation and the possible regulatory mechanisms between DNA methylation and gene expression. Integration analysis of methylation and expression of candidate genes could improve performance in breast cancer prediction. These findings would contribute to molecular characterization and identification of biomarkers for potential clinical applications.
KeywordsBreast cancer Methylation Gene expression Microfludic PCR Next-generation sequencing
This work was supported in part by grants from the Natural Science Foundation of China (No. 81272296 and 81372228), the Scientific Project of China Hunan Provincial Science and Technology Department (No. 2012SK2013), and the Major Special Projects of the Science and Technology Bureau of Changsha, China (No. K1204017-31 and K1306011-31). Zibo Li was supported by the Hunan Province Postgraduate Student Scientific Innovation Project, China (No. CX2013B087).
Compliance with ethical standards
Conflict of interest
Xingwu Guo, Limin Peng, Ming Chen, Zhongping Deng, and Lizhong Dai are employees of Sanway Gene Technology Inc.
We declare that the experiments performed in this study comply with the current laws of the People’s Republic of China.
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