Methylation profiling of 48 candidate genes in tumor and matched normal tissues from breast cancer patients

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• 作者：Zibo Li ; Xinwu Guo ; Yepeng Wu ; Shengyun Li…
• 关键词：Breast cancer ; Candidate gene ; Methylation ; Microfluidic PCR ; Next ; generation sequencing ; Biomarker
• 刊名：Breast Cancer Research and Treatment
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：149
• 期：3
• 页码：767-779
• 全文大小：868 KB
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• 作者单位：Zibo Li (1)
  Xinwu Guo (2)
  Yepeng Wu (1)
  Shengyun Li (3)
  Jinhua Yan (1)
  Limin Peng (2)
  Zhi Xiao (3)
  Shouman Wang (3)
  Zhongping Deng (2)
  Lizhong Dai (2)
  Wenjun Yi (4)
  Kun Xia (1)
  Lili Tang (3)
  Jun Wang (1)
  
  1. The State Key Laboratory of Medical Genetics & School of Life Sciences, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, People’s Republic of China
  2. Sanway Gene Technology Inc., Changsha, 410205, Hunan, People’s Republic of China
  3. Department of Breast Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, People’s Republic of China
  4. Department of Breast and Thyroid Surgery, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, People’s Republic of China
  
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Oncology
  
• 出版者：Springer Netherlands
• ISSN：1573-7217

文摘

Gene-specific methylation alterations in breast cancer have been suggested to occur early in tumorigenesis and have the potential to be used for early detection and prevention. The continuous increase in worldwide breast cancer incidences emphasizes the urgent need for identification of methylation biomarkers for early cancer detection and patient stratification. Using microfluidic PCR-based target enrichment and next-generation bisulfite sequencing technology, we analyzed methylation status of 48 candidate genes in paired tumor and normal tissues from 180 Chinese breast cancer patients. Analysis of the sequencing results showed 37 genes differentially methylated between tumor and matched normal tissues. Breast cancer samples with different clinicopathologic characteristics demonstrated distinct profiles of gene methylation. The methylation levels were significantly different between breast cancer subtypes, with basal-like and luminal B tumors having the lowest and the highest methylation levels, respectively. Six genes (ACADL, ADAMTSL1, CAV1, NPY, PTGS2, and RUNX3) showed significant differential methylation among the 4 breast cancer subtypes and also between the ER?+/ER- tumors. Using unsupervised hierarchical clustering analysis, we identified a panel of 13 hypermethylated genes as candidate biomarkers that performed a high level of efficiency for cancer prediction. These 13 genes included CST6, DBC1, EGFR, GREM1, GSTP1, IGFBP3, PDGFRB, PPM1E, SFRP1, SFRP2, SOX17, TNFRSF10D, and WRN. Our results provide evidence that well-defined DNA methylation profiles enable breast cancer prediction and patient stratification. The novel gene panel might be a valuable biomarker for early detection of breast cancer.