Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers
1
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
2
School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
3
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
*
Authors to whom correspondence should be addressed.
Cells 2020, 9(2), 326; https://doi.org/10.3390/cells9020326
Received: 2 January 2020
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Revised: 28 January 2020
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Accepted: 30 January 2020
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Published: 30 January 2020
(This article belongs to the Special Issue Biocomputing and Synthetic Biology in Cells)
Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.
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Keywords:
breast cancer; metastasis; invasiveness; DNA methylation
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MDPI and ACS Style
Wang, C.; Zhao, N.; Yuan, L.; Liu, X. Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers. Cells 2020, 9, 326. https://doi.org/10.3390/cells9020326
AMA Style
Wang C, Zhao N, Yuan L, Liu X. Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers. Cells. 2020; 9(2):326. https://doi.org/10.3390/cells9020326
Chicago/Turabian StyleWang, Chunyu, Ning Zhao, Linlin Yuan, and Xiaoyan Liu. 2020. "Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers" Cells 9, no. 2: 326. https://doi.org/10.3390/cells9020326
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