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pmid:34022936
Accelerated aging in normal breast tissue of women with breast cancer.
Shoghag Panjarian · Jozef Madzo · Kelsey Keith · Carolyn M Slater · Carmen Sapienza · Jaroslav Jelinek · Jean-Pierre J Issa
Breast cancer research : BCR · 2021 doi:10.1186/s13058-021-01434-7
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Experiment
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Data
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Analysis
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Execution
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In this study, we investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. Outlier phenotype is identified by unsupervised anomaly detection algorithms and is defined by individuals who have normal tissue age-dependent DNA methylation levels that vary dramatically from the population mean.\nMETHODS: We generated whole-genome DNA methylation profiles (GSE160233) on purified epithelial cells and used publicly available Infinium HumanMethylation 450K array datasets (TCGA, GSE88883, GSE69914, GSE101961, and GSE74214) for discovery and validation.\nRESULTS: We found that hypermethylation in normal breast tissue is the best predictor of hypermethylation in cancer. Using unsupervised anomaly detection approaches, we found that about 10% of the individuals (39/427) were outliers for DNA methylation from 6 DNA methylation datasets. We also found that there were significantly more outlier samples in normal-adjacent to cancer (24/139, 17.3%) than in normal samples (15/228, 5.2%). Additionally, we found significant differences between the predicted ages based on DNA methylation and the chronological ages among outliers and not-outliers. Additionally, we found that accelerated outliers (older predicted age) were more frequent in normal-adjacent to cancer (14/17, 82%) compared to normal samples from individuals without cancer (3/17, 18%). Furthermore, in matched samples, we found that the epigenome of the outliers in the pre-malignant tissue was as severely altered as in cancer.\nCONCLUSIONS: A subset of patients with breast cancer has severely altered epigenomes which are characterized by accelerated aging in their normal-appearing tissue. In the future, these DNA methylation sites should be studied further such as in cell-free DNA to determine their potential use as biomarkers for early detection of malignant transformation and preventive intervention in breast cancer.", "claims": [{"id": "claim-method", "statement": "Next, we calculated an unsupervised anomaly detection parameter, the local outlier factor (LOF) on all the principal components of PCA using the DMwR and Rlof packages in R [29, 30].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34022936", "locator": "sections[7] \"Identification of outlier samples and age prediction\" paragraph 2", "quote": "Next, we calculated an unsupervised anomaly detection parameter, the local outlier factor (LOF) on all the principal components of PCA using the DMwR and Rlof packages in R [29, 30]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The dataset generated and analyzed during the current study is available from the corresponding author on request. 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