Exploring and exploiting the aberrant DNA methylation profile of endocrine-resistant breast cancer

Epigenomics ◽  
2013 ◽  
Vol 5 (6) ◽  
pp. 595-598 ◽  
Author(s):  
Andrew Stone ◽  
Fatima Valdes-Mora ◽  
Susan J Clark
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Methylation Profile ◽  
Dna Methylation Profile ◽  
Aberrant Dna Methylation

Aberrant DNA methylation profile of hepatitis B virus infection

2018 ◽  
Vol 91 (1) ◽  
pp. 81-92 ◽  
Author(s):  
Jinglan Jin ◽  
Hongqin Xu ◽  
Ruihong Wu ◽  
Junqi Niu ◽  
Shibo Li
Keyword(s):  
Dna Methylation ◽  
Hepatitis B Virus ◽  
Virus Infection ◽  
Hepatitis B ◽  
Methylation Profile ◽  
Hepatitis B Virus Infection ◽  
B Virus ◽  
Dna Methylation Profile ◽  
Aberrant Dna Methylation

Aberrant DNA methylation profile and frequent methylation ofKLK10andOXGR1genes in hepatocellular carcinoma

2009 ◽  
Vol 48 (12) ◽  
pp. 1057-1068 ◽  
Author(s):  
Chang-Yi Lu ◽  
Sen-Yung Hsieh ◽  
Yen-Jung Lu ◽  
Chi-Sheng Wu ◽  
Lih-Chyang Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Methylation Profile ◽  
Dna Methylation Profile ◽  
Aberrant Dna Methylation

scholarly journals Aberrant DNA methylation profile of hepatocellular carcinoma and surgically resected margin

2009 ◽  
Vol 100 (6) ◽  
pp. 996-1004 ◽  
Author(s):  
Cheng Lou ◽  
Zhi Du ◽  
Bin Yang ◽  
YingTang Gao ◽  
YiJun Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Methylation Profile ◽  
Dna Methylation Profile ◽  
Aberrant Dna Methylation ◽  
Resected Margin

DNA methylation profile in unknown cancer origin identification: A multi-class classification model.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13554-e13554
Author(s):  
Jianing Yu ◽  
Yuanyuan Hong ◽  
Pei Zhihua ◽  
Tiancheng Han ◽  
Song Xiaofeng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Colorectal Cancer ◽  
Dna Methylation ◽  
Methylation Profile ◽  
Classification Model ◽  
Plasma Samples ◽  
Dna Methylation Profile ◽  
Tumor Origin ◽  
Multi Class Classification ◽  
Origin Identification

e13554 Background: Approximately 3-5% of human cancers are cancer of unknown primary (CUP). Treatment of a cancer patient is largely dependent on the tumor origin. Therefore, identification of the tumor origin can improve the survival of patients with CUP. We developed a multi-class classification model using DNA methylation profile as biomarker to determine the primary site of CUP. Methods: We split 7,082 primary tumor samples of 19 cancers and 679 normal samples of 15 tissues from TCGA into a 75% training set and a 25% testing set to develop the classification model. We started with multiple support vector machine (SVM) models, and then combined them into an optimal multi-class ensemble model. Predictors included tumor-specific markers and tissue-specific markers, which were filtered by comparing between groups. Only the training samples were used for feature selection and model development. A validation dataset consisting of 150 primary tissues, 54 metastasis tissues, 105 plasma samples with known cancer site origins from 12 classes was generated in house by a self-designed panel. Performance was measured by area under the curve (AUC) using the one-vs-all approach. Results: 7,453 tumor-specific and 1,533 tissue-specific markers were selected for model construction. AUCs of all cancer types were high in TCGA training and testing set (AUC≥0.96 for all classes). In our validation tissues, esophageal cancer, pancreatic cancer, colorectal cancer, lung adenocarcinoma, breast cancer and liver cancer achieved high AUC in both primary (0.83, 0.83, 0.82, 0.82, 0.80 and 0.79 respectively) and metastasis (0.74, 0.92, 0.86, 0.61, 0.92 and 0.65 respectively). Lung adenocarcinoma, colorectal cancer, liver cancer, breast cancer and esophageal cancer even achieved high AUC in the plasmas. Conclusions: Performance of our model in tissue and plasma samples indicated the potential clinical application of DNA methylation profile in unknown cancer origin identification.

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