In silico study of cancer stage-specific DNA methylation pattern in White breast cancer patients based on TCGA dataset. (June 2021)

• Record Type: Journal Article
• Authors: Ivan, Jeremias; Patricia, Gabriella; Agustriawan, David
• Is Part Of: Computational biology and chemistry. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: TCGA The Cancer Genome Atlas -- BRCA breast invasive carcinoma -- GDC Data Portal Genomic Data Commons Data Portal -- ANOVA analysis of variance -- ROC receiver operating characteristic curve -- DNA deoxyribonucleic acid -- QL-GLM quasi-likelihood - generalized linear model -- Hg38 human genome assembly Genome Reference Consortium Human Build 38 -- AUC area under curve -- EWAS epigenome-wide association studies -- COSMIC Catalogue of Somatic Mutations in Cancer -- StringDB Search Tool for the Retrieval of Interacting proteins database -- logFC log fold-change -- FDR false discovery rate -- miRNA micro-ribonucleic acid -- HCC hepatocellular carcinoma -- OSCC oral squamous cell carcinoma -- N/A not applicable -- ER estrogen receptor -- ERE estrogen response element -- ATP adenosine triphosphate; Methylation -- Breast cancer -- TCGA -- Epigenetics -- Biomarker; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107498
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Background: Breast cancer is one of the most common types of cancer among women. As current breast cancer treatments are still ineffective, we assess the methylation pattern of White breast cancer patients across cancer stage based on The Cancer Genome Atlas (TCGA) dataset. Significant hypermethylation and hypomethylation can regulate the gene expression, thus becoming potential biomarkers in breast cancer tumorigenesis. Methods: DNA methylation data was downloaded using TCGA Assembler 2 based on race-specific metadata of TCGA - Breast Invasive Carcinoma (TCGA-BRCA) project from Genomic Data Commons (GDC) Data Portal. After the data was divided into each cancer stage, duplicated data of each patient was removed using OMICSBind, while differentially-expressed probes were identified using edgeR. The resulting probes were validated based on correlation and regression analysis with the gene expression, ANOVA between cancer stages, ROC curve per stage, as well as databases. Results: Based on the White dataset, we found 66 significant hypermethylated genes with logFC > 1.8 between Stage I-III. From this number, three epigenetic-regulated, stage-specific genes are proposed to be the detection biomarkers of breast cancer due to significant aberrant gene expression and/or low mutation ratio among breast cancer patients: ABCC9 (Stage III), SHISA3 (Stage II), and POU4F1 (Stage I-II). Conclusions: Our study shows that ABCC9, SHISA3, and POU4F1 are potential stage-specific detection biomarkers of breast cancer for White individuals, whereas their roles in other races need to be studied further.