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Sun S, Dammann J, Lai P, Tian C. Thorough statistical analyses of breast cancer co-methylation patterns. BMC Genom Data 2022; 23:29. [PMID: 35428183 PMCID: PMC9011975 DOI: 10.1186/s12863-022-01046-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 04/01/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data).
Results
Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal samples have more than 93% positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites. The top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns.
Conclusions
Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.
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Levy JJ, Chen Y, Azizgolshani N, Petersen CL, Titus AJ, Moen EL, Vaickus LJ, Salas LA, Christensen BC. MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Networks, Inspired by Capsule Networks. NPJ Syst Biol Appl 2021; 7:33. [PMID: 34417465 PMCID: PMC8379254 DOI: 10.1038/s41540-021-00193-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 07/01/2021] [Indexed: 02/07/2023] Open
Abstract
DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules-such as gene promoter context, CpG island relationship, or user-defined groupings-and relate them to diagnostic and prognostic outcomes. We demonstrate these models' utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep-learning analyses' interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.
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Affiliation(s)
- Joshua J Levy
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA.
| | - Youdinghuan Chen
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Curtis L Petersen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA
| | - Alexander J Titus
- Department of Life Sciences, University of New Hampshire, Manchester, NH, USA
| | - Erika L Moen
- The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Louis J Vaickus
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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