Preliminary Analysis of Within-Sample Co-methylation Patterns in Normal and Cancerous Breast Samples




Sun, Lillian
Namboodiri, Surya
Chen, Emily
Sun, Shuying

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DNA methylation plays a significant role in regulating the expression of certain genes in both cancerous and normal breast tissues. It is therefore important to study within-sample co-methylation, ie, methylation patterns between consecutive sites in a chromosome. In this article, we develop 2 new methods to compare co-methylation patterns between normal and cancerous breast samples. In particular, we investigate the co-methylation patterns of 4 different methylation states/levels separately. Using these 2 methods, we focus on addressing the following questions: How often does 1 methylation state change to other methylation states and how is this change dependent on chromosome distance? What co-methylation patterns do normal and cancerous breast samples have? Do genomic sites with different methylation states/levels have different co-methylation patterns? Our results show that cancerous and normal co-methylation patterns are significantly different. We find that this difference exists even when the physical distance of 2 sites are less than 50 bases. Breast cancer cell lines tend to remain in the same methylation state more often than normal samples, especially for the no/low or high/full methylation states. We also find that the co-methylation region lengths for various methylation states (no/low, partial, and high/full methylation states) are very different. For example, the co-methylation region lengths for partial methylation regions are shorter than the unmethylated or fully methylated regions. Our research may provide a deep understanding of co-methylation patterns. These co-methylation patterns will aid in discovering and understanding new methylation events that may be related to novel biomarkers.



bioinformatics, breast cancer, within sample co-methylation, Mathematics


Sun, L., Namboodiri, S., Chen, E., & Sun, S. (2019). Preliminary analysis of within-sample co-methylation patterns in normal and cancerous breast samples. Cancer Informatics, 18, pp. 1-14.


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