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Can Cytosine and Guanine Nucleotide Pairs Influence Epigenetic Aging of Breast Tissue?

By: Vanessa A. Carter, BS
Posted: Tuesday, May 31, 2022

Although the underlying etiology linking age and breast cancer remains unclear, Mariya Rozenblit, MD, of Yale School of Medicine, New Haven, Connecticut, and colleagues previously reported that epigenetic aging signatures may influence breast cancer risk. Their current study aimed to identify which epigenetic clock signatures may determine tumor-adjacent breast tissue versus healthy tissue. The investigators ultimately determined that using DNA methylation as a biomarker may offer further insight into the mechanisms of breast cancer development. These results were published in Clinical Epigenetics.

“Moving forward, it will be critical for studies to explain whether epigenetic age acceleration in breast tissue precedes the emergence of breast cancer,” said Dr. Rozenblit in a Yale Cancer Center press release. “Our next step is to determine if methylation changes at cytosine-phosphate-guanines associated with polycomb-related genes can be used to assess the risk of developing breast cancer.”

Specimens included the following samples: DNA from newly diagnosed, untreated breast cancer (n = 79); DNA from women without cancer (n = 91); normal-appearing breast tissue from at least 3 cm away from a primary tumor (n = 34); and normal breast tissue from women without cancer (n = 50). DNA methylation age was calculated for a total of six epigenetic clocks used to distinguish between breast tissue samples.

The mean age of the breast cancer cohort was 51.4 years, and the mean age of the control cohort was 47.6 years; patient demographics were well balanced between groups. Interestingly, the Levine (P = .0037) and Yang clocks (P = .023) demonstrated significant epigenetic age acceleration in breast cancer tissue versus normal tissue. Furthermore, it was found that cytosine-phosphate-guanines associated with polycomb-related genes more robustly captured epigenetic age acceleration in cases rather than controls (P = .00012).

Disclosure: For full disclosures of the study authors, visit clinicalepigeneticsjournal.biomedcentral.com.


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