DNA methylation age in paired tumor and adjacent normal breast tissue in Chinese women with breast cancer.

Background: Few studies have examined epigenetic age acceleration (AA), the difference between DNA methylation (DNAm) predicted age and chronological age, in relation to somatic genomic features in paired cancer and normal tissue, with less work done in non-European populations. In this study, we aimed to examine DNAm age and its associations with breast cancer risk factors, subtypes, somatic genomic profiles including mutation and copy number alterations and other aging markers in breast tissue of Chinese breast cancer (BC) patients from Hong Kong.

Methods: We performed genome-wide DNA methylation profiling of 196 tumor and 188 paired adjacent normal tissue collected from Chinese BC patients in Hong Kong (HKBC) using Illumina MethylationEPIC array.

Associations between Preserved Foods and Breast Cancer Risk in Hong Kong Chinese Women.

Unlabelled: Preserved food such as cured foods may contain nitrate and nitrite that may contribute to the breast cancer development. Evidence on the associations between these preserved food intakes and risk of breast cancer is sparse. This study aimed to examine the associations between preserved foods (i.

Comparison of somatic mutation landscapes in Chinese versus European breast cancer patients.

Recent genomic studies suggest that Asian breast cancer (BC) may have distinct somatic features; however, most comparisons of BC genomic features across populations did not account for differences in age, subtype, and sequencing methods. In this study, we analyzed whole-exome sequencing (WES) data to characterize omatic copy number alterations (SCNAs) and mutation profiles in 98 Hong Kong BC (HKBC) patients and compared with those from The Cancer Genome Atlas of European ancestry (TCGA-EA, N = 686), which had similar distributions of age at diagnosis and PAM50 subtypes as in HKBC. We developed a two-sample Poisson model to compare driver gene selection pressure, which reflects the effect sizes of cancer driver genes, while accounting for differences in sample size, sequencing platforms, depths, and mutation calling methods.