Breast cancer detection by methylated ultra-short cfDNA fragment features

Cell-free DNA is a promising source of biomarkers for early cancer detection. cfDNA fragmentation patterns and methylation profiles exhibit coordinated changes. Although several studies have explored cfDNA methylation or fragmentomics individually for breast cancer detection, systematic evaluation of integrated methylated fragmentomic features remains limited. Our study focused on identifying cfDNA methylation and methylated fragmentation markers for accurate breast cancer detection. This study included 25 breast cancer patients and 110 healthy controls. We performed comprehensive profiling of methylation and methylated fragmentation patterns of cfDNA using the cfMeDIP-seq method. Then, we used multiple algorithms to identify cfDNA methylation markers and methylated fragmentation markers, and developed a stacked model. Methylated fragment features were significantly different from non-methylated fragment features, with the proportion of ultra-short fragments in breast cancer patients was lower than that in non-cancer controls. We extracted 7 types of features from the methylation and methylated fragments, and developed a stacked machine-learning model that achieved an AUC of 1 in both the test and validation sets, indicating high sensitivity and specificity. Our study demonstrates that cfDNA methylation and methylated ultra-short fragmentomics biomarkers derived from the cfMeDIP-seq method can effectively diagnose breast cancer, indicating its potential clinical utility.