Decomposition-driven biomarker identification enhances non-invasive early cancer detection.

Accurate marker selection in cell-free DNA (cfDNA) is essential for non-invasive early cancer detection, yet conventional approaches based on bulk tissue data often yield markers with limited efficacy in blood-based assays, due to the sparse and heterogeneous nature of tumor-derived cfDNA released into the bloodstream during early tumorigenesis. Here, we present a component decomposition framework that leverages non-negative matrix factorization (NMF) to resolve distinct methylation components from bulk reduced representation bisulfite sequencing (RRBS) data of five gastrointestinal cancers and their para-carcinoma tissues. This approach demonstrates that cancer-specific signals can be recovered at the component level, enabling the identification of cancer-specific and tissue-of-origin markers with significantly improved performance in cfDNA. Extending this framework to transcriptomic data further validates its utility for cfRNA-based biomarker discovery and molecular subtyping. Together, our framework offers a robust and scalable solution to improve biomarker identification for early cancer detection and patient stratification.