Integrative omics approaches to uncover liquid-based cancer-predicting biomarkers in Lynch syndrome.

Abstract

Lynch syndrome is a genetic cancer-predisposing syndrome caused by pathogenic mutations in DNA mismatch repair (path_MMR) genes. Due to the elevated cancer risk, novel screening methods, alongside current surveillance techniques, could enhance cancer risk stratification. Here we show how bi-omics integration could be utilized to pinpoint potential cancer-predicting biomarkers in Lynch syndrome. We studied which blood-based circulating microRNAs and metabolites could predict Lynch syndrome cancer occurrence within a 5.8-year prospective surveillance period. We used single- and bi-omics bioinformatic analyses and identified omics-level patterns and associations across these biological layers. Lasso Cox regression was used to highlight the most promising cancer-predicting biomarkers. Our findings revealed distinct circulating metabolite landscapes among path_MMR variant carriers and a circulating microRNA co-expression module significantly associated with future cancer incidence. These microRNAs regulate cancer-related pathways, including the PI3K/Akt signaling pathway. Additionally, a metabolite module consisting of ApoB-containing lipoproteins (low-, intermediate-, and very low-density lipoproteins) showed distinct levels across path_MMR variants. Notably, three biomarkers-hsa-miR-101-3p, hsa-miR-183-5p, and triglycerides in high-density lipoprotein particles (HDL_TG)-significantly predicted cancer risk, achieving a Harrel's Concordance Index (C-index) of 0.76 (p = .0007). Elevated levels of these biomarkers indicated increased cancer risk. Internal validation of the model yielded a C-index of 0.72. The bi-omics approach and the identified biomarkers offer promising insights for future studies regarding cancer risk identification in Lynch syndrome.