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

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.