Abstract Early screening and targeted intervention can effectively reduce cancer burden. However, most studies have proposed polygenic score (PRS) to perform risk prediction and population risk stratification. We investigate the causal links and shared genetics between cancer and metabolic traits to map the onco-metabolic nexus. Using multivariable Cox models, we assessed 240 trait-cancer associations. Genomic analyses included genome-wide and local genetic correlations, and genomic structural equation modeling (gSEM) to identify pathways linking metabolic traits to cancer. We then used DBSLMM to build both single and integrative PRS models based on gSEM and compared their predictive performance. Most of the metabolic traits are risk factors to cancer, such as WHR-CRC (hazard ratio HR = 1.34, 95% confidence interval CI: 1.23-1.46, P = 1.59×10-11). In the genetic correlation analysis, we identified 41 significant pairs in onco-metabolic nexus and 405 significant genomic regions. MR analysis revealed 17 significant causal pairs. The integrative PRS model combining gSEM for metabolic traits improved prediction, with 13.95% variance explained in kidney cancer. The study highlights the intertwined genetic and clinical relationships between cancers and metabolic traits, improving cancer screening and intervention.
Ji et al. (Fri,) studied this question.