Profiling the Onco-metabolic Nexus and Improving Cancer Risk Prediction Performance: A Large-scale Cohort and...

Area of Science:

• Genetics
• Oncology
• Metabolic Research

Background:

• Early cancer screening and intervention are crucial for reducing disease burden.
• Polygenic risk scores (PRS) are commonly used for cancer risk prediction and population stratification.
• Understanding the interplay between metabolic traits and cancer is essential for developing effective strategies.

Purpose of the Study:

• To investigate the causal relationships and shared genetic factors between metabolic traits and various cancers.
• To map the complex network of onco-metabolic interactions.
• To develop and evaluate an integrative polygenic risk score (PRS) model for improved cancer risk prediction.

Main Methods:

• Utilized multivariable Cox models to assess 240 trait-cancer associations.
• Conducted genome-wide and local genetic correlation analyses.
• Employed genomic structural equation modeling (gSEM) and Mendelian Randomization (MR) analysis.
• Developed single and integrative PRS models using DBSLMM based on gSEM.

Main Results:

• Identified numerous metabolic traits as significant risk factors for cancer (e.g., WHR-CRC, HR=1.34).
• Discovered 41 significant genetic correlation pairs in the onco-metabolic nexus and 405 significant genomic regions.
• Revealed 17 significant causal pairs through MR analysis.
• The integrative PRS model demonstrated improved predictive performance, explaining 13.95% of the variance in kidney cancer.

Conclusions:

• There are significant intertwined genetic and clinical relationships between metabolic traits and cancer.
• The developed integrative PRS model enhances cancer risk prediction accuracy.
• Findings support improved cancer screening and targeted intervention strategies based on metabolic and genetic profiles.