Approved Research

Integrating metabolomic characteristics, genetic predisposition, and host factors to decipher the risk of gastric cancer

Lay summary

Gastric cancer (GC) is one of the major malignancies worldwide, usually diagnosed at an advanced stage with an unfavorable prognosis. Defining high-risk populations and preventing the development of GC at an early stage is still a bottleneck. Efficient biomarkers and risk prediction models are highly needed. Recently, our team has found three metabolites (!-linolenic acid, linoleic acid, palmitic acid) that were significantly inversely associated with the risk of early GC in a Chinese population enrolled from a high-risk area, but the association needs further external replication. Moreover, where there exist causal relationship between key metabolites and GC development is yet unclear.

Human genetic studies have identified several single nucleotide polymorphisms (SNPs) strongly associated with the development of GC. Furthermore, the polygenic score aggregates the GC-related SNPs and shows potential clinical utility for GC risk stratification, yet the specific molecular consequences that precede GC risk for these polygenic effects are unknown. In addition, previously recognized exposures in association with GC development, such as H.pylori infection, smoked food, and limited intake of fruit and vegetables, have not yet been considered for combined risk prediction of GC. Hence, an integrated prediction model is highly in demand due to the complex etiology of GC and the requirement to precisely locate high-risk populations.

Hence, the following objectives are proposed to address the unsolved issues within a three-year duration. (a) To identify and validate key metabolites and define metabolomic signatures associated with GC development. (b) To explore the bi-directional causal relationship between candidate metabolites and GC risk. (c) To evaluate potential effect mediation (or modification) by metabolites on the association between dietary/nutrient factors, other host characteristics, and risk of GC. (d) To construct the multi-dimensional network and establish an integrated model for GC risk prediction and population stratification.