Multi-omics approach for identification of etiopathogenesis and early detection biomarkers of stomach cancer and precursor lesions

The overall aim is to utilize multi-omics approach to identify novel etiopathogenesis and early detection biomarkers for stomach cancer and its precursor lesions.

To achieve this aim, first we will use stored serum samples to perform metabolomics profiling among 12,599 twin subjects, among whom 1034 were deemed to have chronic atrophic gastritis based on measured pepsinogen I and II levels. Logistic regression will be used to search for metabolites related to the risk of chronic atrophic gastritis.

Second, we will further measure serum proteome by using two quantitatively precise proteomics assays, among the above-mentioned twin subjects.

Identified protein biomarkers will be combined with metabolomics biomarkers to create a prediction model for chronic atrophic gastritis.

Last, we have created a cohort of subjects who were histopathologically diagnosed with chronic atrophic gastritis or more severe precursor lesions. They were followed for stomach cancer occurrence, and a nested case-control study will be performed.

Baseline formalin-fixed paraffin-embedded tissue blocks will be retrieved for both stomach cancer cases and their matched controls, and patterns of tissue proteome and transcriptome will be compared, to identify driving factors associated with progression of precursor lesions to malignancy.

The results will hopefully improve our understanding of the etiological factors and provide promising early detection biomarkers for stomach cancer and its precursor lesions.