Research Question:
By integrating multi-omics data (including genomics, epigenomics, and microbiomics) and clinical information, we aim to develop a precise multi-gene risk score model for atrophic gastritis. The goal is to accurately predict an individual’s risk of developing atrophic gastritis and its potential progression to gastric cancer, thereby providing a scientific basis for early risk identification and personalized prevention in clinical settings.
Research Objectives:
To construct and validate a high-performance Atrophic Gastritis Polygenic Risk Score (AG-PRS) model that integrates genetic variations, epigenetic modifications, microbiome characteristics, and clinical factors. Using machine learning methods, we will achieve precise prediction of atrophic gastritis incidence risk, explore the interaction between genetic risks and environmental factors (such as Helicobacter pylori infection), and ultimately provide an innovative tool for clinical risk stratification and personalized prevention strategies.
Scientific Rationale:
Atrophic gastritis, a critical precursor lesion of gastric cancer, involves complex genetic and environmental factors that traditional research methods struggle to comprehensively elucidate. Based on the latest advances in multi-omics research and precision medicine, this study proposes an integrated risk assessment approach. By systematically analyzing genetic variations, epigenetic modifications, microbiome characteristics, and clinical data, we aim to overcome the limitations of traditional single-factor prediction, providing a more precise and reliable scientific basis for early identification and personalized prevention of atrophic gastritis.