Insights on prevention and gastric cancer detection: an integrative approach through risk factors, microbiome, molecular markers and machine learning

Abstract Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with more than 600,000 deaths expected by 2022. Given that prognosis is heavily influenced by early detection, comprehensive strategies addressing the multifactorial nature of gastric carcinogenesis are urgently needed. This review aims to synthesize current evidence on GC prevention, diagnosis, and predictive modeling, highlighting gaps and future directions. We present an extensive analysis of risk factors, including the role of Helicobacter pylori infection and dysbiosis in the gastric microbiome. Furthermore, we evaluate vaccination strategies, emerging clinical and molecular diagnostic techniques (e.g., serum biomarkers, genomic profiling), and predictive tools leveraging statistical models and artificial intelligence (AI). By integrating these themes, this review provides a multidimensional perspective on GC management, emphasizing translational potential. Our findings underscore the need for tailored screening programs and AI-driven innovations to improve outcomes in high-risk populations.