Targeted Therapies and Computational Approaches for the Effective Management of Gastric Cancer

Abstract:: Gastric cancer continues to be a critical global health burden, ranking among the top causes of cancer- related mortality. Its complex and heterogeneous nature, caused by the combination of genetic mutations, epigenetic alterations, environmental influences, and microbial infections, poses significant challenges for early diagnosis. Conventional therapies such as surgery and chemotherapy offer limited benefits, particularly in advanced stages, necessitating the shift toward precision medicine. A literature search was conducted across the PubMed, Scopus, and Google Scholar databases to identify peerreviewed publications on gastric cancer, its pathophysiology, biomarkers, and targeted therapies. Recent advancements in molecular biology, including Next-Generation Sequencing (NGS) and Genome-Wide Association Studies (GWAS), have enabled the identification of key molecular subtypes and predictive biomarkers. These biomarkers are pivotal in developing targeted therapies and improving treatment response. Liquid biopsies and circulating tumor DNA are emerging as non-invasive tools for disease monitoring and early relapse detection. Artificial Intelligence and Machine Learning (AI/ML) are revolutionizing the field by enabling early cancer detection, improving diagnostic accuracy, and personalizing treatment strategies using multiomics data. Novel therapeutic approaches, such as antibody-drug conjugates, immune checkpoint inhibitors, and anti-angiogenic agents, are being explored through clinical trials to enhance survival outcomes. This review presents a comprehensive analysis of the molecular mechanisms, risk factors, therapeutic targets, and computational approaches in gastric cancer, and underscores the importance of integrating AI/ML, molecular diagnostics, and biomarker-driven therapies to advance toward more precise and individualized treatment strategies, ultimately improving prognosis and quality of life for affected patients.