Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer
Dongheng Ma, Canfeng Fan, Tomoya Sano, Kyoka Kawabata, Hinano Nishikubo, Daiki Imanishi, Takashi Sakuma, Koji Maruo, Yurie Yamamoto, Tasuku Matsuoka, Masakazu Yashiro
Abstract
Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC's heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding of GC by integrating molecular and imaging data. In this review, we summarize the current landscape of ML-based multiomics integration for GC, highlighting its role in precision diagnosis, prognosis prediction, and biomarker discovery for achieving personalized medicine.