This analysis reveals advances in biomarkers for gastrointestinal cancers, suggesting AI may improve detection accuracy.
Gastrointestinal (GI) cancers remain a leading cause of global cancer-related morbidity and mortality, underscoring the need for reliable biomarkers to improve early detection, risk stratification, and treatment monitoring. Over the past decade, significant progress has been made in identifying circulating and tissue-based biomarkers, including cell-free DNA, microRNAs, and metabolomic profiles, that offer potential for enhancing diagnostic accuracy and prognostic prediction. Integration of multi-omics approaches, spanning genomics, transcriptomics, proteomics, and metabolomics, has further expanded the biomarker landscape, enabling a more comprehensive understanding of tumor biology and individualized patient trajectories. Artificial intelligence and machine learning models are increasingly being applied to analyze complex datasets, yielding promising results in cancer risk prediction and progression monitoring. Despite these advances, translation into routine clinical practice is limited by technical variability, high costs, lack of standardized protocols, and the need for large-scale, multicenter validation studies. Moreover, ethical concerns, regulatory hurdles, and risks of overdiagnosis remain important barriers. Future directions emphasize the development of point-of-care biomarker testing kits, the incorporation of personalized screening strategies based on genetic and lifestyle risk profiles, and greater international collaboration for biomarker harmonization. Collectively, these advances highlight the transformative potential of biomarkers in reshaping GI cancer management, while also pointing to critical challenges that must be addressed to achieve meaningful clinical adoption.
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Singh et al. (2025) studied this question.
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