Advancing Liver Cancer Treatment through Dynamic Genomics and Systems Biology: A Path Toward Personalized Oncology

This review examines the transformative potential of dynamic genomics and systems biology in modern healthcare, focusing on their roles in precision oncology for liver cancer (Hepatocellular Carcinoma, HCC). It provides an integrated overview of how multi-omics technologies combine to help understand the complex biological landscape of tumors, including genomics, transcriptomics, proteomics, interactomics, metabolomics, and spatial transcriptomics. These advancements enable detailed patient stratification based on molecular, spatial, and functional tumor features, supporting personalized treatment strategies. The review emphasizes the significance of regulatory networks and cell-specific pathways in influencing tumor behavior and immune interactions. By mapping these networks with multi-omics data, clinicians can expect resistance mechanisms, identify the best therapeutic targets, and customize interventions. The approach shifts from traditional one-size-fits-all methods to dynamic, adaptable treatment plans guided by real-time monitoring, including liquid biopsies and wearable biosensors. A practical case study illustrates how a patient with HCC benefits from a personalized therapy plan involving epigenetic therapy, checkpoint inhibitors, and continuous multi-omics monitoring. This highlights the move toward healthcare that anticipates problems, considers the entire body, and adapts quickly to changes in a tumor. Looking ahead, the review discusses innovations such as cloud-based genomic ecosystems, federated learning for data privacy, and AI-driven interpretations that analyze complex multi-layered data. These advancements aim to improve decision-making, enhance clinical results, and change the disease management model, from reactive to predictive and preventative. The review also covers some important ongoing or completed clinical trials targeting HCC that use advanced molecular and immunological techniques. Overall, the review advocates adopting a systems-level, technological, and spatial approach to cancer treatment, stressing the importance of integrating data-driven insights into clinical workflows to advance personalized medicine.