Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 125-136 http://medscipublisher.com/index.php/cge 133 8 Future Perspectives 8.1 Emerging therapies The landscape of liver cancer treatment is rapidly evolving with the advent of new therapeutic strategies. Recent advancements in genomics and molecular biomarkers have paved the way for more personalized treatment approaches. For instance, the development of immune checkpoint inhibitors such as Atezolizumab-Bevacizumab and Durvalumab-Tremelimumab has shown promising results in improving survival outcomes and enabling disease downstaging to curative resection (Moroney et al., 2023). Additionally, the integration of big 'omics' data, including genomics, transcriptomics, proteomics, and metabolomics, has facilitated the identification of new therapeutic targets and biomarkers, thereby enhancing the precision of treatment strategies. The use of targeted therapies like regorafenib, lenvatinib, and cabozantinib, along with immune checkpoint inhibitors, represents a significant leap forward in the management of hepatocellular carcinoma (HCC) (Chen et al., 2020). 8.2 Role of artificial intelligence Artificial Intelligence (AI) is playing an increasingly pivotal role in the diagnosis, treatment, and management of liver cancer. AI technologies, including machine learning and deep learning, have demonstrated superior predictive performance compared to traditional statistical methods in various aspects of liver cancer care. For example, AI has been effectively used in predicting survival outcomes, treatment responses, and identifying prognostic factors in hepatocellular carcinoma (Lai et al., 2020). AI-driven radiogenomics, which combines radiological imaging with genomic data, has also shown great potential in stratifying patient risk, monitoring therapeutic approaches, and assessing clinical outcomes (Saxena et al., 2022). Moreover, AI applications in medical imaging diagnosis and adjuvant therapy have significantly improved the accuracy and efficiency of liver cancer management. The integration of AI in healthcare is expected to continue growing, offering new opportunities for personalized and evidence-based patient care (Rompianesi et al., 2022). 8.3 Collaborative research The future of precision treatment for liver cancer heavily relies on collaborative research efforts. The complexity and heterogeneity of liver cancer necessitate a multidisciplinary approach involving oncologists, geneticists, bioinformaticians, and other healthcare professionals. Collaborative research initiatives are crucial for the development and validation of new biomarkers, therapeutic targets, and treatment algorithms. For instance, the establishment of public data sets related to HCC and the application of AI methods to these data sets have provided valuable insights into the mechanisms of HCC and facilitated the discovery of new drugs and biomarkers3. Additionally, international collaborations and large-scale clinical trials are essential for overcoming the challenges associated with tumor heterogeneity and acquired resistance, ultimately leading to more effective and personalized treatment strategies. The integration of precision medicine and immunoprevention approaches in cancer prevention research further underscores the importance of collaborative efforts in advancing the field (Kensler et al., 2016). In conclusion, the prospects of precision treatment for liver cancer are promising, with emerging therapies, AI applications, and collaborative research playing key roles in shaping the future of liver cancer care. Continued advancements in these areas are expected to significantly improve patient outcomes and revolutionize the management of liver cancer. 9 Concluding Remarks The exploration of precision treatment for liver cancer through genome-wide association studies (GWAS) has yielded several significant insights. Firstly, the identification of high-frequency driver genes and mutations, such as those in the Wnt/β-catenin pathway, TP53/cell-cycle pathways, telomere maintenance, and chromatin regulators, has been pivotal in understanding hepatocarcinogenesis. Additionally, the integration of HBV into cancer-related genes has been recognized as a critical driver event. Molecular classifications based on somatic mutations, RNA expression, and DNA methylation profiles have shown a strong correlation with patient prognosis, underscoring the potential of precision medicine in improving treatment outcomes. Despite these advancements, the clinical application of precision medicine in liver cancer remains challenging. The heterogeneity of liver cancer, both at the genetic and molecular levels, complicates the development of effective targeted therapies. Moreover, while next-generation sequencing (NGS) technologies have facilitated the identification of actionable mutations, the
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