Cancer Genetics and Epigenetics, 2025, Vol.13, No.1, 1-10 http://medscipublisher.com/index.php/cge 6 Analysis based on biological pathways also found that changes in intestinal microbiota and their metabolites interact with human genes, affecting tumor immunity and therapeutic effects (Zhang et al., 2022). These research results indicate that network and biological pathway analysis have great potential in discovering new therapeutic targets and understanding the causes of CRC from an overall perspective (Zhang et al., 2022; Zhang et al., 2023). 6 Challenges and Future Prospects 6.1 The functional verification is relatively slow; the biological mechanism is still unclear Although genome-wide association studies and multi-gene panel tests can quickly identify genetic changes related to colorectal cancer (CRC), it is relatively slow to figure out exactly what these changes are for. Many genes and locations that have been found to potentially increase the risk of disease, especially those that are less active or not in the coding region, have not yet been clarified exactly how they work. This makes it very difficult to turn these genetic discoveries into an understanding of the causes of diseases or actual treatment methods (Valle, 2014; Yuan et al., 2021). How to determine those genetic changes of unclear significance and exactly what roles they play in the pathogenesis of CRC remains a big problem. It is equally difficult to identify the genes that cause some hereditary CRC syndromes that have not yet been clarified (Valle, 2014; Lorans et al., 2018). The diversity of genes themselves and various risk influencing factors (such as the interactions between genes and between genes and the environment) also make the study of the genetics of CRC more complex. New research tools such as patient tissue models and organoids are expected to help clarify the actual effects brought about by genetic alterations. However, the widespread application of these tools in research and their true effectiveness are still under development (Yuan et al., 2021; Alipourgivi et al., 2023). Continuing to collect a large amount of diverse data and strengthening international cooperation are of great significance for understanding the biological causes of CRC susceptibility (Valle, 2014; Lorans et al., 2018). 6.2 Ethical and practical challenges of personalized screening strategies The development of personalized CRC screening methods based on genetic risk has brought about some important ethical and practical issues. Opinions vary on which genes should be included in the clinical testing portfolio, especially those whose functions have not been fully determined or whose clinical values are unclear. This makes it more troublesome for patients to provide advice and manage risks (Lorans et al., 2018). Furthermore, the discovery of pathogenic gene changes in those who originally did not meet the requirements of traditional testing poses a challenge to the existing guidelines and may lead to too much or too little examination or treatment (Pearlman et al., 2017; Lorans et al., 2018). Practical difficulties also include how to conduct genetic screening fairly among various populations, avoid possible genetic discrimination, and the need for a sufficient number of professional genetic counselors. Conducting large-scale genetic testing requires the coordination of doctors, policymakers and educators to ensure maximum benefits and minimum drawbacks, especially in areas with limited resources (Szuman et al., 2024). Those who make decisions need to give priority to supporting relevant research and infrastructure in order to responsibly apply genetic risk assessment to routine CRC prevention and care (Lorans et al., 2018; Szuman et al., 2024). 6.3 The potential of interdisciplinary collaboration and artificial intelligence-driven research Having experts from different fields work together is increasingly regarded as the key to promoting genetic research on colorectal cancer and applying the findings to clinical practice. To achieve truly effective progress, it requires the joint efforts of geneticists, doctors, bioinformatics experts, social scientists and policymakers to address various issues such as explaining genetic changes, establishing risk models, and caring for patients (Lorans et al., 2018; Szuman et al., 2024). Combining a large amount of data containing multiple information (multi-omics) and building a large biobank will help discover and confirm new susceptibility genes and therapeutic targets (Yuan et al., 2021; Alipourgivi et al., 2023). Artificial intelligence (AI) and machine learning provide powerful tools for analyzing complex genetic and multi-omics data, improving risk prediction models, and identifying new biomarkers for CRC (Yuan et al., 2021).
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