International Journal of Molecular Medical Science, 2024, Vol.14, No.4, 227-238 http://medscipublisher.com/index.php/ijmms 233 prediction model than traditional clinical staging (Wu et al., 2020). Additionally, hypermethylation of specific genes has been linked to survival outcomes, with certain methylation markers correlating with five-year survival rates in CRC patients (Noguer et. al., 2023). These biomarkers can guide treatment decisions and help in stratifying patients based on their risk profiles. 7.3 Therapeutic Targets and Epigenetic Therapy The reversibility of DNA methylation makes it an attractive target for therapeutic intervention. Epigenetic therapies aim to reverse abnormal methylation patterns, thereby restoring normal gene function and inhibiting cancer progression. 7.3.1 DNMT inhibitors DNA methyltransferase (DNMT) inhibitors, such as 5-aza-2'-deoxycytidine (5-aza-dC), have shown promise in preclinical and clinical studies. These inhibitors work by demethylating DNA, leading to the re-expression of tumor suppressor genes that are silenced in cancer cells. For instance, combined treatment with 5-aza-dC and EZH2 inhibitors has demonstrated significant anti-tumor effects in colon cancer models, highlighting the potential of DNMT inhibitors in epigenetic therapy (Takeshima et al., 2015). 7.3.2 Combination therapies Combining DNMT inhibitors with other therapeutic agents can enhance their efficacy. For example, the combination of 5-aza-dC with EZH2 inhibitors has been shown to induce the re-expression of genes with dual modifications (DNA methylation and H3K27me3), resulting in a more pronounced anti-tumor effect compared to single-agent treatments (Takeshima et al., 2015). This approach leverages the cancer cell-specific epigenetic landscape to achieve more effective and targeted therapy. Additionally, integrating epigenetic therapies with conventional treatments such as chemotherapy and immunotherapy could potentially improve treatment outcomes and overcome resistance mechanisms (Jung et al, 2020; Davalos and Esteller, 2022). DNA methylation holds significant clinical implications in colon cancer, from early detection and prognosis to therapeutic targeting. Continued research and clinical validation of these biomarkers and therapies will be crucial in advancing precision medicine for colon cancer patients. 8 Challenges and Future Directions 8.1 Technical and analytical challenges The study of DNA methylation in colon cancer faces several technical and analytical challenges. One significant issue is the heterogeneity of tumor samples, which can complicate the interpretation of methylation data. Tumor samples often contain a mix of cancerous and non-cancerous cells, leading to variability in methylation patterns that can obscure true cancer-specific changes (Jung et al., 2020; Costa et al., 2023). Additionally, the dynamic nature of epigenetic modifications, such as DNA methylation, requires high-resolution and high-throughput techniques to capture these changes accurately. Current technologies, while advanced, still struggle with the sensitivity and specificity needed to detect subtle but clinically significant methylation changes (Tao et al., 2020; Feinberg and Levchenko, 2023). Moreover, the integration of methylation data with other omics data (e.g., transcriptomics, proteomics) presents computational and analytical challenges, necessitating the development of robust bioinformatics tools and algorithms (Cao et al., 2020; Li et al., 2020). 8.2 Integrating multi-omics approaches Integrating multi-omics approaches is crucial for a comprehensive understanding of the epigenetic landscape in colon cancer. Combining DNA methylation data with other types of data, such as histone modifications, non-coding RNA expression, and chromatin accessibility, can provide a more holistic view of the regulatory mechanisms driving cancer progression (Jung et al., 2020; Cao et al., 2020). For instance, studies have shown that integrating methylation and gene expression profiles can identify key regulatory networks and potential therapeutic targets (Dai et al., 2020; Li et al., 2020). However, this integration requires sophisticated computational frameworks and large-scale data repositories, which are still under development. The challenge lies
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