Cancer Genetics and Epigenetics, 2025, Vol.13, No.1, 1-10 http://medscipublisher.com/index.php/cge 5 5 Comprehensive Omics Approach 5.1 Epigenetic regulation (such as DNA methylation) and gene-gene interactions Epigenetic changes, especially DNA methylation, play a significant role in making people prone to colorectal cancer (CRC). It can affect the activity of genes and promote tumor formation. Analyzing the gene expression data and DNA methylation data together has discovered many genes that may make people prone to CRC. Studies have shown that many gene changes identified through genome-wide association studies (GWAS) affect gene regulation through epigenetic mechanisms (Yuan et al., 2021). For instance, excessive DNA methylation and alterations in gene expression patterns can change the activity patterns of cells, such as enhancing their mobility and invasion capabilities, which is crucial for the development of CRC (Yuan et al., 2021; Yao et al., 2022). The mutual influence among genes makes the genetic situation of CRC more complex. Studies have found that the interaction between genetic changes and epigenetic changes can disrupt key processes of cancer cell growth, such as MAPK and PI3K-Akt signaling, thereby affecting whether a person will develop CRC (Zhang et al., 2023). These findings indicate that to clarify the risk of CRC, both genetic and epigenetic factors need to be considered simultaneously. 5.2 Integrate multi-omics data to enhance the accuracy of risk prediction The integration of data from genomics, transcriptomics, proteomics, metabolomics and microbiomics has greatly promoted the discovery of new CRC biomarkers and made the disease risk prediction model more accurate (Ullah et al., 2022; Bischof et al., 2024). The multi-omics research method can capture the complex interrelationships among different molecular levels, enabling a comprehensive understanding of the diversity of CRC, which cannot be achieved by studying a single type of data alone (Figure 2) (Ullah et al., 2022; Bischof et al., 2024). For instance, by combining the data of the microbiome, metabolome and transcriptome, high-precision models can be created through machine learning for detecting CRC and classifying disease risk levels (Zhang et al., 2022). Figure 2 Graphical representation of different multi-omics-based approaches in discovering novel CRC biomarkers and therapeutic targets (Adopted from Ullah et al., 2022) Comprehensive multi-omics analysis can also help people determine different molecular subtypes (CMS) of CRC, and the key mutations, signal transduction pathways and immune characteristics of each subtype are different (Bischof et al., 2024). Such detailed molecular classification can provide a reference for formulating personalized treatment plans and also make the treatment management of CRC patients more effective (Ullah et al., 2022; Bischof et al., 2024). 5.3 Network biology and pathway analysis of CRC susceptibility Network biology and biopath-based analysis utilize multi-omics data to study the complex molecular interactions that make people prone to CRC. By integrating gene networks and multi-omics data, researchers identified the genes most likely to be associated with CRC risk and clarified the roles of these genes in key biological processes such as cell growth, immune response, and metabolic regulation (Zhang et al., 2023). For instance, through the method of network analysis, it was found that transcription factors such as CEBPB are key factors regulating CRC. By analyzing biological pathways, genetic changes can be linked to actual influences (Zhang et al., 2023).
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