International Journal of Molecular Medical Science, 2024, Vol.14, No.5, 274-292 http://medscipublisher.com/index.php/ijmms 283 2022). Additionally, the integration of transcriptomics and metabolomics has led to the identification of prognostic signatures that capture relevant molecular alterations in cancer tissues, including those related to cellular signaling and immune system modulation (Xu et al., 2022). Additionally, epidemiological data suggest that abnormalities in the biosynthesis and metabolism of sex steroid hormones are associated with the development of various cancers, including colorectal cancer (Kennelly et al., 2008; Figueroa and Brinton, 2012). Figure 4 Patterns of variation in concordance index (C-index) across omic platforms and cancer types (Adopted from Zhu et. al., 2017) Imagine caption: (a) Scatterplot of the cross-validated C-index across cancer types and omic platforms; (b) proportions of variation explained by cancer type, clinical factors, and omic platforms respectively; (c) the pairwise Spearman correlation pattern in C-index between omic platforms (Adopted from Zhu et. al., 2017) 5.5 Integrated biomarker discovery: case studies The integration of multi-omics data has proven to be a powerful approach for the discovery of early colon cancer biomarkers. For instance, a study utilizing multi-omics data integration identified essential prognostic features such as EPB41, PSMA1, and FGFR3, which were validated through independent datasets and shown to distinguish the prognosis of colon cancer patients effectively (Yin et al., 2020). This study systematically analyzes the prognosis of colorectal cancer based on four omics data types from COAD samples: gene expression, exon expression, DNA methylation, and somatic mutations. It performs functional annotation of prognosis-related features through protein-protein interaction (PPI) networks and cancer-related pathways. Another study demonstrated the utility of multi-omics integration in identifying cancer subtypes and prognostic biomarkers, highlighting the effectiveness of methods like MDICC in clustering and survival analysis (Yang et al., 2022). In summary, the integration of genomic, transcriptomic, epigenetic, proteomic, and metabolomic data provides a comprehensive framework for identifying early colon cancer biomarkers. This multi-omics approach not only enhances the specificity and sensitivity of biomarker discovery but also offers valuable insights into the underlying molecular mechanisms driving colon cancer. 6 Clinical Application of Multi-Omics Biomarkers 6.1 Biomarker validation and verification The validation and verification of biomarkers identified through multi-omics data integration are crucial steps in translating these findings into clinical practice. Multi-omics approaches, which combine data from genomics, transcriptomics, proteomics, and metabolomics, provide a comprehensive view of the molecular underpinnings of cancer. This comprehensive approach can identify novel biomarkers and therapeutic targets that may not be revealed through single-omics data analysis. For instance, the integration of various omics data types has been shown to improve the identification of cancer driver genes and pathways, thereby enhancing the robustness of potential biomarkers (Silverbush et al., 2019; Zhao et al., 2020). The use of advanced computational methods, such as deep learning and autoencoders, further refines the selection of candidate biomarkers by reducing noise and bias in the data (Chai et al., 2021; Li and Sun, 2022). These validated biomarkers can then be subjected to rigorous clinical testing to ensure their efficacy and reliability in predicting cancer prognosis and treatment outcomes (Hristova and Chan, 2019).
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