Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 144-156 http://medscipublisher.com/index.php/cge 151 Figure 3 Performance deep learning biomarkers on validation datasets (Adopted from Xu et al., 2019) Image caption: The deep learning models were evaluated on an independent test set for performance. The 2-year overall survival Kaplan–Meier curves were performed with median stratification (derived from the training set) of the low and high mortality risk groups with no follow-up or up to three follow-ups at 1, 3, and 6 months posttreatment for dataset A (72 definitive patients in the independent test set, log-rank test P < 0.05 for > one follow-up) (Adopted from Xu et al., 2019) Li et al. (2020) conducted genome-wide sequencing, transcriptome sequencing, and proteomic analysis on samples from 146 Chinese colorectal cancer patients, revealing significant differences between primary and metastatic tumors. Research has found that although metastatic tumors have high genetic similarity to primary tumors, there is significant heterogeneity in protein expression patterns. Especially in kinase network analysis, there are significant differences in protein expression patterns between primary tumors and their liver metastases, indicating the role of different signaling pathways in tumor metastasis (Figure 4) (Li et al., 2020) . In addition, through joint analysis of proteomics and phosphoproteomics, the study successfully classified CRC into three subtypes with different clinical outcomes. The integration of multi omics data enables researchers to identify specific phosphorylation patterns that are closely related to drug responses. This is particularly important for patients without obvious targeted mutations, as these phosphorylation patterns can serve as new therapeutic targets, providing new strategies for personalized treatment. The results of this study indicate that the combination of genomics and proteomics is of great significance in understanding the complexity of CRC. The application of comprehensive molecular analysis in the treatment of colorectal cancer has demonstrated its enormous potential and value. By integrating multiple data types, researchers can gain a more comprehensive understanding of the biological characteristics of tumors and develop more precise and personalized treatment strategies, thereby improving the prognosis and quality of life of patients. 5.4 Pan-Cancer studies Integrative Principal Component Regression (iPCR) has been applied successfully in pan-cancer studies to predict drug responses. This method combines various data types, including genomic, transcriptomic, and proteomic data, to create robust predictive models. The integration of these diverse data sources enhances the accuracy of predictions and helps tailor treatments to individual patient profiles(Pender et al., 2020).
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