5-hydroxymethylcytosine (5-hmC) as an epigenetics marker has significant impacts on cancer progression. Identification of preserved 5-hmC-related subnetworks in pan-cancer studies could lead to a better understanding of gastrointestinal (GI) cancers insights. Here, we conducted a network-based analysis on 5-hmC values of GI cancers, including colon, gastric, pancreatic cancers, and healthy donors. The co-5-hmC network was reconstructed using the weighted gene coexpression network (WGCNA) method. The hierarchical clustering method was implemented to detect pan-cancer-related modules/subnetworks. The preservation of modules was assessed using another dataset. Modules were functionally enriched, and biological pathways were visualized using the ConsensuspathDB. A 5-hmC predictive model was determined using the elastic network classifier to distinguish cancer patients and healthy individuals. To assess the efficiency of the model the recursive operating characteristics (AUC) curve was computed using the 5 cross-fold validation and an external dataset as well. Three pan-cancer-related subnetworks were detected preserved in another dataset. The main biological pathways were the cell cycle, apoptosis, and extracellular matrix (ECM) organization. The direct association between the cell cycle and ECM, the inverse association between apoptosis and ECM organization, and the inverse association between the cell cycle and ECM organization were detected for the 5-hmC marker in GI cancers. The AUC of 92% (0.73-1.00) was detected for the predictive model. In conclusion, the intricate association among biological pathways of ECM organization, Cell cycle, and apoptosis in GI cancers might be the consequence of epigenetics aberration; such findings could be beneficial in precision medicine using liquid biopsy in early stages.
The greatest challenge in recent years in cancer treatment has been drug screening. Few platforms presented reliable solutions for personalized drug validation and safety testing. Here, we constructed a personalized drug combination protocol as the primary input to such platforms. We used public data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. We developed an individual pattern of perturbed gene expression (IPPGE) for each patient. A protocol was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. We tried to use the concept of drug repurposing, which searches for the new benefits of the existing medicines that may perturb the desired genes. The potential for treatment effectiveness was more significant for drug combinations extracted from specialized and nonspecialized cancer medicines than specialized medicines. Thus, effective treatments can be provided through the approach of drug repurposing and combination drug therapy. Implications The protocol allows personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations. This can be used as a methodological interface between drug repurposing and combination drug therapy in cancer treatment.
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