Interactomics to determine new drug targets with data mining: In silico a scenario

The occurrence of cancer is a comprehensive process that does not depend solely on the generation of genetic mutations. They usually occur as a result of disruptions in complex cellular networks (such as protein-protein interaction, transcriptional regulatory and metabolic networks). Although complex diseases such as cancer show a similar phenotype, the pathways they follow vary in each patient. In this study, pre-processing steps were performed on raw RNA sequence data prior to transcriptome analysis. In silico analysis was used to provide detailed information about the molecular mechanisms of colorectal cancer. Expression levels were analyzed on the RNA sequence data, which were became suitable for analysis. Target molecule and pathway analyzes were performed with statistically significant transcriptom analysis results. Protein-protein interaction networks were designed on potential molecules. As a result of the analyzes, biomarker and potential drug targets were shown by identifying the significant pathways in colorectal cancer. Xenobiotic glucuronidation mechanisms, UDP glucuronosyltransferase enzymes, drug resistance mechanisms have been found to be associated with metastatic colorectal cancer. CYP3A4 was found to be related to drug resistance metabolism especially due to UGT1A family and PPI network. CD44 is signified as a candidate drug target for CRC. The in silico approach, which enables the identification of new drug targets and biomarkers which are specific to the disease, will enable the design of new diagnostic and personalized medical treatment methods for colorectal cancers.