Gene expression profiling to uncover prognostic and therapeutic targets in colon cancer, combined with docking and dynamics studies to discover potent anticancer inhibitor.

Drug resistance poses a major obstacle to the efficient treatment of colorectal cancer (CRC), which is one of the cancers that kill people most often in the United States. Advanced colorectal cancer patients frequently pass away from the illness, even with advancements in chemotherapy and targeted therapies. Developing new biomarkers and therapeutic targets is essential to enhancing prognosis and therapy effectiveness. My goal in this study was to use bioinformatics analysis of microarray data to find possible biomarkers and treatment targets for colorectal cancer. Using an ArrayExpress database, I examined a dataset on colon cancer to find genes that were differentially expressed (DEGs) in tumor versus healthy tissues. Integration of advanced bioinformatics tools provided robust insights into the identification and analysis of EGFR as a key player. STRING and Cytoscape enabled the construction and visualization of protein-protein interaction networks, highlighting EGFR as a hub gene due to its centrality and interaction profile. Functional enrichment analysis through DAVID revealed EGFR's involvement in critical biological pathways, as identified in GO and KEGG analyses. This underscores the power of combining computational tools to uncover significant biomarkers like EGFR. Autodock Vina screening of the NCI diversity dataset identified two potential EGFR inhibitors, ZINC13597410 and ZINC04896472. MD simulation data revealed that ZINC04896472 could be potential anticancer inhibitor. These findings serve as a basis for the creation of novel therapeutic approaches that target EGFR and other discovered pathways in CRC. The suggested strategy may improve the efficacy of CRC therapy and advance personalized medicine.