Systems Biology Approach to Gene Network Analysis Identifies Therapeutic and Diagnostic Targets in Colorectal Cancer

Background Colorectal cancer (CRC) is a significant contributor to global cancer mortality. Although numerous transcriptomic studies have identified differentially expressed genes (DEGs), few have progressed to experimentally validated, druggable targets. A critical gap lies in bridging large-scale gene network analysis with in silico drug prediction and experimental confirmation to accelerate precision therapy. Methods We applied an integrative systems biology framework combining transcriptomic profiling, protein–protein interaction (PPI) network modeling, enrichment analysis, drug–target prediction, and experimental validation. From the GSE225570 dataset of paired CRC and normal tissues, DEGs were identified, hub genes ranked with CytoHubba, and biological significance assessed via Gene Ontology/Reactome. Expression was confirmed via UALCAN and real-time PCR, while molecular docking evaluated the binding affinities of therapeutically significant ligands. Results The differential analysis identified over 200 DEGs, with ten hub genes situated at the core of the PPI network. Enrichment indicated that these hubs and clusters converge on dysregulated cell cycle control and DNA replication fidelity, while also implicating immune-associated processes and metastatic signaling pathways. TTK, CKS2, TPX2, and MYC stood out as good biomarkers and treatment targets. Docking revealed potent affinities of BAY-1217389 and BAY-1161909 for TTK, CX-4945 for CKS2, and MYCi975 and PROTAC-based degraders for MYC, while dacomitinib targeted TPX2. real-time PCR confirmed ~ 50-fold upregulation of TTK in CRC tissues, corroborating computational predictions. Conclusions This study addresses a translational gap by including network modeling, drug–target prediction, and experimental validation. Our concept offers a scalable approach for biomarker identification and targeted therapy development in cancer by focusing on druggable CRC drivers and linking them with therapeutically relevant inhibitors.