Network pharmacological approach combined with weighted gene co-expression network analysis identifies CDKN2A as the keg target of Changweiqing against colorectal cancer.

Background and objective: Changweiqing (CWQ) is a Chinese herbal formula for the treatment of the gastrointestinal tract diseases, but its role in the treatment of colorectal cancer (CRC) has not been clarified. This study aimed to explore the molecular mechanism of CWQ in CRC treatment through bioinformatics analysis and network pharmacology.

Methods: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and SwissTargetPrediction database were used to collect the bioactive components of CWQ. The databases including DisgeNET, GeneCards, MalaCards, Online Mendelian Inheritance in Man and Comparative Toxicogenomics were used to obtain CRC-related targets. The Cancer Genome Atlas - colon adenocarcinoma dataset was used to obtain prognosis-related genes in CRC based on weighted gene co-expression network analysis (WGCNA). A protein-protein interaction network was constructed to screen core targets, with STRING database and Cytoscape software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery database. Molecular docking was performed with AutoDock Vina software. Core targets were further analyzed using Gene Expression Profiling Interactive Analysis platform, Human Protein Atlas database, University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) and GeneMANIA database. In vitro experiments were further performed to investigate the effects of quercetin, one of the main components of CWQ, on CRC cells.

Results: 6356, 1901 and 2980 CRC-related genes were obtained from differential expression analysis, WGCNA and open access databases, respectively. CWQ contained a total of 70 bioactive ingredients, of which 64 ingredients had a total of 836 therapeutic targets. Functional enrichment analysis indicated that CWQ may be involved in regulating pathways in cancer, MAPK signaling pathway and AGE-RAGE signaling pathway, and further analysis identified 14 core targets of CWQ. These core targets were significantly correlated with cell cycle, p53 signaling pathway, FoxO signaling pathway and pathways in cancer. Among these core targets, cyclin-dependent kinase inhibitor 2 A (CDKN2A) expression was closely associated with shorter overall survival and clinical stage of CRC patients. The main bioactive ingredients of CWQ targeting CDKN2A were quercetin, luteolin, kaempferol, isorhamnetin, 7-O-methylisomucronulatol and 7-Methoxy-2-methyl isoflavone. Additionally, quercetin caused G0/G1 phase arrest and inhibited the viability of CRC cells.

Conclusion: The active ingredients of CWQ may play an anti-CRC role through multi-targets and multi-pathways, regulating the cell cycle and cell viability of CRC cells.