Integrative approach using network pharmacology, bioinformatics, and experimental methods to explore the mechanism of cantharidin in treating colorectal cancer

Cantharidin, a terpenoid produced by blister beetles, has been used in traditional Chinese medicine to treat various ailments and cancers. However, its biological activity, impact, and Anticancer mechanisms remain unclear. The Cantharidin chemical gene connections were identified using various databases. The GSE21815 dataset was used to collect the gene expression information. Differential gene analysis and gene ontology analyses were performed. Gene set enrichment analysis was used to assess the activation of disease pathways. Weighted gene co-expression network analysis and differential analysis were used to identify illness-associated genes, examine differential genes, and discover therapeutic targets via protein-protein interactions. MCODE analysis of major subgroup networks was used to identify critical genes influenced by Cantharidin, examine variations in the expression of key clustered genes in colorectal Cancer vs. control samples, and describe the subject operators. Single-cell GSE188711 dataset was preprocessed to investigate Cantharidin's therapeutic targets and signaling pathways in colorectal Cancer. Single-cell RNA sequencing was utilized to identify 22 cell clusters and marker genes for two different cell types in each cluster. The effects of different Cantharidin concentrations on colorectal Cancer cells were studied in vitro. One hundred and ninety-seven Cantharidin-associated target genes and 480 critical genes implicated in the development of the illness were identified. Cantharidin significantly inhibited the proliferation and migration of HCT116 cells and promoted Apoptosis at certain concentrations. Patients on current therapy develop inherent and acquired resistance. Our study suggests that Cantharidin may play an anti-CRC role by modulating immune function.

Cantharidin; Colorectal cancer; Immune infiltration; Network pharmacology; Single-cell RNA sequencing.