Preliminary investigation of the anti‐colon cancer activity of cucurbitacin C from cucumber: A network pharmacological study and experimental validation

Abstract

Colon cancer (CC) is a prevalent malignant tumor of the gastrointestinal tract and ranks among the leading causes of death in cancer patients worldwide. Cucurbitacin, a group of tetracyclic triterpenoids widely found in Cucurbitaceae plants, exhibits diverse pharmacological activities. However, the pharmacological activity of cucurbitacin C (CuC), a cucurbitacin derivative exclusively present in cucumbers, remains unclear. To investigate CuC's action targets and potential targets for CC, we utilized a database to obtain relevant information. Additionally, we identified differentially expressed genes in CC patients from The Cancer Genome Atlas and discovered 64 crossover targets formed through the intersection of drug‐disease targets. Through the construction of a protein‐protein interaction network, we identified 20 central genes. Enrichment analysis revealed that the cross‐targets were significantly enriched in four signaling pathways, namely, the PI3K‐Akt, Ras, MAPK, and IL‐17 signaling pathways. Correlation and expression analysis of the enriched central genes led to the identification of 7 key targets, among which matrix metalloproteinases (MMP)‐1, MMP‐3, and MMP‐13 demonstrated a significant association with poor prognosis in patients. Furthermore, we validated the network pharmacology results with in vitro and in vivo experiments. CuC can effectively inhibit the proliferation and migration of HCT‐116. CuC also can suppress the colon tumor growth in vivo, while reducing the expression of placental growth factor, MMP‐1, MMP‐3, MMP‐9, and MMP‐13 both in vitro and in vivo, consistent with the predicted results. Together, our results demonstrated that CuC holds promise as a potential agent for CC treatment.

Extraction, purification and anticancer activity studies on triterpenes from pomegranate peel

“…31 As a biological analysis method, network pharmacology can effectively collect and organize information on related proteins and targets, and provide a new method for the mechanistic study of traditional Chinese medicines. 32 Liu et al used network pharmacology to predict relevant targets of Gynostemma chamomin against anti-renal cell carcinoma. 33 It was found that Gynostemma chamomin induced the apoptosis of renal cell carcinoma through the PI3K/AKT/mTOR pathway.…”