The use of integrated text mining and protein-protein interaction approach to evaluate the effects of combined chemotherapeutic and chemopreventive agents in cancer therapy

Abstract

Combining chemotherapeutic (CT) and chemopreventive (CP) agents for cancer treatment is controversial, and the issue has not yet been conclusively resolved. In this study, by integrating text mining and protein-protein interaction (PPI), the combined effects of these two kinds of agents in cancer treatment were investigated. First, text mining was performed by the Pathway Studio database to study the effects of various agents (CP and CT) on cancer-related processes. Then, each group’s most important hub genes were obtained by calculating different centralities. Finally, the results of in silico analysis were validated by examining the combined effects of hesperetin (Hst) and vincristine (VCR) on MCF-7 cells. In general, the results of the in silico analysis revealed that the combination of these two kinds of agents could be useful for treating cancer. However, the PPI analysis revealed that there were a few important proteins that could be targeted for intelligent therapy while giving treatment with these agents. In vitro experiments confirmed the results of the in silico analysis. Also, Hst and VCR had good harmony in modulating the hub genes obtained from the in silico analysis and inducing apoptosis in the MCF-7 cell line.