Metabolic profiling-based data-mining for an effective chemical combination to induce apoptosis of cancer cells.

ABSTRACT: Green tea extract (GTE) induces apoptosis of cancer cells without adversely affecting normal cells. Several clinical trials reported that GTE was well tolerated and had potential anti-cancer efficacy. Epigallocatechin-3-O-gallate (EGCG) is the primary compound responsible for the anti-cancer effect of GTE; however, the effect of EGCG alone is limited. To identify GTE compounds capable of potentiating EGCG bioactivity, we performed metabolic profiling of 43 green tea cultivar panels by liquid chromatography-mass spectrometry (LC-MS). Here, we revealed the polyphenol eriodictyol significantly potentiated apoptosis induction by EGCG in vitro and in a mouse tumour model by amplifying EGCG-induced activation of the 67-kDa laminin receptor (67LR)/protein kinase B/endothelial nitric oxide synthase/protein kinase C delta/acid sphingomyelinase signalling pathway. Our results show that metabolic profiling is an effective chemical-mining approach for identifying botanical drugs with therapeutic potential against multiple myeloma. Metabolic profiling-based data mining could be an efficient strategy for screening additional bioactive compounds and identifying effective chemical combinations.

SUBMITTER: Kumazoe M 

PROVIDER: S-EPMC4379465 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature