An Evidence-Based Perspective of Camellia Sinensis (Green Tea) for Cancer Patients

Abstract

Abstract Camellia sinensis, Theaceae (green tea) and its major polyphenolic constituent, (-)-epigallocatechin-3-gallate (EGCG) have been extensively studied for their preventive activity against cancer. Studies in animal models of carcinogenesis have shown that green tea and EGCG can inhibit the development of cancer in various organ systems at the initiation, promotion and progression stages. This inhibition of tumorigenesis is associated with decreased cell proliferation, increased apoptosis, and inhibition of angiogenesis. Various mechanisms of action have been proposed based on studies with human cell lines and cell-free systems. These mechanisms include induction of oxidative stress, inhibition of key enzyme systems, inhibition of growth factor signaling and others. Few of these mechanisms have been clearly demonstrated to play a role in the prevention of cancer by tea in vivo. Although a large number of epidemiological studies have been conducted on the relationship between tea consumption and cancer risk, the results remain mixed. Very few human intervention studies have been conducted, yet at least one demonstrated that tea has promise for the prevention of prostate cancer. Further controlled intervention studies are needed to clearly test the cancer preventive effects of green tea in high risk human subjects. Additionally, carefully designed, mechanism-based animal model studies in conjunction with biochemical and immunohistochemical studies of human samples will be critical for unraveling the key underlying mechanisms of action. Although there is promise for green tea as a cancer chemopreventive agent, considerable work is needed to fully realize its potential impact on public health.KeywordsDextran Sulfate SodiumAberrant Crypt FocusHuman Intervention StudyCancer Preventive EffectCancer Preventive ActivityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.