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Clinical relevance of the inhibitory effect of green tea catechins [GTCs] on prostate cancer progression in combination with molecular profiling of catechin-resistant tumors: an integrated view

100%

Bettuzzi S. , Rizzi F. , Belloni L.

Polish Journal of Veterinary Sciences

2007

tom 10

nr 1

Prostate cancer (CaP) is a fast-growing health and social problem already representing the second leading cause of cancer-related death among men in Western countries. Lifestyle-related factors and diet are major contributors for CaP promotion. Because of unfavourable prognosis of extra-prostatic CaP, prevention is considered the best approach to fight it at present time. Green Tea Catechins (GTCs) were proven effective at inhibiting cancer growth in several laboratory studies. We recently performed a pilot clinical trial in HG-PIN subjects showing that only 1/30 tumour was diagnosed in subjects treated for 1 year with 600 mg/die GTCs, while 9/30 cancers were found in placebo-treated men. CaP is an elusive disease, whose biological behaviour is difficult to predict. We have recently described and validated a RT-qPCR method based on a 8-genes signature that significantly discriminated benign tissue from CaP in both humans and TRAMP mice spontaneously developing CaP. In the animal model, also GTCs-resistant CaP was significantly discriminated from GTCs-sensitive CaP, i.e. responding to GTCs administration. Preliminary experiments in our laboratory have shown that this method can be successfully applied to a single tissue needle biopsy specimen in humans. The combination of these results may be of particular significance on the field. In fact, GTCs treatment for men at high risk of CaP as first line prevention therapy in combination with the 8-genes signature profiling in tissue needle biopsies for real time monitoring of patient's response might importantly change, in the near future, the clinical managing of this highly diffuse malignancy.

Machine learning techniques combined with dose profiles indicate radiation response biomarkers

84%

Papiez A. , Badie C. , Polanska J.

International Journal of Applied Mathematics and Computer Science

2019

tom Vol. 29, no. 1

169--178

The focus of this research is to combine statistical and machine learning tools in application to a high-throughput biological data set on ionizing radiation response. The analyzed data consist of two gene expression sets obtained in studies of radiosensitive and radioresistant breast cancer patients undergoing radiotherapy. The data sets were similar in principle; however, the treatment dose differed. It is shown that introducing mathematical adjustments in data preprocessing, differentiation and trend testing, and classification, coupled with current biological knowledge, allows efficient data analysis and obtaining accurate results. The tools used to customize the analysis workflow were batch effect filtration with empirical Bayes models, identifying gene trends through the Jonckheere–Terpstra test and linear interpolation adjustment according to specific gene profiles for multiple random validation. The application of non-standard techniques enabled successful sample classification at the rate of 93.5% and the identification of potential biomarkers of radiation response in breast cancer, which were confirmed with an independent Monte Carlo feature selection approach and by literature references. This study shows that using customized analysis workflows is a necessary step towards novel discoveries in complex fields such as personalized individual therapy.

Antimicrobial resistance and virulence factor gene profiles of Enterococcus spp. isolated from giant panda oral cavities

84%

Zhong R. , Zhou Z. , Liu H. , Zhong Z. , Peng G.

Journal of Veterinary Research

2021

tom 65

nr 2

Exploring cumulus-oocyte-complex-oviductal cell interactions: gene profiling in the bovine oviduct

84%

Gabler C. , Odau S. , Muller K. , Schon J. , Bondzio A. , Einspanier R.

tom 59

nr 9

29-42