Breast cancer brain metastasis is resistant to therapy and a particularly poor prognostic feature in patient survival. Altered metabolism is a common feature of cancer cells but little is known as to what metabolic changes benefit breast cancer brain metastases. We found that brain-metastatic breast cancer cells evolved the ability to survive and proliferate independent of glucose due to enhanced gluconeogenesis and oxidations of glutamine and branched chain amino acids, which together sustain the non-oxidative pentose pathway for purine synthesis. Silencing expression of fructose-1,6-bisphosphatases (FBPs) in brain metastatic cells reduced their viability and improved the survival of metastasis-bearing immunocompetent hosts. Clinically, we showed that brain metastases from human breast cancer patients expressed higher levels of FBP and glycogen than the corresponding primary tumors. Together, our findings identify a critical metabolic condition required to sustain brain metastasis, and suggest that targeting gluconeogenesis may help eradicate this deadly feature in advanced breast cancer patients.

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Cancer is a serious health issue worldwide. Traditional treatment methods focus on killing cancer cells by using anticancer drugs or radiation therapy, but the cost of these methods is quite high, and in addition there are side effects. With the discovery of anticancer peptides, great progress has been made in cancer treatment. For the purpose of prompting the application of anticancer peptides in cancer treatment, it is necessary to use computational methods to identify anticancer peptides (ACPs). In this paper, we propose a sequence-based model for identifying ACPs (SAP). In our proposed SAP, the peptide is represented by 400D features or 400D features with g-gap dipeptide features, and then the unrelated features are pruned using the maximum relevance-maximum distance method. The experimental results demonstrate that our model performs better than some existing methods. Furthermore, our model has also been extended to other classifiers, and the performance is stable compared with some state-of-the-art works.

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Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.

Germinated adlay had higher free and total phenolic and flavonoid contents, and antioxidant activities than ungerminated adlay. This study indicates that germinated adlay could be a promising functional food, more suitable for human consumption. © 2017 Society of Chemical Industry.