Plasma mRNA expression levels of BRCA1 and TS as potential predictive biomarkers for chemotherapy in gastric cancer

OBJECTIVE: Personalized chemotherapy based on predictive biomarkers can maximize efficacy. However, tumor tissue obtained at the time of initial diagnosis will not reflect genetic alterations observed at the time of disease progression. We have examined whether plasma mRNA levels can be a surrogate for tumor levels in predicting chemosensitivity. METHODS: In 150 gastric cancer patients, mRNA levels of BRCA1 and TS were assessed in plasma and paired tumor tissue. The Mann-Whitney U-test was used to compare mRNA expression levels between tumor samples exhibiting in vitro sensitivity or resistance to docetaxel and pemetrexed. All statistical tests were two-sided. RESULTS: There were significant correlations between plasma and tumor mRNA levels of BRCA1 (rho = 0.696, P < 0.001) and TS (rho = 0.620, P < 0.001). BRCA1 levels in plasma (docetaxel-sensitive: 1.25; docetaxel-resistant: 0.50, P < 0.001) and tumor (docetaxel-sensitive: 8.81; docetaxel-resistant: 4.88, P < 0.001) were positively associated with docetaxel sensitivity. TS levels in plasma (pemetrexed-sensitive: 0.90; pemetrexed-resistant: 1.82, P < 0.001) and tumor (pemetrexed-sensitive: 6.56; pemetrexed-resistant: 16.69, P < 0.001) were negatively associated with pemetrexed sensitivity. CONCLUSIONS: Plasma mRNA expression levels mirror those in the tumor and may have a promising role as potential predictive biomarkers for chemotherapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-014-0355-2) contains supplementary material, which is available to authorized users

An Effective Hybrid Recommender Using Metadata-based Conceptualization and Temporal Semantics

Modern recommender systems target the satisfaction of the end user through the personalization techniques that collects the history of the user’s navigation. But the sole dependency on user profile based on navigation alone cannot promise the quality of recommendations because of the lack of semantics of various aspect such as demographics of the user, time of usage, concept of need etc in the processing. Though the literature provides many techniques to conceptualize the process makes high computational complexity because of the content data considered as input information. In this paper a hybrid recommender framework is developed that considers Meta data based conceptual semantics and the temporal patterns on top of the history of the usage. This framework also includes an online process that identifies the conceptual drift of the usage dynamically. The experimental results shown the effectiveness of the proposed framework when compared to the existing modern recommenders also indicate that the proposed model can resolve a cold start problem yet accurate suggestions reducing computational complexity