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Background:Selenium-binding protein 1 (SELENBP1) expression is reduced markedly in many types of cancers and low SELENBP1 expression levels are associated with poor patient prognosis.Methods:SELENBP1 gene expression in head and neck squamous cell carcinoma (HNSCC) was analyzed with GEO dataset and characteristics of SELENBP1 expression in paraffin embedded tissue were summarized. Expression of SELENBP1 in nasopharyngeal carcinoma (NPC), laryngeal cancer, oral cancer, tonsil cancer, hypopharyngeal cancer and normal tissues were detected using immunohistochemistry, at last, 99 NPC patients were followed up more than 5 years and were analyzed the prognostic significance of SELENBP1.Results:Analysis of GEO dataset concluded that SELENBP1 gene expression in HNSCC was lower than that in normal tissue (P < 0.01), but there was no significant difference of SELENBP1 gene expression in different T-stage and N-stage (P > 0.05). Analysis of pathological section concluded that SELENBP1 in the majority of HNSCC is low expression and in cancer nests is lower expression than surrounding normal tissue, even associated with the malignant degree of tumor. Further study indicated the low SELENBP1 expression group of patients with NPC accompanied by poor overall survival and has significantly different comparing with the high expression group.Conclusion:SELENBP1 expression was down-regulated in HNSCC, but has no associated with T-stage and N-stage of tumor. Low expression of SELENBP1 in patients with NPC has poor over survival, so SELENBP1 could be a novel biomarker for predicting prognosis.

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This paper proposes a novel approach to the directional forecasting problem of short-term oil price changes. In this approach, the short-term oil price series is associated with incomplete fuzzy information, and a new fused genetic-fuzzy information distribution method is developed to process such a fuzzy incomplete information set; then a feasible coding method of multidimensional information controlling points is adopted to fit genetic-fuzzy information distribution to time series forecasting. Using the crude oil spot prices of West Texas Intermediate (WTI) and Brent as sample data, the empirical analysis results demonstrate that the novel fused genetic-fuzzy information distribution method statistically outperforms the benchmark of logistic regression model in prediction accuracy. The results indicate that this new approach is effective in direction accuracy.

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