Trehalose‐6‐phosphate synthase (TPS), an enzyme that hydrolyzes two glucose molecules to yield trehalose, plays a pivotal role in various physiological processes. In this study, we cloned the trehalose‐6‐phosphate synthase gene ( HvTPS) and investigated its expression patterns in various tissues and developmental stages in Heortia vitessoides Moore (Lepidoptera: Crambidae). HvTPS was highly expressed in the fat body and after pupation or before molting. We knocked down TPS in H. vitessoides by RNA interference and found that 3.0 μ g of ds HvTPS resulted in optimal interference at 24 h and 36 h post‐injection and caused a sharp decline in the survival rate during the 5th instar larval–pupal stage and obviously abnormal or lethal phenotypes. Additionally, compared to the controls, TPS activity and trehalose contents were significantly lower and the glucose content was significantly higher 24 h or 36 h after injection with 3.0 μg of ds HvTPS . Furthermore, the silencing of HvTPS suppressed the expression of six key genes in the chitin biosynthesis pathway and one key gene related to lipid catabolism. The expression levels of two genes associated with lipid biosynthesis were upregulated. These results strongly suggest that HvTPS is essential for the normal growth and development of H. vitessoides and provide a reference for further studies of the utility of key genes involved in chitin and lipid biosynthesis for controlling insect development.

Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for ordinal treatment settings, such as individualized dose finding, it is unclear how to use OWL. In this article, we propose a new technique for estimating ITR with ordinal treatments. In particular, we propose a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the resulting estimated ITR under certain conditions, and obtain the convergence and risk bound properties. Simulated examples and an application to a dataset from a type 2 diabetes mellitus observational study demonstrate the highly competitive performance of the proposed method compared to existing alternatives.

In this study, gastric cancer progression was correlated with the over-expression of erythropoietin-producing hepatocellular (Eph)A2 receptor and down-expression of epithelial cadherin (E-cadherin). Immunohistochemistry of EphA2 and E-cadherin were performed on these tumor samples from 165 primary lesions of gastric cancer. The results showed that expression of EphA2 was obviously increased in gastric cancer tissues (P < 0.01), which was positively correlated with the depth of cancer invasion, tumor-node-metastasis (TNM) stage and lymph node metastasis (P < 0.05). Meanwhile, the expression of E-cadherin was significantly reduced (P < 0.01), which was negatively correlated with the depth of cancer invasion, grade of tumor differentiation, TNM stage and lymph node metastasis (P < 0.05). The correlation between EphA2 and E-cadherin expression was negative (r = -0.198, P = 0.011). In conclusion, either the over-expression of EphA2 or the down-expression of E-cadherin is correlated with cancer progression and lymphogenous metastasis in gastric cancer, suggesting that both of them may play an important role in tumor progression and metastasis.

ObjectiveThe transcription factor 7-like 2 (TCF7L2) gene has been suggested to play an important role in the pathogenesis of cancer. However, the results have been inconsistent. In this study, we performed a meta-analysis to clarify the associations between TCF7L2 polymorphism and cancer risk.MethodsPublished literature from PubMed and EMBASE were retrieved. Pooled odds ratios (ORs) with 95% confidence interval (CIs) were calculated using fixed- or random-effects model.ResultsA total of 19 studies (14,814 cases and 33,856 controls) were identified for the analysis of the association between TCF7L2 polymorphism and cancer risk. The results showed that TCF7L2 polymorphism was associated with breast cancer (Homogeneous model: OR = 1.17, 95%CI = 1.02–1.35, I 2 = 21.8%, p for heterogeneity = 0.276; Heterogeneous model: OR = 1.11, 95%CI = 1.03–1.20, I 2 = 0.0%, p for heterogeneity = 0.543), prostate cancer (Homogeneous model: OR = 0.89, 95%CI = 0.84–0.96, I 2 = 0.0%, p for heterogeneity = 0.640; Heterogeneous model: OR = 0.89, 95%CI = 0.84–0.95, I 2 = 0.0%, p for heterogeneity = 0.871), and colon cancer (Heterogeneous model: OR = 1.15, 95%CI = 1.01–1.31, I 2 = 0.0%, p for heterogeneity = 0.658), but not with colorectal cancer, lung cancer, and ovarian cancer.ConclusionsThe present meta-analysis indicated that there were significantly associations between the TCF7L2 rs7903146 polymorphism and risk of breast, prostate and colon cancers, rather than colorectal cancer, lung cancer, and ovarian cancer.

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