An Integrated Bioinformatic Analysis of the S100 Gene Family for the Prognosis of Colorectal Cancer.

BACKGROUND: S100 family genes exclusively encode at least 20 calcium-binding proteins, which possess a wide spectrum of intracellular and extracellular functions in vertebrates. Multiple lines of evidences suggest that dysregulated S100 proteins are associated with human malignancies including colorectal cancer (CRC). However, the diverse expression patterns and prognostic roles of distinct S100 genes in CRC have not been fully elucidated. METHODS: In the current study, we analyzed the mRNA expression levels of S100 family genes and proteins and their associations with the survival of CRC patients using the Oncomine analysis and GEPIA databases. Expressions and mutations of S100 family genes were analyzed using the cBioPortal, and protein-protein interaction (PPI) networks of S100 proteins and their mutation-related coexpressed genes were analyzed using STRING and Cytoscape. RESULTS: We observed that the mRNA expression levels of S100A2, S100A3, S100A9, S100A11, and S100P were higher and the level of S100B was lower in CRC tissues than those in normal colon mucosa. A high S100A10 levels was associated with advanced-stage CRC. Results from GEPIA database showed that highly expressed S100A1 was correlated with worse overall survival (OS) and disease-free survival (DFS) and that overexpressions of S100A2 and S100A11 were associated with poor DFS of CRC, indicating that S100A1, S100A2, and S100A11 are potential prognostic markers. Unexpectedly, most of S100 family genes showed no significant prognostic values in CRC. CONCLUSIONS: Our findings, though still need to be ascertained, offer novel insights into the prognostic implications of the S100 family in CRC and will inspire more clinical trials to explore potential S100-targeted inhibitors for the treatment of CRC.

Development of novel in silico model for developmental toxicity assessment by using naïve Bayes classifier method.

Toxicological testing associated with developmental toxicity endpoints are very expensive, time consuming and labor intensive. Thus, developing alternative approaches for developmental toxicity testing is an important and urgent task in the drug development filed. In this investigation, the naïve Bayes classifier was applied to develop a novel prediction model for developmental toxicity. The established prediction model was evaluated by the internal 5-fold cross validation and external test set. The overall prediction results for the internal 5-fold cross validation of the training set and external test set were 96.6% and 82.8%, respectively. In addition, four simple descriptors and some representative substructures of developmental toxicants were identified. Thus, we hope the established in silico prediction model could be used as alternative method for toxicological assessment. And these obtained molecular information could afford a deeper understanding on the developmental toxicants, and provide guidance for medicinal chemists working in drug discovery and lead optimization.

Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity.

Prediction of drug candidates for mutagenicity is a regulatory requirement since mutagenic compounds could pose a toxic risk to humans. The aim of this investigation was to develop a novel prediction model of mutagenicity by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test sets. For comparison, the recursive partitioning classifier prediction model was also established and other various reported prediction models of mutagenicity were collected. Among these methods, the prediction performance of naïve Bayes classifier established here displayed very well and stable, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set I set were 89.1±0.4% and 77.3±1.5%, respectively. The concordance of the external test set II with 446 marketed drugs was 90.9±0.3%. In addition, four simple molecular descriptors (e.g., Apol, No. of H donors, Num-Rings and Wiener) related to mutagenicity and five representative substructures of mutagens (e.g., aromatic nitro, hydroxyl amine, nitroso, aromatic amine and N-methyl-N-methylenemethanaminum) produced by ECFP_14 fingerprints were identified. We hope the established naïve Bayes prediction model can be applied to risk assessment processes; and the obtained important information of mutagenic chemicals can guide the design of chemical libraries for hit and lead optimization.

[Development and effectiveness evaluation of time-resolved fluoroimmunoassay kit for detection of Schistosoma japonicum].

OBJECTIVE: To develop a kit of time-resolved fluoroimmunoassay (TRFIA) for detection of Schistosoma japonicum protein SjP38, and evaluate its effectiveness. METHODS: The anti 9G7 SjP38 monoclonal antibody was used as the capture anti-body coated with 96-hole plate, and the Eu3+ labeled 1A6 monoclonal antibody was used as the detection antibody to establish the TRFIA SjP38 kit. In addition, the accuracy, sensitivity, precision, stability and coincidence rate to pathogenic diagnosis of the kit were evaluated. RESULTS: This established kit possessed high accuracy, wide linear range from 2 to 1 250 ng/ml, high sensitivity with the minimum detectable concentration of 0.14 ng/ml, and good precision (the coefficient variation of the intra- and inter-assay were 3.6% to 4.6% and 5.1% to 6.7%, respectively). The stability tests showed that the reagents could be stable for six months at 4 ℃, 7 d at 37 ℃. The positive and negative corresponding rates to the pathogen detection method were 95% and 100% respectively. CONCLUSIONS: All the performance and detection indicators of the kit have reached the requirements of clinical test, but its clinical application still needs further validation.

In silico prediction of drug-induced myelotoxicity by using Naïve Bayes method.

Drug-induced myelotoxicity usually leads to decrease the production of platelets, red cells, and white cells. Thus, early identification and characterization of myelotoxicity hazard in drug development is very necessary. The purpose of this investigation was to develop a prediction model of drug-induced myelotoxicity by using a Naïve Bayes classifier. For comparison, other prediction models based on support vector machine and single-hidden-layer feed-forward neural network  methods were also established. Among all the prediction models, the Naïve Bayes classification model showed the best prediction performance, which offered an average overall prediction accuracy of [Formula: see text] for the training set and [Formula: see text] for the external test set. The significant contributions of this study are that we first developed a Naïve Bayes classification model of drug-induced myelotoxicity adverse effect using a larger scale dataset, which could be employed for the prediction of drug-induced myelotoxicity. In addition, several important molecular descriptors and substructures of myelotoxic compounds have been identified, which should be taken into consideration in the design of new candidate compounds to produce safer and more effective drugs, ultimately reducing the attrition rate in later stages of drug development.

Monoclonal antibodies targeting basic fibroblast growth factor inhibit the growth of B16 melanoma in vivo and in vitro.

Up-regulated basic fibroblast growth factor (bFGF or FGF-2) plays an important role in the development and metastasis of melanoma; therefore, neutralizing antibodies to bFGF may suppress melanoma growth. In this study, we have developed three monoclonal antibodies against bFGF (anti-bFGF mAbs), which display remarkable anti-tumor and anti-angiogenic effects in vitro and in vivo. Anti-bFGF mAbs significantly inhibit the proliferation and induce apoptosis of B16 cells, and show inhibitory effects on the migration of B16F10 cells and the tube formation of human umbilical vein endothelial cells (HUVECs) in vitro. Treatment of B16 melanoma spheroids with anti-bFGF mAbs in vivo results in significant reduction in tumor size and prolonged survival time of animals. Moreover, TUNEL (terminal transferase dUTP nick end labeling) assay and CD31 staining confirmed the increase of apoptosis and decrease of intratumoral microvessel density in tumor sections from animals treated with anti-bFGF mAbs. Our data indicate that anti-bFGF mAbs are potential therapeutic candidates for melanoma therapy by effectively suppressing the melanoma growth through inhibition of angiogenesis and induction of apoptosis in the tumor.

[Ecological benefits of planting winter rapeseed in western China].

To evaluate the ecological benefits of popularizing winter rapeseed planting in western China, a wind tunnel simulation test was conducted with four kinds of farmland surface, i.e., winter rapeseed, winter wheat, wheat stubble, and bare field just after spring sowing, collected from west Gansu in April. The results showed that winter rapeseed surface had a roughness of 4.08 cm and a threshold wind velocity as high as 14 m x s(-1), being more effective in blown sand control than the other three surfaces. Under the same experimental conditions, the wind erosion modulus and sand transportation rate of winter rapeseed surface were only 4.1% and 485% of those of the bare field just after spring sowing, and the losses of soil organic matter, alkali-hydrolyzed N, available P and K, catalase, urease, alkaline phosphatase, invertase, and microbes of winter rapeseed surface due to wind erosion were only 1.4%, 5.1%, 1.6%, 2.7%, 9.7%, 3.6%, 6.3%, 6.7% and 1.5% of those of the bare field, respectively. It was suggested that popularizing winter rapeseed planting in west China could control wind erosion, retain soil water and nutrients, increase multicropping index, and improve economic benefits of farmland. In addition, it could benefit the regional desertification control and ecological environment improvement.