Polymorphisms and a Haplotype in Heparanase Gene Associations with the Progression and Prognosis of Gastric Cancer in a Northern Chinese Population

Background: Human heparanase plays an important role in cancer development and single nucleotide polymorphisms (SNPs) in the heparanase gene (HPSE) have been shown to be correlated with gastric cancer. The present study examined the associations between individual SNPs or haplotypes in HPSE and susceptibility, clinicopathological parameters and prognosis of gastric cancer in a large sample of the Han population in northern China. Methodology/Principal Findings: Genomic DNA was extracted from formalin-fixed, paraffin-embedded normal gastric tissue samples from 404 patients and from blood from 404 healthy controls. Six SNPs were genotyped by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. A chi-square (x2) test and unconditional logistic regression were used to analyze the risk of gastric cancer; a Log-rank test and Cox proportional hazards model were used to produce survival analysis and a Kaplan-Meier method was used to map survival curves. The mean genotyping success rates were more than 99 % in both groups. Haplotype CA in the block composed of rs11099592 and rs4693608 had a greater distribution in the group of Borrmann types 3 and 4 (P = 0.037), the group of a greater number of lymph node metastases (N3 vs N0 group, P = 0.046), and moreover was correlated to poor survival (CG vs CA: HR = 0.645, 95%CI: 0.421–0.989, P = 0.044). In addition, genotypes rs4693608 AA and rs4364254 TT were associated with poor survival (P = 0.030, HR = 1.527, 95%CI: 1.042–2.238 for rs4693608 AA; P = 0.013, HR = 1.546, 95%CI: 1.096–2.181 for rs4364254 TT). There were n

A sheep pangenome reveals the spectrum of structural variations and their effects on tail phenotypes

Structural variations (SVs) are a major contributor to genetic diversity and phenotypic variations, but their prevalence and functions in domestic animals are largely unexplored. Here we generated high-quality genome assemblies for 15 individuals from genetically diverse sheep breeds using Pacific Biosciences (PacBio) high-fidelity sequencing, discovering 130.3 Mb nonreference sequences, from which 588 genes were annotated. A total of 149,158 biallelic insertions/deletions, 6531 divergent alleles, and 14,707 multiallelic variations with precise breakpoints were discovered. The SV spectrum is characterized by an excess of derived insertions compared to deletions (94,422 vs. 33,571), suggesting recent active LINE expansions in sheep. Nearly half of the SVs display low to moderate linkage disequilibrium with surrounding single-nucleotide polymorphisms (SNPs) and most SVs cannot be tagged by SNP probes from the widely used ovine 50K SNP chip. We identified 865 population-stratified SVs including 122 SVs possibly derived in the domestication process among 690 individuals from sheep breeds worldwide. A novel 168-bp insertion in the 5' untranslated region (5' UTR) of HOXB13 is found at high frequency in long-tailed sheep. Further genome-wide association study and gene expression analyses suggest that this mutation is causative for the long-tail trait. In summary, we have developed a panel of high-quality de novo assemblies and present a catalog of structural variations in sheep. Our data capture abundant candidate functional variations that were previously unexplored and provide a fundamental resource for understanding trait biology in sheep

Karyological studies on seven cephalopods

Karyological studies were made on the embryos of seven cephalopods using chopping method. Two sepiids (Sepia esculenta and Sepia lycidas) and three loliginids (Sepioteuthis lessoniana, Heterololigo bleekeri and Photololigo edulits) were all 2n = 92). Their karyotypes and total length of chromosomes were slightly different from each other. Two octopuses (Octopus ocellatus and O. vulgaris) were both 2n=60. Their karyotypes and total length of chromosomes were, however, remarkably different from each other

Tungsten Phosphide Microsheets In‐Situ Grown on Carbon Fiber as Counter Electrode Catalyst for Efficient Dye‐Sensitized Solar Cells

Abstract The development of low‐cost, green, and pollution‐free counter electrode materials with high catalytic activity plays a critical role in improving the photovoltaic performance of dye‐sensitized solar cells (DSSCs). In recent years, transition metal phosphides have been widely used in DSSCs due to their outstanding catalytic activity and stability. Herein, a novel binary phosphide is immobilized on carbon paper (CP) by a two‐step strategy. This strategy involves the preparation of WO3 precursor by the hydrothermal method, and synthesis of tungsten phosphide (WP) with the vapor deposition method, which finally leads to the uniform dispersion of WP on carbon paper. The acquired WP/CP counter electrode demonstrates high electrical conductivity and prefect catalytic ability for reducing triiodide, and the DSSCs assembled with WP/CP counter electrode achieve a high‐power conversion efficiency of 10.29%, which is superior to that of the Pt‐based (7.34%). These findings illustrate that the WP microsheets in‐situ grown on carbon paper are a potential candidate to replace Pt as an economical and efficient counter electrode for DSSCs

Which Is a More Accurate Predictor in Colorectal Survival Analysis? Nine Data Mining Algorithms <em>vs.</em> the TNM Staging System

<div><h3>Objective</h3><p>Over the past decades, many studies have used data mining technology to predict the 5-year survival rate of colorectal cancer, but there have been few reports that compared multiple data mining algorithms to the TNM classification of malignant tumors (TNM) staging system using a dataset in which the training and testing data were from different sources. Here we compared nine data mining algorithms to the TNM staging system for colorectal survival analysis.</p> <h3>Methods</h3><p>Two different datasets were used: 1) the National Cancer Institute's Surveillance, Epidemiology, and End Results dataset; and 2) the dataset from a single Chinese institution. An optimization and prediction system based on nine data mining algorithms as well as two variable selection methods was implemented. The TNM staging system was based on the 7^th edition of the American Joint Committee on Cancer TNM staging system.</p> <h3>Results</h3><p>When the training and testing data were from the same sources, all algorithms had slight advantages over the TNM staging system in predictive accuracy. When the data were from different sources, only four algorithms (logistic regression, general regression neural network, Bayesian networks, and Naïve Bayes) had slight advantages over the TNM staging system. Also, there was no significant differences among all the algorithms (p>0.05).</p> <h3>Conclusions</h3><p>The TNM staging system is simple and practical at present, and data mining methods are not accurate enough to replace the TNM staging system for colorectal cancer survival prediction. Furthermore, there were no significant differences in the predictive accuracy of all the algorithms when the data were from different sources. Building a larger dataset that includes more variables may be important for furthering predictive accuracy.</p> </div

AUC^a calculated by testing prediction models on CMU-SO^b.

<p>AUC^a: area under the receiver operating characteristic curves.</p><p>CMU-SO^b: A dataset collects clinical information from Department of Surgical Oncology at the First Hospital of China Medical University.</p><p>variable selection^c: the variable selection method which has the highest AUC.</p><p>Global^d: without variable selection.</p><p>GA^e: variable selection using genetic algorithms.</p><p>BSFS^f: variable selection using backward stepwise feature selection.</p>*<p>: median AUC of 15 tests.</p>**<p>: comparing the AUC of prediction models with TNM staging system.</p

Associations between haplotype frequencies of the six SNPs in HPSE and clinicopathological parameters (n = 404).

<p>*Two-sided χ² test, each haplotype compared with all other haplotypes.</p>#<p>Statistically significant (P<0.05).</p