Objective To investigate the role of preoperative peripheral blood neutrophil to lymphocyte ratio (NLR),platelet to lymphocyte ratio (PLR),prognostic nutritional index (PNI) in the prognosis of patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after radical operation.Methods This is a retrospective study,involving 426 surgically resected hepatitis B related hepatocellular carcinoma cases in a single center from 2003 to 2012.Results Kaplan-Meier analysis showed patients in NLR ≤ 1.62 group achieve higher rate of recurrence-free and overall survival than that in the NLR ＞ 1.62 group,the difference was statistically significant (P ＜ 0.005);Also PNI ＞ 49.42 group showed higher rate of overall survival significantly than PNI≤49.42 group (P ＜ 0.005).The results of Cox regression multivariate analysis further suggested that both NLR ＞ 1.62 (HR 1.74,P =0.007) and PNI ≤49.42 (HR 0.70,P =0.021) were independent risk factors for overall survival,NLR ＞ 1.62 (HR 1.45,P =0.03) was also an independent risk factor for recurrence-free survival.Conclusion The preoperative NLR and PNI may be independent risk factors for prognosis of patients with HBV-related HCC after radical operation.

Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly sequenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred,to improve the accuracy of prediction by combining multiple sources of evidence.SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http://bio.scu.edu.cn/SCGPred/.

Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly sequenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCG-Pred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCG-Pred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http://bio.scu.edu.cn/SCGPred/.
Algorithms ; Chromosome Mapping ; methods ; Computational Biology ; methods ; Exons ; genetics ; Genes, Fungal ; genetics ; Genome, Fungal ; Genome, Human ; Humans ; Reproducibility of Results ; Software ; Ustilago ; genetics