• Asian-Australasian Journal of Animal Sciences
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• v.4 no.4
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• pp.305-328
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• 1991

Pig breeding programs have been very successful in the improvement of animals by the simple expedient of focusing on a few traits of economic importance, particularly growth efficiency and leanness. Further reductions in leanness may become more difficult to achieve, due to reduced genetic variation, and less desirable, due to adverse correlated effects on meat and eating quality. Best linear unbiased prediction (BLUP) of breeding values makes possible the incorporation of data from many sources and increases the value of including traits such as sow performance in the breeding objective. Advances in technology, such as electronic animal identification, electronic feeders, improved ultrasonic scanners and automated data capture at slaughter houses, increase the number of sources of information that can be included in breeding value predictions. Breeding program structures will evolve to reflect these changes and a common structure is likely to be several or many breeding farms genetically linked by A.i., with data collected on a number of traits from many sources and integrated into a single breeding value prediction using BLUP. Future developments will include the production of a porcine gene map which may make it possible to identify genes controlling economically valuable traits, such as those for litter size in the Meishan, and introgress them into nucleus populations. Genes identified from the gene map or from other sources will provide insight into the genetic basis of performance and may provide the raw material from which transgenic programs will channel additional genetic variance into nucleus populations undergoing selection.

• ETRI Journal
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• v.41 no.3
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• pp.358-370
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• 2019

The main aim of this study is to select the optimal set of genes from microarray cancer datasets that contribute to the prediction of specific cancer types. This study proposes the enhancement of the feature selection filter algorithm based on Joe's normalized mutual information and its use for gene selection. The proposed algorithm is implemented and evaluated on seven benchmark microarray cancer datasets, namely, central nervous system, leukemia (binary), leukemia (3 class), leukemia (4 class), lymphoma, mixed lineage leukemia, and small round blue cell tumor, using five well-known classifiers, including the naive Bayes, radial basis function network, instance-based classifier, decision-based table, and decision tree. An average increase in the prediction accuracy of 5.1% is observed on all seven datasets averaged over all five classifiers. The average reduction in training time is 2.86 seconds. The performance of the proposed method is also compared with those of three other popular mutual information-based feature selection filters, namely, information gain, gain ratio, and symmetric uncertainty. The results are impressive when all five classifiers are used on all the datasets.

• Microbiology and Biotechnology Letters
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• v.46 no.3
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• pp.300-312
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• 2018

Whole-genome sequencing of the wood-rotting fungus, Flammulina fennae, was carried out to identify carbohydrate-active enzymes (CAZymes). De novo genome assembly (31 kmer) of short reads by next-generation sequencing revealed a total genome length of 32,423,623 base pairs (39% GC). A total of 11,591 gene models in the assembled genome sequence of F. fennae were predicted by ab initio gene prediction using the AUGUSTUS tool. In a genome-wide comparison, 6,715 orthologous groups shared at least one gene with F. fennae and 10,667 (92%) of 11,591 genes for F. fennae proteins had orthologs among the Dikarya. Additionally, F. fennae contained 23 species-specific genes, of which 16 were paralogous. CAZyme identification and annotation revealed 513 CAZymes, including 82 auxiliary activities, 220 glycoside hydrolases, 85 glycosyltransferases, 20 polysaccharide lyases, 57 carbohydrate esterases, and 45 carbohydrate binding-modules in the F. fennae genome. The genome information of F. fennae increases the understanding of this basidiomycete fungus. CAZyme gene information will be useful for detailed studies of lignocellulosic biomass degradation for biotechnological and industrial applications.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6281-6286
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• 2013

Background: To further investigate the molecular basis of lung cancer development, we utilize a microarray to identify differentially expressed genes associated with various TNM stages of adenocarcinoma, a subtype with increasing incidence in recent years in China. Methods: A 35K oligo gene array, covering about 25,100 genes, was used to screen differentially expressed genes among 90 tumor samples of lung adenocarcinoma in various TNM stages. To verify the gene array data, three genes (Zimp7, GINS2 and NAG-1) were confirmed by real-time RT-PCR in a different set of samples from the gene array. Results: First, we obtained 640 differentially expressed genes in lung adenocarcinomas compared to the surrounding normal lung tissues. Then, from the 640 candidates we identified 10 differentially expressed genes among different TNM stages (Stage I, II and IIIA), of which Zimp7, GINS2 and NAG-1 genes were first reported to be present at a high level in lung adenocarcinoma. The results of qRT-PCR for the three genes were consistent with those from the gene array. Conclusions: We identified 10 candidate genes associated with different TNM stages in lung adenocarcinoma in the Chinese population, which should provide new insights into the molecular basis underlying the development of lung adenocarcinoma and may offer new targets for the diagnosis, therapy and prognosis prediction.

• Korean Journal of Biological Psychiatry
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• v.13 no.4
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• pp.299-304
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• 2006

Objectives : The purpose of the present study was to investigate whether the TaqI A polymorphism of dopamine receptor D2 gene(DRD2) is associated with Tourette syndrome(TS) and chronic motor tic disorder(CMT) in Korean population. Methods : DRD2 TaqI A RFLP genotyping was carried out with DNA extracted from blood samples of 75 patients with tic disorders(47 with TS and 28 with CMT) and 90 healthy subjects. Genotype and allelic frequencies for the DRD2 gene polymorphisms of the tic disorder group as a whole were compared to those of the control group. Separating the TS group, thereafter, the frequency of genotypes and alleles were compared to those of the controls. Results : The results demonstrated that genotype and allele distributions for the DRD2 gene polymorphism in the tic disorder as a whole, TS, and control groups were not significantly different. Conclusion : No association was found for DRD2 gene, TS and CMT. The data suggest that DRD2 gene may not be a useful marker for the prediction of the susceptibility of tic disorder.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5229-5235
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• 2016

Prostate cancer (PCa) remains one of the most widespread and perplexing of all human malignancies. Assessment of gene expression is thought to have an important impact on cancer diagnosis, prognosis and therapeutic decisions. In this context, we explored combined expression of PCa related target genes AMACR and PCA3 in 126 formalin fixed paraffin embedded prostate tissues (FFPE) from Moroccan patients, using quantitative real time reverse transcription-PCR (RT-qPCR). This quantification required data normalization accomplished using stably expressed reference genes (RGs). A panel of twelve RG was assessed, data being analyzed using GenEx V6 based on geNorm, NormFinder and statistical methods. Accordingly, the hnRNP A1 gene was identified and selected as the most stably expressed RG for reliable and accurate gene expression quantification in prostate tissues. The ratios of both PCA3 and AMACR gene expression relative to that of the hnRNP A1 gene were calculated and the performance of each target gene for PCa diagnosis was evaluated using receiver-operating characteristics. PCA3 and AMACR mRNA quantification based on RT-qPCR may prove useful in PCa diagnosis. Of particular interesting, combining PCA3 and AMACR quantification improved PCa prediction by increasing sensitivity with retention of good specificity.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.185-190
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• 2014

Influence of loneliness on human survival has been established epidemiologically, but genomic research remains undeveloped. We identified 34 loneliness-associated genes which were statistically significant for high-lonely and low-lonely individuals. With the univariate Cox proportional hazards regression model, we obtained corresponding regression coefficients for loneliness-associated genes fo individual cancer patients. Furthermore, risk scores could be generated with the combination of gene expression level multiplied by corresponding regression coefficients of loneliness-associated genes. We verified that high-risk score cancer patients had shorter mean survival time than their low-risk score counterparts. Then we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85 and 191), significantly separable by log-rank test with hazard ratios (HR) >1 and p-values <0.0001 (HR=2.94, 3.82, and 1.78). Moreover, we validated the loneliness-associated gene signature in bone cancer (HR=5.10, p-value=4.69e-3), lung cancer (HR=2.86, p-value=4.71e-5), ovarian cancer (HR=1.97, p-value=3.11e-5), and leukemia (HR=2.06, p-value=1.79e-4) cohorts. The last lymphoma cohort proved to have an HR=3.50, p-value=1.15e-7. Loneliness-associated genes had good survival prediction for cancer patients, especially bone cancer patients. Our study provided the first indication that expression of loneliness-associated genes are related to survival time of cancer patients.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4153-4158
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• 2014

Background: This study was aimed to establish a novel method to simultaneously detect expression of four genes, ribonucleotide reductase subunit M1(RRM1), X-ray repair cross-complementing gene 1 (XRCC1), thymidylate synthase (TS) and class III ${\beta}$-tubulin (TUBB3), and to assess their application in the clinic for prediction of response of non-small cell lung cancer (NSCLC) to chemoradiotherapy. Materials and Methods: We have designed four gene molecular beacon (MB) probes for multiplex quantitative real-time polymerase chain reactions to examine RRM1, XRCC1, TUBB3 and TS mRNA expression in paraffin-embedded specimens from 50 patients with advanced or metastatic carcinomas. Twenty one NSCLC patients receiving cisplatin-based first-line treatment were analyzed. Results: These molecular beacon probes could specially bind to their target genes in homogeneous solutions. Patients with low RRM1 and XRCC1 mRNA levels were found to have apparently higher response rates to chemoradiotherapy compared with those with high levels of RRM1 and XRCC1 expression (p<0.05). The TS gene expression level was not significantly associated with chemotherapy response (p>0.05). Conclusions: A method of simultaneously detecting four molecular markers was successfully established and applied for evaluation of chemoradiotherapy response. It may be a useful tool in personalized cancer therapy.

• Toxicological Research
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• v.32 no.4
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• pp.289-300
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• 2016

Non-genotoxic carcinogens are substances that induce tumorigenesis by non-mutagenic mechanisms and long term rodent bioassays are required to identify them. Recent studies have shown that transcription profiling can be applied to develop early identifiers for long term phenotypes. In this study, we used rat liver expression profiles from the NTP (National Toxicology Program, Research Triangle Park, USA) DrugMatrix Database to construct a gene classifier that can distinguish between non-genotoxic carcinogens and other chemicals. The model was based on short term exposure assays (3 days) and the training was limited to oxidative stressors, peroxisome proliferators and hormone modulators. Validation of the predictor was performed on independent toxicogenomic data (TG-GATEs, Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System, Osaka, Japan). To build our model we performed Random Forests together with a recursive elimination algorithm (VarSelRF). Gene set enrichment analysis was employed for functional interpretation. A total of 770 microarrays comprising 96 different compounds were analyzed and a predictor of 54 genes was built. Prediction accuracy was 0.85 in the training set, 0.87 in the test set and increased with increasing concentration in the validation set: 0.6 at low dose, 0.7 at medium doses and 0.81 at high doses. Pathway analysis revealed gene prominence of cellular respiration, energy production and lipoprotein metabolism. The biggest target of toxicogenomics is accurately predict the toxicity of unknown drugs. In this analysis, we presented a classifier that can predict non-genotoxic carcinogenicity by using short term exposure assays. In this approach, dose level is critical when evaluating chemicals at early time points.

• Journal of Bone Metabolism
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• v.25 no.4
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• pp.251-266
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• 2018

Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.