• Genomics & Informatics
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• v.19 no.1
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• pp.10.1-10.7
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• 2021

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC₅₀ and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC₅₀ and 0.81 for AUC. We identify common predictor genes for IC₅₀ and AUC, with which the performance was similar to those with genes separately found for IC₅₀ and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC₅₀, 0.7945 for AUC) with 321 predictor genes.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.195-198
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• 2004

One Important problem in a cancer microarray study is to identify a set of genes from which a molecular prognostic indicator can be developed. In parallel with this problem is to validate the chosen set of genes. We develop in this note a K-fold cross validation procedure by combining a 'pre-validation' technique and a bootstrap resampling procedure in the Cox regression . The pre-validation technique predicts the microarray predictor of a case without having seen the true class level of the case. It was suggested by Tibshirani and Efron (2002) to avoid the possible over-fitting in the regression in which a microarray based predictor is employed. The bootstrap resampling procedure for the Cox regression was proposed by Sauerbrei and Schumacher (1992) as a means of overcoming the instability of a stepwise selection procedure. We apply this K-fold cross validation to the microarray data of 92 gastric cancers of which the experiment was conducted at Cancer Metastasis Research Center, Yonsei University. We also share some of our experience on the 'false positive' result due to the information leak.

• BMB Reports
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• v.46 no.1
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• pp.41-46
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• 2013

Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4217-4224
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• 2014

Cholangiocarcinoma (CCA), a slow growing but highly metastatic tumor, is highly prevalent in Northeast Thailand. Specific tests that predict prognosis of CCA remain elusive. The present study was designed to investigate whether peripheral blood leukocyte (PBL) transcriptional profiles might be of use as a prognostic test in CCA patients. Gene expression profiles of PBLs from 9 CCA and 8 healthy subjects were conducted using the Affymetrix HG_U133 Plus 2.0 GeneChip. We indentified informative PBLs gene expression profiles that could reliably distinguish CCA patients from healthy subjects. Of these CCA specific genes, 117 genes were up regulated and 60 were down regulated. The molecular and cellular functions predicted for these CCA specific genes according to the Gene Ontology database indicated differential PBL expression of host immune response and tumor progression genes (EREG, TGF ${\beta}1$, CXCL2, CXCL3, IL-8, and VEGFA). The expression levels of 9 differentially expressed genes were verified in 36 CCA vs 20 healthy subjects. A set of three tumor invasion related genes (PLAU, CTSL and SERPINB2) computed as "prognostic index" was found to be an independent and statistically significant predictor for CCA patient survival. The present study shows that CCA PBLs may serve as disease predictive clinically accessible surrogates for indentifying expressed genes reflective of CCA disease severity.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.595-598
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• 2014

Background: NF-${\kappa}B$ inhibits apoptosis through induction of antiapoptotic proteins and suppression of proapoptotic genes. Various chemotherapy agents induce NF-${\kappa}B$ translocation and target gene activation. We conducted the present study to assess the predictive value of NF-${\kappa}B$ regarding pathologic responses after receiving neoadjuvant chemotherapy. Materials and Methods: We enrolled 131 patients with locally advanced invasive ductal breast carcinoma. Immunohistochemistry (IHC) was used to detect NF-${\kappa}B$ expression. Evaluation of pathologic response was elaborated with the Ribero classification. Results: Expression of NF-${\kappa}B$ was significantly associated with poor pathological response (p=0.02). From the multivariate analysis, it was found that the positive expression of NF-${\kappa}B$ yielded RR=1.74 (95%CI 0.77 to 3.94). Conclusions: NF-${\kappa}B$ can be used as a predictor of poor pathological response after neoadjuvant chemotherapy.

• 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 the Korean Society of Food Science and Nutrition
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• v.42 no.12
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• pp.1899-1907
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• 2013

We investigated the effects of unripe black raspberry water extract (UBR-W) and oxidation-LDL treatment on cholesterol levels. Experiments using an established human hepatocellular carcinoma cell line (HepG2) showed a time-dependent increase in expression of LDL receptor after UBR-W treatment. Expression of LDL receptor-related genes, such as SREBP1 and 2, increased upon UBR-W treatment. However, expression of HDL-related genes was unaffected by UBR-W. HMG-CoA reductase activity was reduced by UBR-W treatment, whereas HMG-CoA mRNA expression significantly increased. In addition, the ApoB/ApoA1 mRNA level, which is a predictor of cardiovascular risk, was reduced in a time-dependent manner by UBR-W treatment. Macrophage-like cells (RAW 264.7) showed increased expression of ox-LDL-related genes, such as CD36, scavenger receptor-A, adipophilin, and PPAR-gamma, upon ox-LDL treatment compared to untreated control cells, and quantitative lipid analysis indicated a dramatic increase in lipid accumulation. However, UBR-W treatment significantly reduced expression of ox-LDL-related genes and largely prevented lipid accumulation. The results indicate that UBR-W mediates a cholesterol-lowering effect via inhibition of cholesterol synthesis and induction of LDL uptake through SREBP.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1255-1262
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• 2014

Background: The aim of the study was to investigate any associations between benign prostate hyperplasia (BPH) and single nucleotide polymorphisms (SNPs) in the VDR gene (FokI, BsmI, ApaI and Taq${\alpha}$I loci) and the CYP17 gene (MspA1I locus), as well as TA repeat polymorphism in SRD5A2 gene among Lebanese men. Materials and Methods: DNA extracted from blood of 68 subjects with confirmed BPH and 79 age-matched controls was subjected to PCR/PCR-restriction fragment length polymorphism analysis. The odds ra=tio (OR) of having a genotype and the relative risk (RR) of developing BPH for having the genotype were calculated and the alleles were designated risk-bearing or protective. Results: Our data indicated that the A and B alleles of the VDR ApaI and BsmI SNPs were highly associated with increased risk of BPH (p=0.0168 and 0.0002, respectively). Moreover, 63% of the controls compared to 43% of the subjects with BPH were homozygous for none of the risk-bearing alleles (p=0.0123) whereas 60% of the controls and 28% of the subjects with BPH were homozygous for two or more protective alleles (p<0.0001). Conclusions: For the first time, our study demonstrated that ApaI and BsmI of the VDR gene are associated with risk of BPH among Lebanese men. Our study also indicated that overall polymorphism profile of all the genes involved in prostate physiology could be a better predictor of BPH risk.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.3
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• pp.1053-1057
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• 2012

Objective: Identifying cancer-related genes or proteins is critical in preventing and controlling colorectal cancer (CRC). This study was to investigate the clinicopathological and prognostic value of activating transcription factor 1 (ATF1) in CRC. Methods: Protein expression of ATF1 was detected using immunohistochemistry in 66 CRC tissues. Clinicopathological association of ATF1 in CRC was analyzed with chi-square test or Fisher's exact test. The prognostic value of ATF1 in CRC is estimated using the Kaplan-Meier analysis and Cox regression models. Results: The ATF1 protein expression was significantly lower in tumor tissues than corresponding normal tissues (51.5% and 71.1%, respectively, P = 0.038). No correlation was found between ATF1 expression and the investigated clinicopathological parameters, including gender, age, depth of invasion, lymph node status, metastasis, pathological stage, vascular tumoral emboli, peritumoral deposits, chemotherapy and original tumor site (all with P > 0.05). Patients with higher ATF1 expression levels have a significantly higher survival rate than that with lower expression (P = 0.026 for overall survival, P = 0.008 for progress free survival). Multivariate Cox regression model revealed that ATF1 expression and depth of invasion were the predictors of the overall survival (P = 0.008 and P = 0.028) and progress free survival (P = 0.002 and P = 0.005) in CRC. Conclusions: Higher ATF1 expression is a predictor of a favorable outcome for the overall survival and progress free survival in CRC.

• Korean Journal of Biological Psychiatry
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• v.15 no.1
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• pp.14-22
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• 2008

Objectives : The authors purposed to present data for explaining gene-environmental interaction causing depressive disorder by examining the effects of genetic factors related to the serotonin system and environmental factors such as stressful life events in early adulthood. Methods : The subjects were 150 young adults(mean age 25.0${\pm}$0.54), a part of 534 freshmen who had completed the previous study of genotyping of TPH1 gene. We assessed characteristics of life events, depression and anxiety scale and checked if they had a depressive disorder with DSM-IV SCID interview. Along with TPH1 A218C genotype confirmed in previous study, TPH2 -1463G/A and 5HTR2A -1438A/G genes were genotyped using the SNaPshot$^{TM}$ method. Results : In comparison with the group without C allele of TPH1 gene, the number of life events had a significant effect on the probability of depressive disorder in the group with C allele. Other alleles or genotypes did not have a significant effect on the causality of life events and depressive disorder. Conclusion : The results of this study suggest that TPH1 C allele is a significant predictor of onset of depressive disorder following environmental stress. It means that the TPH1 gene may affect the gene-environmental interaction of depressive disorder.