Shinuk Kim

Pathway-based classification of cancer subtypes

BackgroundMolecular markers based on gene expression profiles have been used in experimental and clinical settings to distinguish cancerous tumors in stage, grade, survival time, metastasis, and drug sensitivity. However, most significant gene markers are unstable (not reproducible) among data sets. We introduce a standardized method for representing cancer markers as 2-level hierarchical feature vectors, with a basic gene level as well as a second level of (more stable) pathway markers, for the purpose of discriminating cancer subtypes. This extends standard gene expression arrays with new pathway-level activation features obtained directly from off-the-shelf gene set enrichment algorithms such as GSEA. Such so-called pathway-based expression arrays are significantly more reproducible across datasets. Such reproducibility will be important for clinical usefulness of genomic markers, and augment currently accepted cancer classification protocols.ResultsThe present method produced more stable (reproducible) pathway-based markers for discriminating breast cancer metastasis and ovarian cancer survival time. Between two datasets for breast cancer metastasis, the intersection of standard significant gene biomarkers totaled 7.47% of selected genes, compared to 17.65% using pathway-based markers; the corresponding percentages for ovarian cancer datasets were 20.65% and 33.33% respectively. Three pathways, consisting of Type_1_diabetes mellitus, Cytokine-cytokine_receptor_interaction and Hedgehog_signaling (all previously implicated in cancer), are enriched in both the ovarian long survival and breast non-metastasis groups. In addition, integrating pathway and gene information, we identified five (ID4, ANXA4, CXCL9, MYLK, FBXL7) and six (SQLE, E2F1, PTTG1, TSTA3, BUB1B, MAD2L1) known cancer genes significant for ovarian and breast cancer respectively.ConclusionsStandardizing the analysis of genomic data in the process of cancer staging, classification and analysis is important as it has implications for both pre-clinical as well as clinical studies. The paradigm of diagnosis and prediction using pathway-based biomarkers as features can be an important part of the process of biomarker-based cancer analysis, and the resulting canonical (clinically reproducible) biomarkers can be important in standardizing genomic data. We expect that identification of such canonical biomarkers will improve clinical utility of high-throughput datasets for diagnostic and prognostic applications.ReviewersThis article was reviewed by John McDonald (nominated by I. King Jordon), Eugene Koonin, Nathan Bowen (nominated by I. King Jordon), and Ekaterina Kotelnikova (nominated by Mikhail Gelfand).

Inferring gene regulatory networks from temporal expression profiles under time-delay and noise

Ordinary differential equations (ODE) have been widely used for modeling and analysis of dynamic gene networks in systems biology. In this paper, we propose an optimization method that can infer a gene regulatory network from time-series gene expression data. Specifically, the following four cases are considered: (1) reconstruction of a gene network from synthetic gene expression data with noise, (2) reconstruction of a gene network from synthetic gene expression data with time-delay, (3) reconstruction of a gene network from synthetic gene expression data with noise and time-delay, and (4) reconstruction of a gene network from experimental time-series data in budding yeast cell cycle.

Identifying molecular subtypes related to clinicopathologic factors in pancreatic cancer

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal tumors and usually presented with locally advanced and distant metastasis disease, which prevent curative resection or treatments. In this regard, we considered identifying molecular subtypes associated with clinicopathological factor as prognosis factors to stratify PDAC for appropriate treatment of patients.ResultsIn this study, we identified three molecular subtypes which were significant on survival time and metastasis. We also identified significant genes and enriched pathways represented for each molecular subtype. Considering R0 resection patients included in each subtype, metastasis and survival times are significantly associated with subtype 1 and subtype 2.ConclusionsWe observed three PDAC molecular subtypes and demonstrated that those subtypes were significantly related with metastasis and survival time. The study may have utility in stratifying patients for cancer treatment.

Brief Communication: Identifying the target mRNAs of microRNAs in colorectal cancer

MicroRNAs (miRNAs) play an important role in gene regulatory networks by inhibiting the expression of target mRNAs. There is a growing interest in identifying the relationship between miRNAs and their target mRNAs. Various experimental studies have been carried out to discover miRNAs involved in cancer and to identify their target genes. At the same time, a large volume of miRNA and mRNA expression profiles have become available owing to the development of high-throughput measurement technologies. So, there is now a pressing need to develop a computational method by which we can identify the target mRNAs of given miRNAs from such massive expression data sets. In this respect, we propose an effective linear model based identification method to unravel the relationship between miRNAs and their target mRNAs in colorectal cancer by using microarray expression profiles and sequence data.

Effect of Training Intensity on Nonalcoholic Fatty Liver Disease.

BACKGROUND Training intensity may play a key role in magnifying the protective effect of physical exercise against nonalcoholic fatty liver disease (NAFLD). PURPOSE This study aimed to test the hypothesis that vigorous-intensity and interval training is as effective as moderate-intensity and continuous exercise training on NAFLD in high-fat diet (HFD)-induced obese mice. METHODS C57BL/6 mice (N = 40) were fed a standard-chow diet (n = 10) or HFD (n = 30) for 16 wk. After the initial 8-wk dietary treatments, HFD mice were further divided into HFD only (n = 10), HFD plus vigorous-intensity and interval treadmill running (VIT) (n = 10), and HFD plus moderate-intensity and continuous treadmill running (MIT) (n = 10) for the remaining 8-wk period. RESULTS Chronic exposure to HFD resulted in hepatic steatosis in conjunction with an obese and impaired glucose tolerance condition characterized by dyslipidemia, hyperinsulinemia elevated markers for the liver damage, and hypoadiponectinemia. Although VIT and MIT alleviated the NAFLD conditions, the former was more effective at alleviating hepatic steatosis than the latter. The intensity-dependent benefit of exercise training against hepatic steatosis was associated with greater activation of VIT on hepatic AMP-mediated protein kinase in conjunction with greater suppressive effect of VIT on hypoadiponectinemia, downregulation of the Adiponectin receptor 2 signaling pathway, and upregulation of the NF-κB signaling pathway in the liver. CONCLUSIONS The current findings suggest that VIT is an alternative way of exercise training to combat hepatic steatosis associated with an obese and impaired glucose tolerance phenotype.

Treadmill Running Reverses Cognitive Declines due to Alzheimer Disease.

PURPOSE This study investigated the effect of treadmill running on cognitive declines in the early and advanced stages of Alzheimer disease (AD) in 3xTg-AD mice. METHODS At 4 months of age, 3xTg-AD mice (N = 24) were assigned to control (AD + CON, n = 12) or exercise (AD + EX, n = 12) group. At 24 months of age, 3xTg-AD mice (N = 16) were assigned to AD + CON (n = 8) or AD + EX (n = 8) group. The AD + EX mice were subjected to treadmill running for 12 wk. At each pathological stage, the background strain mice were included as wild-type control (WT + CON, n = 8-12). RESULTS At the early stage of AD, 3xTg-AD mice had impaired short- and long-term memory based on Morris water maze along with higher cortical Aβ deposition, higher hippocampal and cortical tau pathology, and lower hippocampal and cortical PSD-95 and synaptophysin. A 12-wk treadmill running reversed the impaired cognitive declines and significantly improved the tau pathology along with suppression of the decreased PSD-95 and synaptophysin in the hippocampus and cortex. At the advanced stage of AD, 3xTg-AD mice had impaired short- and long-term memory along with higher levels of Aβ deposition, soluble Aβ1-40 and Aβ1-42, tau pathology, and lower levels of brain-derived neurotrophic factor, PSD-95, and synaptophysin in the hippocampus and cortex. A 12-wk treadmill running reversed the impaired cognitive declines and significantly improved the Aβ and tau pathology along with suppression of the decreased synaptic proteins and brain-derived neurotrophic factor in the hippocampus and cortex. CONCLUSIONS The current findings suggest that treadmill running provides a nonpharmacological means to combat cognitive declines due to AD pathology.

Physical activity and metabolic syndrome in Korean children.

Little is known about whether lifestyle factors such as dietary intake, physical activity (PA), and cardio/respiratory fitness (CRF) are associated with metabolic risk factors in Korean children. The purpose of the study was to investigate the relationships among those lifestyle-related modifiable factors and the clustering of metabolic risk factors in young Korean children. In a cross-sectional study, we studied 246 Korean children (mean+/-SD; age: 12.6+/-0.5 years, BMI: 19.9+/-3.2 kg/m (2)) who were recruited from local elementary schools. In the total study population, physical activity and CRF were inversely associated with metabolic risk factors including body fatness, blood pressures, blood lipids and glucose. Daily caloric intake and proportion of carbohydrates were positively associated with BMI and percent body fat only. Multivariate regression analyses showed that physical activity was independently and inversely associated with the clustering of metabolic risk factors, even after adjustments for age, sex, sexual maturation, dietary intake, and CRF. Overall, the current findings of the study suggest that physical activity rather than CRF and/or dietary intake is an independent predictor for the clustering of metabolic risk factors in Korean children.

GNB3 C825T Polymorphism and Elevated Blood Pressure

Unlike in Europeans and Africans, the relationship between the human guanine nucleotide binding beta polypeptide 3 (GNB3) C825T gene polymorphism (rs5443) and blood pressures is inconsistent in Asians. The aim of the study was to investigate whether the GNB3 genotype demonstrates different associations with resting blood pressure and body fatness across cardio/respiratory fitness (CRF) levels. A total of 727 Korean women aged 31-60 years (mean, 47.8+/-5.4 years) participated in the study. In subgroup analyses of the obese group, TT individuals had significantly higher values of body weight than CC and CT individuals (p=0.006 and p=0.006, respectively) and body mass index (BMI) (p=0.002 and p=0.011, respectively). TT and CT individuals also tended to have higher CRF values than CC individuals. Regression analyses showed that the association between GNB3 genotype and resting blood pressure remained significant after adjustment for age and menopause, but was not significant after additional adjustment for body fatness. In summary, the findings of this study suggest that body fatness and CRF might modify the GNB3-mediated genetic susceptibility to elevated resting blood pressures in middle-aged Korean women.

A method for generating new datasets based on copy number for cancer analysis.

New data sources for the analysis of cancer data are rapidly supplementing the large number of gene-expression markers used for current methods of analysis. Significant among these new sources are copy number variation (CNV) datasets, which typically enumerate several hundred thousand CNVs distributed throughout the genome. Several useful algorithms allow systems-level analyses of such datasets. However, these rich data sources have not yet been analyzed as deeply as gene-expression data. To address this issue, the extensive toolsets used for analyzing expression data in cancerous and noncancerous tissue (e.g., gene set enrichment analysis and phenotype prediction) could be redirected to extract a great deal of predictive information from CNV data, in particular those derived from cancers. Here we present a software package capable of preprocessing standard Agilent copy number datasets into a form to which essentially all expression analysis tools can be applied. We illustrate the use of this toolset in predicting the survival time of patients with ovarian cancer or glioblastoma multiforme and also provide an analysis of gene- and pathway-level deletions in these two types of cancer.

Cancer survival classification using integrated data sets and intermediate information

OBJECTIVE Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. METHODS FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS). RESULTS In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF; 75%, SVM; 75%, FSCOX_median; and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF; 63.64%, SVM; 72.73%, FSCOX_median; and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of miRNA/mRNA using IFS was 75.51% (RF), 87.76% (SVM) 85.71% (FSCOX_median), 85.71% (FSCOX_SVM). These results are higher than the results of using miRNA expression and mRNA expression alone. In addition we predict 16 hsa-miR-23b and hsa-miR-27b target genes in ovarian cancer data sets, obtained by SVM-based feature selection through integration of sequence information and gene expression profiles. CONCLUSION Among the approaches used, the integrated miRNA and mRNA data set yielded better results than the individual data sets. The best performance was achieved using the FSCOX_SVM method with independent feature selection, which uses intermediate survival information between short-term and long-term survival time and the combination of the 2 different data sets. The results obtained using the combined data set suggest that there are some strong interactions between miRNA and mRNA features that are not detectable in the individual analyses.