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Breast cancer is a clinically heterogeneous disease characterized by distinct molecular aberrations. Understanding the heterogeneity and identifying subgroups of breast cancer are essential to improving diagnoses and predicting therapeutic responses. In this paper, we propose a classification scheme for breast cancer which integrates data on differentially expressed genes (DEGs), copy number variations (CNVs) and microRNAs (miRNAs)-regulated mRNAs. Pathway information based on the estimation of molecular pathway activity is also applied as a postprocessor to optimize the classifier. A total of 250 malignant breast tumors were analyzed by k-means clustering based on the patterns of the expression profiles of 215 intrinsic genes, and the classification performances were compared with existing breast cancer classifiers including the BluePrint and the 625-gene classifier. We show that a classification scheme which incorporates pathway information with various genetic variations achieves better performance than classifiers based on the expression levels of individual genes, and propose that the identified signature serves as a basic tool for identifying rational therapeutic opportunities for breast cancer patients.

Osteoarthritis (OA) is the most common degenerative joint disorder in the elderly population. To identify OA-associated genetic variants and candidate genes, we conducted a genome-wide association study (GWAS). A total 3,793 samples (476 cases: wrist ＋ knee and 3317 controls) from a community-based epidemiological study were genotyped using the Affymetrix SNP 5.0. An intronic SNP (rs4789934) in the TIMP2 (tissue inhibitor of metalloproteinase-2) showed the most significance with OA (odd ratio [OR] = 2.06, 95% confidence interval [CI] = 1.52-2.81, p = 4.01 × 10 －6 ). Furthermore, a polymorphism (rs1352677) in the NKAIN2 (Na ＋ /K ＋ transporting ATPase interacting 2) was suggestively associated with OA (OR = 1.43, CI = 1.22-1.66, p = 7.01 × 10 －6 ). The present study provides new insights into the identification of genetic predisposing factors for OA.

The objective of this study was to analyze and identify pancreatic cancer stem cell-specific microRNAs (miRNAs) and messenger RNAs (mRNAs) and investigate their correlations to cancer stem cell biology. More, pancreatic cancer stem cell-specific miRNAs in cancer patients’ serum were analyzed to determine as a diagnostic application. We used sphere cultivation methods to enrich the stem cell population from the pancreatic cancer cell line, Capan-1 with the normal pancreatic cell line, YGIC-6, and analyzed the overall miRNA and mRNA expressions using with microarray analysis. Total RNA from normal and patient's serum was extracted and the expression individual miRNAs in serum was analyzed by real-time PCR. Fifty two miRNAs including mir-26a, miR-99a, miR-106a, mir-125b, miR-192 and mir-429 were differentially expressed in the pancreatic cancer stem cells. From the miRNA analysis, 170 miRNAs were differentially expressed in the normal cell line YGIC-6, 126 miRNAs in the cancer cell line Capan-1 and 12 miRNAs in both the normal and cancer cell line originated stem cell populations. Examining both the miRNA and mRNA profiles, 52 miRNAs and 111 stem cell-associated mRNAs were highly correlated (both p values Citation Format: Dawoon E. Jung, Hyungseok Choi, Jee Yeon Heo, Hee Seung Lee, Kyong Joo Lee, Si Young Song. Computational correlation among microRNAs and mRNAs specifically expressed in pancreatic cancer stem cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1955.

Background Type 2 diabetes mellitus (T2D), a metabolic disorder characterized by insulin resistance and relative insulin deficiency, is a complex disease of major public health importance. Its incidence is rapidly increasing in the developed countries. Complex diseases are caused by interactions between multiple genes and environmental factors. Most association studies aim to identify individual susceptibility single markers using a simple disease model. Recent studies are trying to estimate the effects of multiple genes and multi-locus in genome-wide association. However, estimating the effects of association is very difficult. We aim to assess the rules for classifying diseased and normal subjects by evaluating potential gene-gene interactions in the same or distinct biological pathways. Results We analyzed the importance of gene-gene interactions in T2D susceptibility by investigating 408 single nucleotide polymorphisms (SNPs) in 87 genes involved in major T2D-related pathways in 462 T2D patients and 456 healthy controls from the Korean cohort studies. We evaluated the support vector machine (SVM) method to differentiate between cases and controls using SNP information in a 10-fold cross-validation test. We achieved a 65.3% prediction rate with a combination of 14 SNPs in 12 genes by using the radial basis function (RBF)-kernel SVM. Similarly, we investigated subpopulation data sets of men and women and identified different SNP combinations with the prediction rates of 70.9% and 70.6%, respectively. As the high-throughput technology for genome-wide SNPs improves, it is likely that a much higher prediction rate with biologically more interesting combination of SNPs can be acquired by using this method. Conclusions Support Vector Machine based feature selection method in this research found novel association between combinations of SNPs and T2D in a Korean population.

To identify genetic factors influencing quantitative traits of biomedical importance, we conducted a genome-wide association study in 8,842 samples from population-based cohorts recruited in Korea. For height and body mass index, most variants detected overlapped those reported in European samples. For the other traits examined, replication of promising GWAS signals in 7,861 independent Korean samples identified six previously unknown loci. For pulse rate, signals reaching genome-wide significance mapped to chromosomes 1q32 (rs12731740, P = 2.9 x 10(-9)) and 6q22 (rs12110693, P = 1.6 x 10(-9)), with the latter approximately 400 kb from the coding sequence of GJA1. For systolic blood pressure, the most compelling association involved chromosome 12q21 and variants near the ATP2B1 gene (rs17249754, P = 1.3 x 10(-7)). For waist-hip ratio, variants on chromosome 12q24 (rs2074356, P = 7.8 x 10(-12)) showed convincing associations, although no regional transcript has strong biological candidacy. Finally, we identified two loci influencing bone mineral density at multiple sites. On chromosome 7q31, rs7776725 (within the FAM3C gene) was associated with bone density at the radius (P = 1.0 x 10(-11)), tibia (P = 1.6 x 10(-6)) and heel (P = 1.9 x 10(-10)). On chromosome 7p14, rs1721400 (mapping close to SFRP4, a frizzled protein gene) showed consistent associations at the same three sites (P = 2.2 x 10(-3), P = 1.4 x 10(-7) and P = 6.0 x 10(-4), respectively). This large-scale GWA analysis of well-characterized Korean population-based samples highlights previously unknown biological pathways.