Methylation Signature for Prediction of Progression Free Survival in Surgically Treated Clear Cell Renal Cell Carcinoma.

BACKGROUND: Little is known about epigenetic silencing of genes by promoter hypermethylation in renal cell carcinoma (RCC). The aim of this study was to identify prognostic methylation markers in surgically treated clear cell RCC (ccRCC). METHODS: Methylation patterns were assayed using the Infinium HumanMethylation450 BeadChip array on pairs of ccRCC and normal tissue from 12 patients. Using quantitative PSQ analysis, tumor-specific hypermethylated genes were validated in 25 independent cohorts and their clinical relevance was also verified in 152 independent cohorts. RESULTS: Using genome-wide methylation array, Zinc finger protein 278 (ZNF278), Family with sequence similarity 155 member A (FAM155A) and Dipeptidyl peptidase 6 (DPP6) were selected for tumor-specific hypermethylated genes in primary ccRCC. The promoter methylation of these genes occurred more frequently in ccRCC than normal kidney in independent validation cohort. The hypermethylation of three genes were associated with advanced tumor stage and high grade tumor in ccRCC. During median follow-up of 39.2 (interquartile range, 15.4-79.1) months, 22 (14.5%) patients experienced distant metastasis. Multivariate analysis identified the methylation status of these three genes, either alone, or in a combined risk score as an independent predictor of distant metastasis. CONCLUSION: The promoter methylation of ZNF278, FAM155A and DPP6 genes are associated with aggressive tumor phenotype and early development of distant metastasis in patients with surgically treated ccRCC. These potential methylation markers, either alone, or in combination, could provide novel targets for development of individualized therapeutic and prevention regimens.

ZNF492 and GPR149 methylation patterns as prognostic markers for clear cell renal cell carcinoma: Array­based DNA methylation profiling.

The present study aimed to identify novel methylation markers of clear cell renal cell carcinoma (ccRCC) using microarray methylation analysis and evaluate their prognostic relevance in patient samples. To identify cancer­specific methylated biomarkers, microarray profiling of ccRCC samples from our institute (n=12) and The Cancer Genome Atlas (TCGA) database (n=160) were utilized, and the prognostic relevance of candidate genes were investigated in another TCGA dataset (n=153). For validation, pyrosequencing analyses with ccRCC samples from our institute (n=164) and another (n=117) were performed and the potential clinical application of selected biomarkers was examined. We identified 22 CpG island loci that were commonly hypermethylated in ccRCC. Kaplan­Meier analysis of TCGA data indicated that only 4/22 loci were significantly associated with disease progression. In the internal validation set, Kaplan­Meier analysis revealed that hypermethylation of two loci, zinc finger protein 492 (ZNF492) and G protein­coupled receptor 149 (GPR149), was significantly associated with shorter time­to­progression. Multivariate Cox regression models revealed that hypermethylation of ZNF492 [hazard ratio (HR), 5.44; P=0.001] and GPR149 (HR, 7.07; P<0.001) may be independent predictors of tumor progression. Similarly, the methylation status of these two genes was significantly associated with poor outcomes in the independent external validation cohort. Collectively, the present study proposed that the novel methylation markers ZNF492 and GPR149 could be independent prognostic indicators in patients with ccRCC.

Intra-Oral Factors Influencing Halitosis in Young Women.

OBJECTIVES: The aim of this research was to determine intra-oral factors that affect halitosis in young women. METHODS: This study was performed between March 2014 to May 2014, and included 35 women in their 20s with good oral health. Correlation and logistic regression analyses were performed to investigate the change in halitosis immediately, and 1 hour after scaling. RESULTS: In both oral gas (OG) and extraoral gas (EG) groups, halitosis was reduced after scaling compared to before scaling. The logistic regression analysis of oral state factors in OG showed that as oral fluid [odds ratio (OR) = 0.792, p = 0.045] and dental plaque (OR = 0.940, p = 0.016) decreased by 1 unit, the OR in the OG group decreased (> 50). In addition, as glucose levels in the oral cavity (OR = 1.245, p = 0.075) and tongue coating index (OR = 2.912, p = 0.064) increased by 1 unit, the OR in the OG group increased (> 50). Furthermore, in the EG group, as oral fluid (OR = 0.66, p = 0.01) and dental plaque (OR = 0.95, p = 0.04) decreased, the OR in the EG group decreased (> 50) significantly. CONCLUSION: To control halitosis, it is necessary to increase oral fluid and decrease the amount of tongue plaque. Furthermore, maintaining a healthy oral environment, aided by regular scaling and removal of dental plaque, may significantly control halitosis.

Biomarker for the Prediction of Major Adverse Cardiac Events in Patients with Non-ST-Segment Elevation Myocardial Infarction.

N-terminal pro-brain natriuretic peptide (NT-proBNP) is a well-known biomarker for the diagnosis and prognosis of heart failure, and is directly associated with myocardial dysfunction. We evaluated the prognostic value of NT-proBNP for major adverse cardiac events (MACEs) among patients with non-ST-segment elevation myocardial infarction (NSTEMI) from the Korea Acute Myocardial Infarction Registry during their mid-term follow-up period. In this paper, we analyzed NT-proBNP according to various MACE and level of NT-proBNP. We used multivariate logistic regression to determine the risk factors according to MACE type and NT-proBNP levels, and to identify the cutoff value for each MACE by using the receiver operating characteristic (ROC) curve. NT-proBNP was a significant variable among cardiac deaths (p = 0.016), myocardial infarction (p = 0.000), and coronary artery bypass grafting (CABG) (p = 0.000) in patients with MACE compared with those without MACE. Two-vessel coronary artery disease (CAD) (p = 0.037) and the maximum creatinine kinase (max-CK) (p = 0.031) produced significant results in repeat percutaneous coronary intervention. The area under the ROC curve was found to be statistically significant for cardiac death and CABG. NT-proBNP is a useful predictor for 12-month MACEs among patients with NSTEMI and in those with heart failure. We propose that a new index incorporating NT-proBNP, max-CK, and CAD vessel will be useful as a prognostic indicator of MACEs in the future.

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data.

BACKGROUND: Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments. RESULTS: In this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results. CONCLUSION: Spearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.

Comparison of clinical outcomes between culprit vessel only and multivessel percutaneous coronary intervention for ST-segment elevation myocardial infarction patients with multivessel coronary diseases.

BACKGROUND: The clinical significance of complete revascularization for ST segment elevation myocardial infarction (STEMI) patients during admission is still debatable. METHODS: A total of 1406 STEMI patients from the Korean Myocardial Infarction Registry with multivessel diseases without cardiogenic shock who underwent primary percutaneous coronary intervention (PPCI) were analyzed. We used propensity score matching (PSM) to control differences of baseline characteristics between culprit only intervention (CP) and multivessel percutaneous coronary interventions (MP), and between double vessel disease (DVD) and triple vessel disease (TVD). The major adverse cardiac event (MACE) was analyzed for one year after discharge. RESULTS: TVD patients showed higher incidence of MACE (14.2% vs. 8.6%, P = 0.01), any cause of revascularization (10.6% vs. 5.9%, P = 0.01), and repeated PCI (9.5% vs. 5.7%, P = 0.02), as compared to DVD patients during one year after discharge. MP reduced MACE effectively (7.3% vs. 13.8%, P = 0.03), as compared to CP for one year, but all cause of death (1.6% vs. 3.2%, P = 0.38), MI (0.4% vs. 0.8%, P = 1.00), and any cause of revascularization (5.3% vs. 9.7%, P = 0.09) were comparable in the two treatment groups. CONCLUSIONS: STEMI patients with TVD showed higher rate of MACE, as compared to DVD. MP performed during PPCI or ad hoc during admission for STEMI patients without cardiogenic shock showed lower rate of MACE in this large scaled database. Therefore, MP could be considered as an effective treatment option for STEMI patients without cardiogenic shock.

Application of gap-constraints given sequential frequent pattern mining for protein function prediction.

OBJECTIVES: Predicting protein function from the protein-protein interaction network is challenging due to its complexity and huge scale of protein interaction process along with inconsistent pattern. Previously proposed methods such as neighbor counting, network analysis, and graph pattern mining has predicted functions by calculating the rules and probability of patterns inside network. Although these methods have shown good prediction, difficulty still exists in searching several functions that are exceptional from simple rules and patterns as a result of not considering the inconsistent aspect of the interaction network. METHODS: In this article, we propose a novel approach using the sequential pattern mining method with gap-constraints. To overcome the inconsistency problem, we suggest frequent functional patterns to include every possible functional sequence-including patterns for which search is limited by the structure of connection or level of neighborhood layer. We also constructed a tree-graph with the most crucial interaction information of the target protein, and generated candidate sets to assign by sequential pattern mining allowing gaps. RESULTS: The parameters of pattern length, maximum gaps, and minimum support were given to find the best setting for the most accurate prediction. The highest accuracy rate was 0.972, which showed better results than the simple neighbor counting approach and link-based approach. CONCLUSION: The results comparison with other approaches has confirmed that the proposed approach could reach more function candidates that previous methods could not obtain.

Combined hypermethylation of APC and GSTP1 as a molecular marker for prostate cancer: quantitative pyrosequencing analysis.

A total of 149 human prostate tissues obtained from our institute were assessed: 52 specimens of benign prostate hyperplasia (BPH) and 97 specimens of prostate cancer (PCa). The methylation status of the genes of Adenomatous polyposis coli (APC) and glutathione-S-transferase-P1 (GSTP1) was analyzed by quantitative pyrosequencing. A methylation score (M score) was calculated to capture the combined methylation level of both genes. The methylation level of each single gene and that of both genes combined was significantly higher in PCa specimens than in BPH (each p < 0.001). The value of APC methylation, GSTP1 methylation, and M score for predicting PCa was measured by the area under the receiver operating characteristic (ROC) curve and reached 0.954, 0.942, and 0.983, respectively. The sensitivity and specificity of the M score in discriminating between PCa and BPH reached 92.8% and 100.0%, respectively. The M score was positively associated with the serum prostate-specific antigen (PSA) level (p trend < 0.001). Our study demonstrates that the quantitative measurement of two methylation markers might drastically improve the ability to discriminate PCa from BPH.

Trigger learning and ECG parameter customization for remote cardiac clinical care information system.

Coronary heart disease is being identified as the largest single cause of death along the world. The aim of a cardiac clinical information system is to achieve the best possible diagnosis of cardiac arrhythmias by electronic data processing. Cardiac information system that is designed to offer remote monitoring of patient who needed continues follow up is demanding. However, intra- and interpatient electrocardiogram (ECG) morphological descriptors are varying through the time as well as the computational limits pose significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is, therefore, a promising new intelligent diagnostic tool.

QSE: A new 3-D solvent exposure measure for the analysis of protein structure.

Solvent exposure of amino acids measures how deep residues are buried in tertiary structure of proteins, and hence it provides important information for analyzing and predicting protein structure and functions. Existing methods of calculating solvent exposure such as accessible surface area, relative accessible surface area, residue depth, contact number, and half-sphere exposure still have some limitations. In this article, we propose a novel solvent exposure measure named quadrant-sphere exposure (QSE) based on eight quadrants derived from spherical neighborhood. The proposed measure forms a microenvironment around Cα atom as a sphere with a radius of 13 Å, and subdivides it into eight quadrants according to a rectangular coordinate system constructed based on geometric relationships of backbone atoms. The number of neighboring Cα atoms whose labels are the same is given as the QSE value of the center Cα atom at hand. As evidenced by histograms that show very different distributions for different structure configurations, the proposed measure captures local properties that are characteristic for a residue's eight-directional neighborhood within a sphere. Compared with other measures, QSE provides a different view of solvent exposure, and provides information that is specific for different tertiary structure. As the experimental results show, QSE measure can potentially be used in protein structure analysis and predictions.