A clinicogenetic model to predict lymph node invasion by use of genome-based biomarkers from exome arrays in prostate cancer patients

PURPOSE: Genetic variations among prostate cancer (PCa) patients who underwent radical prostatectomy (RP) and pelvic lymph node dissection were evaluated to predict lymph node invasion (LNI). Exome arrays were used to develop a clinicogenetic model that combined clinical data related to PCa and individual genetic variations. MATERIALS AND METHODS: We genotyped 242,186 single-nucleotide polymorphisms (SNPs) by using a custom HumanExome BeadChip v1.0 (Illumina Inc.) from the blood DNA of 341 patients with PCa. The genetic data were analyzed to calculate an odds ratio as an estimate of the relative risk of LNI. We compared the accuracies of the multivariate logistic model incorporating clinical factors between the included and excluded selected SNPs. The Cox proportional hazard models with or without genetic factors for predicting biochemical recurrence (BCR) were analyzed. RESULTS: The genetic analysis indicated that five SNPs (rs75444444, rs8055236, rs2301277, rs9300039, and rs6908581) were significant for predicting LNI in patients with PCa. When a multivariate model incorporating clinical factors was devised to predict LNI, the predictive accuracy of the multivariate model was 80.7%. By adding genetic factors in the aforementioned multivariate model, the predictive accuracy increased to 93.2% (p=0.006). These genetic variations were significant factors for predicting BCR after adjustment for other variables and after adding the predictive gain to BCR. CONCLUSIONS: Based on the results of the exome array, the selected SNPs were predictors for LNI. The addition of individualized genetic information effectively enhanced the predictive accuracy of LNI and BCR among patients with PCa who underwent RP.

Methodology for Big Data Analysis Using Data from National Health Insurance Service: Preliminary Methodologic Study and Review about the Relationship between Sinus Surgery and Asthma

BACKGROUND AND OBJECTIVES: Sinus surgery has been reported to improve pulmonary function and decrease the use of asthma medications in patients with chronic rhinosinusitis and asthma. Most studies, however, have used a small number of patients and were conducted over a short period. To demonstrate a causal relationship between sinus surgery and asthma, a sufficient amount of patient data observed over a long period is required. To address the limitations of the existing approaches, we conducted a preliminary methodological study for large-scale data analysis using data from the National Health Insurance Service (NHIS) to suggest a basis for the effect of sinus surgery on asthma. MATERIALS AND METHODS: The data from NHIS consisted of unidentified medical histories of a sample cohort representing the whole nation over a period of nine years. We selected the following types of study samples: 1) patients with surgical codes for sinus surgery; 2) patients with disease codes for sinusitis; 3) patients with disease codes for asthma; and 4) patients with medication codes for asthma treatment. RESULTS: In this study, we applied a methodology for selection of subjects from big data to investigate the effect of sinus surgery on improving asthma in the future. We could include 152 subjects after the four-stage selection method from 1,025,340 patients. CONCLUSION: We could establish a method to select patients with history of sinus surgery and asthma treatment from a big data. This methodology using big data may contribute to identify relationship between sinus surgery and asthma treatment in the future.

Refinement and Evaluation of Korean Outpatient Groups for Visits with Multiple Procedures and Chemotherapy, and Medical Visit Indicators / 보건행정학회지

BACKGROUND: Issues concerning with the classification accuracy of Korean Outpatient Groups (KOPGs) have been raised by providers and researchers. The KOPG is an outpatient classification system used to measure casemix of outpatient visits and to adjust provider risk in charges by the Health Insurance Review & Assessment Service in managing insurance payments. The objective of this study were to refine KOPGs to improve the classification accuracy and to evaluate the refinement. METHODS: We refined the rules used to classify visits with multiple procedures, newly defined chemotherapy drug groups, and modified the medical visit indicators through reviews of other classification systems, data analyses, and consultations with experts. We assessed the improvement by measuring % of variation in case charges reduced by KOPGs and the refined system, Enhanced KOPGs (EKOPGs). We used claims data submitted by providers to the HIRA during the year 2012 in both refinement and evaluation. RESULTS: EKOPGs explicitly allowed additional payments for multiple procedures with exceptions of packaging of routine ancillary services and consolidation of related significant procedures, and discounts ranging from 30% to 70% were defined in additional payments. Thirteen chemotherapy drug KOPGs were added and medical visit indicators were streamlined to include codes for consultation fees for outpatient visits. The % of variance reduction achieved by EKOPGs was 48% for all patients whereas the figure was 40% for KOPGs, and the improvement was larger in data from tertiary and general hospitals than in data from clinics. CONCLUSION: A significant improvement in the performance of the KOPG was achieved by refining payments for visits with multiple procedures, defining groups for visits with chemotherapy, and revising medical visit indicators.

Initial Biopsy Outcome Prediction in Korean Patients-Comparison of a Noble Web-based Korean Prostate Cancer Risk Calculator versus Prostate-specific Antigen Testing

We developed and validated a novel Korean prostate cancer risk calculator (KPCRC) for predicting the probability of a positive initial prostate biopsy in a Korean population. Data were collected from 602 Koreans who underwent initial prostate biopsies due to an increased level of prostate-specific antigen (PSA), a palpable nodule upon digital rectal examination (DRE), or a hypoechoic lesion upon transrectal ultrasound (TRUS). The clinical and laboratory variables were analyzed by simple and multiple logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was computed to compare its performance to PSA testing alone. Prostate cancer was detected in 172 (28.6%) men. Independent predictors included age, DRE findings, PSA level, and prostate transitional zone volume. We developed the KPCRC using these variables. The AUC for the selected model was 0.91, and that of PSA testing alone was 0.83 (P < 0.001). The AUC for the selected model with an additional dataset was 0.79, and that of PSA testing alone was 0.73 (P = 0.004). The calculator is available on the website: http://dna.korea.ac.kr/PC-RISC/. The KPCRC improved the performance of PSA testing alone in predicting the risk of prostate cancer in a Korean population. This calculator would be a practical tool for physicians and patients.

Got target?: computational methods for microRNA target prediction and their extension

MicroRNAs (miRNAs) are a class of small RNAs of 19-23 nucleotides that regulate gene expression through target mRNA degradation or translational gene silencing. The miRNAs are reported to be involved in many biological processes, and the discovery of miRNAs has been provided great impacts on computational biology as well as traditional biology. Most miRNA-associated computational methods comprise the prediction of miRNA genes and their targets, and increasing numbers of computational algorithms and web-based resources are being developed to fulfill the need of scientists performing miRNA research. Here we summarize the rules to predict miRNA targets and introduce some computational algorithms that have been developed for miRNA target prediction and the application of the methods. In addition, the issue of target gene validation in an experimental way will be discussed.