Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species.

DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.

Rapid regulatory evolution of a nonrecombining autosome linked to divergent behavioral phenotypes.

In the white-throated sparrow (Zonotrichia albicollis), the second chromosome bears a striking resemblance to sex chromosomes. First, within each breeding pair of birds, one bird is homozygous for the standard arrangement of the chromosome (ZAL2/ZAL2) and its mate is heterozygous for a different version (ZAL2/ZAL2m). Second, recombination is profoundly suppressed between the two versions, leading to genetic differentiation between them. Third, the ZAL2m version is linked with phenotypic traits, such as bright plumage, high aggression, and low parental behavior, which are usually associated with males. These similarities to sex chromosomes suggest that the evolutionary mechanisms that shape sex chromosomes, in particular genetic degeneration of the heterogametic version due to the suppression of recombination, are likely important in this system as well. Here, we investigated patterns of protein sequence evolution and gene expression evolution between the ZAL2 and ZAL2m chromosomes by whole-genome sequencing and transcriptome analyses. Patterns of protein evolution exhibited only weak signals of genetic degeneration, and few genes harbored signatures of positive selection. We found substantial evidence of transcriptome evolution, such as significant expression divergence between ZAL2 and ZAL2m alleles and signatures of dosage compensation for highly expressed genes. These results suggest that, early in the evolution of heteromorphic chromosomes, gene expression divergence and dosage compensation can prevail before large-scale genetic degeneration. Our results show further that suppression of recombination between heteromorphic chromosomes can lead to the evolution of alternative (sex-like) behavioral phenotypes before substantial genetic degeneration.

Effectiveness of a community-based integrated service model for older adults living alone: A nonrandomized prospective study.

OBJECTIVE: Older adults living alone face physical, emotional, and social health problems, and prefer to age in place (AIP) in their homes. A community-based integrated model for AIP is needed and few studies have identified its impact on older adults living alone. METHODS: This was a non-randomized prospective study. Participants were 877 community-dwelling older adults living alone, aged above 65 years, in S* city in South Korea. The intervention group (n = 331) received a community-based integrated service (CBIS) model based on AIP for six months from October 2019 to April 2020. RESULTS: Scores on frailty (ß = -0.377, p < .001), loneliness (ß = -1.897, p = .018), and health-related quality of life (ß = 4.299, p = .021) significantly improved in the intervention group. Among the intervention group, loneliness scores significantly improved among participants aged under 80 years than those aged over 80 years. CONCLUSION: The CBIS model improved frailty, loneliness, and quality of life in community-dwelling older adults living alone.

HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene-Environment Interactions.

Gene-environment interaction (G×E) studies are one of the most important solutions for understanding the "missing heritability" problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene-Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene-alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

Functional conservation of sequence determinants at rapidly evolving regulatory regions across mammals.

Recent advances in epigenomics have made it possible to map genome-wide regulatory regions using empirical methods. Subsequent comparative epigenomic studies have revealed that regulatory regions diverge rapidly between genome of different species, and that the divergence is more pronounced in enhancers than in promoters. To understand genomic changes underlying these patterns, we investigated if we can identify specific sequence fragments that are over-enriched in regulatory regions, thus potentially contributing to regulatory functions of such regions. Here we report numerous sequence fragments that are statistically over-enriched in enhancers and promoters of different mammals (which we refer to as 'sequence determinants'). Interestingly, the degree of statistical enrichment, which presumably is associated with the degree of regulatory impacts of the specific sequence determinant, was significantly higher for promoter sequence determinants than enhancer sequence determinants. We further used a machine learning method to construct prediction models using sequence determinants. Remarkably, prediction models constructed from one species could be used to predict regulatory regions of other species with high accuracy. This observation indicates that even though the precise locations of regulatory regions diverge rapidly during evolution, the functional potential of sequence determinants underlying regulatory sequences may be conserved between species.

Association between mutations of critical pathway genes and survival outcomes according to the tumor location in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) develops through the alteration of several critical pathways. This study was aimed at evaluating the influence of critical pathways on survival outcomes for patients with CRC. METHODS: Targeted next-generation sequencing of 40 genes included in the 5 critical pathways of CRC (WNT, P53, RTK-RAS, phosphatidylinositol-4,5-bisphosphate 3-kinase [PI3K], and transforming growth factor ß [TGF-ß]) was performed for 516 patients with stage III or high-risk stage II CRC treated with surgery followed by adjuvant fluoropyrimidine and oxaliplatin chemotherapy. The associations between critical pathway mutations and relapse-free survival (RFS) and overall survival were analyzed. The associations were further analyzed according to the tumor location. RESULTS: The mutation rates for the WNT, P53, RTK-RAS, PI3K, and TGF-ß pathways were 84.5%, 69.0%, 60.7%, 30.0%, and 28.9%, respectively. A mutation in the PI3K pathway was associated with longer RFS (adjusted hazard ratio [HR], 0.59; 95% confidence interval [CI], 0.36-0.99), whereas a mutation in the RTK-RAS pathway was associated with shorter RFS (adjusted HR, 1.60; 95% CI, 1.01-2.52). Proximal tumors showed a higher mutation rate than distal tumors, and the mutation profile was different according to the tumor location. The mutation rates of Kirsten rat sarcoma viral oncogene homolog (KRAS), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α (PIK3CA), and B-Raf proto-oncogene serine/threonine kinase (BRAF) were higher in proximal tumors, and the mutation rates of adenomatous polyposis coli (APC), tumor protein 53 (TP53), and neuroblastoma RAS viral oncogene homolog (NRAS) were higher in distal tumors. The better RFS with the PI3K pathway mutation was significant only for proximal tumors, and the worse RFS with the RTK-RAS pathway mutation was significant only for distal tumors. CONCLUSIONS: A PI3K pathway mutation was associated with better RFS for CRC patients treated with adjuvant chemotherapy, and an RTK-RAS pathway mutation was associated with worse RFS. The significance of the prognostic impact differed according to the tumor location. Cancer 2017;123:3513-23. © 2017 American Cancer Society.

HNF4α is a therapeutic target that links AMPK to WNT signalling in early-stage gastric cancer.

BACKGROUND: Worldwide, gastric cancer (GC) is the fourth most common malignancy and the most common cancer in East Asia. Development of targeted therapies for this disease has focused on a few known oncogenes but has had limited effects. OBJECTIVE: To determine oncogenic mechanisms and novel therapeutic targets specific for GC by identifying commonly dysregulated genes from the tumours of both Asian-Pacific and Caucasian patients. METHODS: We generated transcriptomic profiles of 22 Caucasian GC tumours and their matched non-cancerous samples and performed an integrative analysis across different GC gene expression datasets. We examined the inhibition of commonly overexpressed oncogenes and their constituent signalling pathways by RNAi and/or pharmacological inhibition. RESULTS: Hepatocyte nuclear factor-4α (HNF4α) upregulation was a key signalling event in gastric tumours from both Caucasian and Asian patients, and HNF4α antagonism was antineoplastic. Perturbation experiments in GC tumour cell lines and xenograft models further demonstrated that HNF4α is downregulated by AMPKα signalling and the AMPK agonist metformin; blockade of HNF4α activity resulted in cyclin downregulation, cell cycle arrest and tumour growth inhibition. HNF4α also regulated WNT signalling through its target gene WNT5A, a potential prognostic marker of diffuse type gastric tumours. CONCLUSIONS: Our results indicate that HNF4α is a targetable oncoprotein in GC, is regulated by AMPK signalling through AMPKα and resides upstream of WNT signalling. HNF4α may regulate 'metabolic switch' characteristic of a general malignant phenotype and its target WNT5A has potential prognostic values. The AMPKα-HNF4α-WNT5A signalling cascade represents a potentially targetable pathway for drug development.

Prevalence, behavioral manifestations and associated individual and climatic factors of seasonality in the Korean general population.

BACKGROUNDS: Seasonality, an individual trait of seasonal variations in mood and behavior, has received clinical attention for its association with mood disorders. This study aimed to explore the prevalence, specific manifestation, and associated individual and climatic factors of seasonality in the non-elderly adult population. METHODS: Five hundred fifty-two participants [male n=220; female n=332; mean age 34.92years, standard deviation (SD) 10.18] with no psychiatric history were recruited from the Seoul metropolitan area (37°33'58.87″N 126°58'40.63″E). Seasonality was evaluated using the Seasonal Pattern Assessment Questionnaire. Climatic variables used in analyses were averaged over recent 5years (from 2008 to 2013) on a monthly basis. RESULTS: The mean global seasonality score (GSS) was 5.53 (SD 3.91), and 16.2% (n=89) of participants had seasonal affective disorder (SAD) or sub-SAD. The "feeling worst" month in most of the participants with significant seasonality were winter (41.6%) or summer (38.2%). Socio-demographic factors including age and sex were not related to the seasonality. Decreased sunlight amount and diurnal temperature range in a given and previous month, and increased humidity in a previous month showed significant associations with the percentage of participants with the worst mood. The most frequently reported symptom related to seasonality was 'changes in energy level'. Specific manifestations were not significantly different between the winter type and the summer type. CONCLUSION: The summer and winter type seasonality in the non-clinical adult population did not differ in terms of behavioral manifestations. Decreased sunlight amount, diurnal temperature range, and increased humidity appeared to be major climatic factors associated with seasonality.

Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information.

BACKGROUND: Whole genome sequencing of bisulfite converted DNA ('methylC-seq') method provides comprehensive information of DNA methylation. An important application of these whole genome methylation maps is classifying each position as a methylated versus non-methylated nucleotide. A widely used current method for this purpose, the so-called binomial method, is intuitive and straightforward, but lacks power when the sequence coverage and the genome-wide methylation level are low. These problems present a particular challenge when analyzing sparsely methylated genomes, such as those of many invertebrates and plants. RESULTS: We demonstrate that the number of sequence reads per position from methylC-seq data displays a large variance and can be modeled as a shifted negative binomial distribution. We also show that DNA methylation levels of adjacent CpG sites are correlated, and this similarity in local DNA methylation levels extends several kilobases. Taking these observations into account, we propose a new method based on Bayesian classification to infer DNA methylation status while considering the neighborhood DNA methylation levels of a specific site. We show that our approach has higher sensitivity and better classification performance than the binomial method via multiple analyses, including computational simulations, Area Under Curve (AUC) analyses, and improved consistencies across biological replicates. This method is especially advantageous in the analyses of sparsely methylated genomes with low coverage. CONCLUSIONS: Our method improves the existing binomial method for binary methylation calls by utilizing a posterior odds framework and incorporating local methylation information. This method should be widely applicable to the analyses of methylC-seq data from diverse sparsely methylated genomes. Bis-Class and example data are provided at a dedicated website (http://bibs.snu.ac.kr/software/Bisclass).

Single nucleotide polymorphism in the FLT4 gene is associated with atopic dermatitis in Koreans.

BACKGROUND: The histology of atopic dermatitis includes dilated, tortuous vessels within the papillary dermis and perivascular edema. METHOD: This study included 1120 case-control samples (646 AD patients and 474 normal controls), for which we genotyped 34 SNPs from four VEGF family genes and the FLT4 gene. For the rs11607007 SNP in the VEGFB gene and three SNPs (rs10085109, rs3736062, and rs11949194) in the FLT4 gene, which had significant p-values in the initial stage, were further investigated using 1132 independent samples (440 AD patients and 692 normal controls). RESULT: Of the four SNPs, rs10085109 in the FLT4 gene was only significantly associated with the AD phenotype in both initial and replication samples. Although no SNPs in the VEGFA gene were significantly associated with AD, the rs2010963 SNP had a marginally significant effect on log-eosinophil counts. CONCLUSION: The rs10085109 SNP in the FLT4 gene were associated with susceptibility to AD.

[An Investigation of the Cumulative Effects of Depressive Symptoms on the Cognitive Function in Community-Dwelling Older Adults: Analysis of the Korean Longitudinal Study of Aging].

PURPOSE: This study investigated the cumulative effects of depressive symptoms on cognitive function over time in community-dwelling older adults. METHODS: Data were investigated from 2,533 community-dwelling older adults who participated in the Korean Longitudinal Study of Aging (KLoSA) from the 5th (2014) to the 8th wave (2020). The association between cumulative depressive symptoms and cognitive function was identified through multiple regression analysis. RESULTS: When the multiple regression analysis was conducted from each wave, the current depressive symptoms scores and cognitive function scores were negatively associated, regardless of the waves (B5th = -0.26, B6th = -0.26, B7th = -0.26, and B8th = -0.27; all p < .001). Further, when all the previous depressive symptoms scores were added as explanatory variables in the 8th wave, the current one (B8th = -0.09, p < .001) and the previous ones (B5th = -0.11, B6th = -0.09, and B7th = -0.13; all p < .001) were also negatively associated with the cognitive function score. The delta R², which indicates the difference between the model's R² with and without the depressive symptoms scores, was greater in the model with all the previous and current depressive symptoms scores (6.4%) than in the model with only the current depressive symptoms score (3.6%). CONCLUSION: Depressive symptoms in older adults have a long-term impact. This results in an accumulated adverse effect on the cognitive function. Therefore, to prevent cognitive decline in older adults, we suggest detecting their depressive symptoms early and providing continuous intervention to reduce exposure to long-term depressive symptoms.

Associations between weekend catch-up sleep and health-related quality of life with focusing on gender differences.

This study investigated associations between weekend catch-up sleep (WCUS) and health-related quality of life (HRQoL) in 15,837 participants from the 7th (2016-2018) Korea National Health and Nutrition Examination Survey. We categorized WCUS durations into four groups: none (≤ 0 h [h]), short (> 0 h, ≤ 1 h), medium (> 1 h, ≤ 2 h), and long (> 2 h), and performed complex samples logistic regression and likelihood ratio χ2 test. The study found significant associations in women for the European Quality of Life-5 Dimensions (EQ-5D) index and three EQ-5D subdomains (self-care, usual activities, and anxiety/depression) with the WCUS durations, but no significant association in men. Compared to the non-WCUS, the short or medium WCUS was positively associated with the EQ-5D index and EQ-5D subdomains (usual activities and anxiety/depression) in women, while the long WCUS significantly reduced the quality of life in the self-care domain. In an additional subgroup analysis by age, middle-aged and elderly women had a more noticeable effect of WCUS on HRQoL than young women, and the short or medium WCUS improved HRQoL in middle-aged and elderly women in general. Therefore, we recommend appropriate WCUS durations to improve HRQoL, considering both gender and age.

Effects of educational intervention on mortality and patient-reported outcomes in individuals with heart failure: A randomized controlled trial.

OBJECTIVE: To examine the effects of an educational intervention on patient-reported outcomes and all-cause mortality in heart failure (HF) patients METHODS: In this randomized controlled trial, we enrolled 122 hospitalized patients with HF. The intervention group (n = 60) received an individual nurse-led education session on HF self-management during hospitalization and three telephone calls after discharge. The control group (n = 62) received care as usual. Patient-reported outcomes were measured at baseline and at 3 and 6 months. Mortality status was determined using the National Death Records. Intervention effects were evaluated using the Cox proportional hazards regression model and linear mixed models. RESULTS: During the follow-up (median: 568 days), 7 deaths (12%) in the intervention group and 15 deaths (24%) in the control group occurred (adjusted hazard ratio, 0.40; 95% confidence interval, 0.16-0.98; P = .046). From baseline to 3 and 6 months, the intervention group showed greater improvements in HF knowledge (difference=6.14, P = .03; difference=5.76, P = .02, respectively), self-care (difference=-6.08, P < .001; difference=-6.16, P < .001, respectively), and health-related quality of life (difference=-11.90, P = .01; difference=-14.57, P = .004, respectively) than the control group. CONCLUSION: Educational intervention with telephone follow-up reduced all-cause mortality and improved patient-reported outcomes. PRACTICE IMPLICATION: Educational intervention should be considered as part of routine care for HF patients.

Impact of COVID-19 on the social relationships and mental health of older adults living alone: A two-year prospective cohort study.

BACKGROUND: Owing to the COVID-19 outbreak, older adults living alone, who can only connect socially outside their homes, are at risk of social isolation and poor mental health. This study aimed to identify the changes, before and after COVID-19, by sex and age, in social relationships (social activity, social network, and social support) and mental health (depression and suicide ideation) among older adults living alone. METHODS: This is a prospective cohort study of community-dwelling older adults who were at least 65 years old and living alone in South Korea. The study was conducted during 2018-2020 with 2,291 participants (795, 771, and 725 for the 1st to 3rd waves, respectively). The data were collected via face-to-face interviews. A generalized linear mixed modeling framework was used to test for changes over three years. RESULTS: Social activity was reduced after the COVID-19, with an interaction effect of sex: older women (odds ratio [OR], 0.19; 95% confidence interval [CI], 0.15-0.23; p < .001) showed greater reduction than older men (OR, 0.50; 95% CI, 0.34-0.75; p < .001). Interaction with neighbors also reduced after the pandemic, but there was no significant evidence of interaction effects. Interaction with family members increased in both sexes during the pandemic, with the interaction effect of sex: older women (OR, 1.40; 95% CI, 1.11-1.76; p = .004) showed greater increase than men (OR, 1.55; 95% CI, 1.13-2.14; p = .007). Social support increased in both sexes during the pandemic, but there was no significant evidence of interaction effects. Depression and suicide ideation showed no significant differences before and after the pandemic. CONCLUSIONS: The findings provide health administrators and health providers with explorative insights into the impact of the COVID-19 on social relationships and mental health among older adults living alone and can guide further studies of interventions considering specific properties of social relationships.

Meta-Analysis of Randomized Clinical Trials Evaluating Effectiveness of a Multivitamin Supplementation against Oxidative Stress in Healthy Subjects.

A meta-analysis has been widely applied to draw general conclusions using a set of studies with similar purposes and designs. This study aimed to perform a meta-analysis of six randomized placebo-controlled trials, independently conducted for the relationship between a plant-based multivitamin/mineral supplementation (PMS) and oxidative stress for 6 to 8 weeks, to provide overall estimates of those effects. In detail, linear mixed model analysis was first conducted on each study to obtain individual estimates; then, two types of meta-analysis were applied to combine the individual estimates from all available studies (overall meta-analysis) and region-specific studies (subgroup meta-analysis). In the meta-analysis, we selected 19 biomarker variables that overlapped in at least two studies and found 6 variables significant in at least one meta-analysis. The overall estimates of beta coefficients were 0.17 for vitamin C, 0.80 for vitamin B6, 0.46 for vitamin B12, 0.81 for folate, 0.36 for ß-carotene, and -0.17 for oxidized LDL (ox-LDL). Subsequent association analysis revealed significant negative correlations between plasma free radical scavenging nutrients and plasma ox-LDL levels, indicating a general benefit of PMS in alleviating oxidative stress by providing exogenous oxidant scavengers.

Development and Multiple Validation of the Protein Multi-marker Panel for Diagnosis of Pancreatic Cancer.

PURPOSE: To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance. EXPERIMENTAL DESIGN: A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls. RESULTS: A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets. CONCLUSIONS: This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.

Evolution of DNA methylation in the human brain.

DNA methylation is a critical regulatory mechanism implicated in development, learning, memory, and disease in the human brain. Here we have elucidated DNA methylation changes during recent human brain evolution. We demonstrate dynamic evolutionary trajectories of DNA methylation in cell-type and cytosine-context specific manner. Specifically, DNA methylation in non-CG context, namely CH methylation, has increased (hypermethylation) in neuronal gene bodies during human brain evolution, contributing to human-specific down-regulation of genes and co-expression modules. The effects of CH hypermethylation is particularly pronounced in early development and neuronal subtypes. In contrast, DNA methylation in CG context shows pronounced reduction (hypomethylation) in human brains, notably in cis-regulatory regions, leading to upregulation of downstream genes. We show that the majority of differential CG methylation between neurons and oligodendrocytes originated before the divergence of hominoids and catarrhine monkeys, and harbors strong signal for genetic risk for schizophrenia. Remarkably, a substantial portion of differential CG methylation between neurons and oligodendrocytes emerged in the human lineage since the divergence from the chimpanzee lineage and carries significant genetic risk for schizophrenia. Therefore, recent epigenetic evolution of human cortex has shaped the cellular regulatory landscape and contributed to the increased vulnerability to neuropsychiatric diseases.

A Clinically Applicable 24-Protein Model for Classifying Risk Subgroups in Pancreatic Ductal Adenocarcinomas using Multiple Reaction Monitoring-Mass Spectrometry.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) subtypes have been identified using various methodologies. However, it is a challenge to develop classification system applicable to routine clinical evaluation. We aimed to identify risk subgroups based on molecular features and develop a classification model that was more suited for clinical applications. EXPERIMENTAL DESIGN: We collected whole dissected specimens from 225 patients who underwent surgery at Seoul National University Hospital [Seoul, Republic of Korea (South)], between October 2009 and February 2018. Target proteins with potential relevance to tumor progression or prognosis were quantified with robust quality controls. We used hierarchical clustering analysis to identify risk subgroups. A random forest classification model was developed to predict the identified risk subgroups, and the model was validated using transcriptomic datasets from external cohorts (N = 700), with survival analysis. RESULTS: We identified 24 protein features that could classify the four risk subgroups associated with patient outcomes: stable, exocrine-like; activated, and extracellular matrix (ECM) remodeling. The "stable" risk subgroup was characterized by proteins that were associated with differentiation and tumor suppressors. "Exocrine-like" tumors highly expressed pancreatic enzymes. Two high-risk subgroups, "activated" and "ECM remodeling," were enriched in terms such as cell cycle, angiogenesis, immunocompetence, tumor invasion metastasis, and metabolic reprogramming. The classification model that included these features made prognoses with relative accuracy and precision in multiple cohorts. CONCLUSIONS: We proposed PDAC risk subgroups and developed a classification model that may potentially be useful for routine clinical implementations, at the individual level. This clinical system may improve the accuracy of risk prediction and treatment guidelines.See related commentary by Thakur and Singh, p. 3272.

Enhanced Permutation Tests via Multiple Pruning.

Big multi-omics data in bioinformatics often consists of a huge number of features and relatively small numbers of samples. In addition, features from multi-omics data have their own specific characteristics depending on whether they are from genomics, proteomics, metabolomics, etc. Due to these distinct characteristics, standard statistical analyses using parametric-based assumptions may sometimes fail to provide exact asymptotic results. To resolve this issue, permutation tests can be a way to exactly analyze multi-omics data because they are distribution-free and flexible to use. In permutation tests, p-values are evaluated by estimating the locations of test statistics in an empirical null distribution generated by random shuffling. However, the permutation approach can be infeasible when the number of features increases, because more stringent control of type I error is needed for multiple hypothesis testing, and consequently, much larger numbers of permutations are required to reach significance. To address this problem, we propose a well-organized strategy, "ENhanced Permutation tests via multiple Pruning (ENPP)." ENPP prunes the features in every permutation round if they are determined to be non-significant. In other words, if the feature statistics from the permuted datasets exceed the feature statistics from the original dataset, beyond a predetermined threshold, the feature is determined to be non-significant. If so, ENPP removes the feature and iterates the process without the feature in the next permutation round. Our simulation study showed that the ENPP method could remove about 50% of the features at the first permutation round, and, by the 100th permutation round, 98% of the features had been removed and only 7.4% of the computation time with the original unpruned permutation approach had elapsed. In addition, we applied this approach to a real data set (Korea Association REsource: KARE) of 327,872 SNPs to find association with a non-normally distributed phenotype (fasting plasma glucose), interpreted the results, and discussed the feasibility and advantages of the approach.

Association between Health Problems and Turnover Intention in Shift Work Nurses: Health Problem Clustering.

Shift work nurses experience multiple health problems due to irregular shifts and heavy job demands. However, the comorbidity patterns of nurses' health problems and the association between health problems and turnover intention have rarely been studied. This study aimed to identify and cluster shift work nurses' health problems and to reveal the associations between health problems and turnover intention. In this cross-sectional study, we analyzed data from 500 nurses who worked at two tertiary hospitals in Seoul, South Korea. Data, including turnover intention and nine types of health issues, were collected between March 2018 and April 2019. Hierarchical clustering and multiple ordinal logistic regressions were used for the data analysis. Among the participants, 22.2% expressed turnover intention and the mean number of health problems was 4.5 (range 0-9). Using multiple ordinal logistic regressions analysis, it was shown that sleep disturbance, depression, fatigue, a gastrointestinal disorder, and leg or foot discomfort as a single health problem significantly increased turnover intention. After clustering the health problems, four clusters were identified and only the neuropsychological cluster-sleep disturbance, fatigue, and depression-significantly increased turnover intention. We propose that health problems within the neuropsychological cluster must receive close attention and be addressed simultaneously to decrease nurse's turnover intentions.