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Investigating how genes jointly affect complex human diseases is important, yet challenging. The network approach (e.g., weighted gene co-expression network analysis (WGCNA)) is a powerful tool. However, genomic data usually contain substantial batch effects, which could mask true genomic signals. Paired design is a powerful tool that can reduce batch effects. However, it is currently unclear how to appropriately apply WGCNA to genomic data from paired design. In this paper, we modified the current WGCNA pipeline to analyse high-throughput genomic data from paired design. We illustrated the modified WGCNA pipeline by analysing the miRNA dataset provided by Shiah et al . (2014), which contains forty oral squamous cell carcinoma (OSCC) specimens and their matched non-tumourous epithelial counterparts. OSCC is the sixth most common cancer worldwide. The modified WGCNA pipeline identified two sets of novel miRNAs associated with OSCC, in addition to the existing miRNAs reported by Shiah et al . (2014). Thus, this work will be of great interest to readers of various scientific disciplines, in particular, genetic and genomic scientists as well as medical scientists working on cancer.

The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of complex human diseases. A key step to characterize the genetics of a complex human disease is to unbiasedly identify disease-associated gene transcripts on a whole-genome scale. Confounding factors could cause false positives. Paired design, such as measuring gene expression before and after treatment for the same subject, can reduce the effect of known confounding factors. However, not all known confounding factors can be controlled in a paired/match design. Model-based clustering, such as mixtures of hierarchical models, has been proposed to detect gene transcripts differentially expressed between paired samples. To the best of our knowledge, no model-based gene clustering methods have the capacity to adjust for the effects of covariates yet. In this article, we proposed a novel mixture of hierarchical models with covariate adjustment in identifying differentially expressed transcripts using high-throughput whole-genome data from paired design. Both simulation study and real data analysis show the good performance of the proposed method.

Coronary artery disease remains the leading cause of death in developed countries despite significant progress in primary prevention and treatment strategies. Older patients are at particularly high risk of poor outcomes following acute coronary syndrome and impaired nutrition, including low vitamin D levels, may play a role. The extraskeletal effects of vitamin D, in particular, its role in maintaining a healthy cardiovascular system are receiving increased attention. Longitudinal studies have demonstrated increased cardiovascular mortality and morbidity associated with vitamin D deficiency. Low vitamin D levels have been linked to inflammation, higher coronary artery calcium scores, impaired endothelial function and increased vascular stiffness. However, so far, few randomized controlled trials have investigated the potential benefits of vitamin D supplementation in preventing cardiovascular events, and most available trials have tested low doses of supplementation in relatively low-risk populations. Whether vitamin D supplementation will be beneficial among patients with coronary artery disease, including high risk older patients presenting with acute coronary syndrome, is unknown and warrants further investigation.

Patients with asthma vary markedly in their clinical response to inhaled glucocorticoids. These investigators used a novel approach to identify a common variant in the glucocorticoid-induced transcript 1 gene associated with a decreased response to glucocorticoids. Asthma is a complex genetic syndrome that affects 300 million persons worldwide. 1 The response to treatment is also genetically complex and is characterized by high intraindividual repeatability 2 and high interindividual variability, 3 with up to 40% of patients with asthma having no response to therapy. Inhaled glucocorticoids are the most widely prescribed medications for controlling asthma. Levels of endogenous glucocorticoids are heritable and vary, both at baseline and in response to environmental perturbation. 4 – 6 Moreover, studies in families with conditions other than asthma have shown both familial segregation and heritability in responses to glucocorticoid medications. 7 , 8 Given the heritability within the . . .

Abstract Rationale Chronic obstructive pulmonary disease (COPD) is associated with local (lung) and systemic (blood) inflammation and manifestations. DNA methylation is an important regulator of gene transcription, and global and specific gene methylation marks may vary with cigarette smoke exposure. Objectives To perform a comprehensive assessment of methylation marks in DNA from subjects well phenotyped for nonneoplastic lung disease. Methods We conducted array-based methylation screens, using a test-replication approach, in two family-based cohorts (n = 1,085 and 369 subjects). Measurements and Main Results We observed 349 CpG sites significantly associated with the presence and severity of COPD in both cohorts. Seventy percent of the associated CpG sites were outside of CpG islands, with the majority of CpG sites relatively hypomethylated. Gene ontology analysis based on these 349 CpGs (330 genes) suggested the involvement of a number of genes responsible for immune and inflammatory system pathways, responses to stress and external stimuli, as well as wound healing and coagulation cascades. Interestingly, our observations include significant, replicable associations between SERPINA1 hypomethylation and COPD and lower average lung function phenotypes (combined P values: COPD, 1.5 × 10−23; FEV1/FVC, 1.5 × 10−35; FEV1, 2.2 × 10−40). Conclusions Genetic and epigenetic pathways may both contribute to COPD. Many of the top associations between COPD and DNA methylation occur in biologically plausible pathways. This large-scale analysis suggests that DNA methylation may be a biomarker of COPD and may highlight new pathways of COPD pathogenesis.

Background With poor prognosis and limited treatment options for advanced hepatocellular carcinoma (HCC), development of novel therapeutic agents is urgently needed. This single-arm phase I study sought to assess the safety and preliminary efficacy of icaritin in human as a potential oral immunotherapy in addition to the immune-checkpoint inhibitors. Methods Eligible advanced HCC patients with Child-Pugh Class A or B were administered with a fixed oral dose of icaritin at either 600 or 800 mg b.i.d. The primary endpoint was safety, and the secondary endpoints included time-to-progression (TTP), overall survival (OS) and the clinical benefit rate (CBR). Icaritin treatment induced immune biomarkers and immune-modulating activities in myeloid cells were also explored. Results No drug-related adverse events ≥ Grade 3 were observed in all 20 enrolled HCC patients. Among the 15 evaluable patients, 7 (46.7%) achieved clinical benefit, representing one partial response (PR, 6.7%) and 6 stable disease (SD, 40%). The median TTP was 141 days (range: 20-343 days), and the median OS was 192 days (range: 33-1036 days). Durable survival was observed in PR/SD patients with a median OS of 488 days (range: 72-773). TTP was significantly associated with the dynamic changes of peripheral neutrophils ( p  = 0.0067) and lymphocytes ( p  = 0.0337). Icaritin treatment induced changes in immune biomarkers-and immune-suppressive myeloid cells were observed. Conclusions Icaritin demonstrated safety profiles and preliminary durable survival benefits in advanced HCC patients, which were correlated with its immune-modulation activities and immune biomarkers. These results suggested the potential of icaritin as a novel oral immunotherapy for advanced HCC in addition to antibody-based PD-1/PD-L1 blockade therapies. Trial registration Clinicaltrial.gov identifier. NCT02496949 (retrospectively registered, July 14, 2015).

To determine gender differences and predictors of all-cause mortality (30 days and 1 year) after percutaneous coronary intervention (PCI) in patients with stable angina pectoris and acute coronary syndrome (non–ST-elevation myocardial infarction/unstable angina pectoris and ST-elevation myocardial infarction) in the British Cardiovascular Intervention Society (BCIS) and Swedish Coronary Angiography and Angioplasty Registry (SCAAR) data sets, an analysis of prospectively collected data from 2007 to 2011 was performed. In total, 458,261 patients (BCIS: n = 368,492 [25.9% women]; Sweden: n = 89,769 [27.2% women]) who underwent PCI were included in this analysis. Using multiple regression analysis, in the BCIS registry, female gender was an independent predictor of all-cause mortality at 30 days (odds ratio [OR] 1.15, 95% CI 1.10 to 1.22, p <0.0001) and at 1 year (OR 1.08, 95% CI 1.04 to 1.12, p <0.0001) after PCI for all patients. Likewise, in the SCAAR registry, female gender was an independent predictor of all-cause mortality at 30 days (OR 1.15, 95% CI 1.05 to 1.26, p = 0.002) and 1 year (OR 1.09, 95% CI 1.03 to 1.17, p = 0.006) after PCI for all patients. In both data sets, there was no statistically significant interaction between age and gender for all-cause mortality at 30 days (BCIS, p = 0.59; SCAAR, p = 0.40) and at 1 year (BCIS, p = 0.11; SCAAR, p = 0.83). In conclusion, despite advances in care, women compared with men continue to experience higher all-cause mortality after PCI for coronary artery disease. The patient's age at the time of PCI remains a strong predictive factor of mortality in this population. Strategies and further research are warranted to better address the management of coronary artery disease in women with possibly earlier diagnosis and more tailored treatments.

We propose eight data transformations ( r , r2 , rv , rv2 , l , l2 , lv , and lv2 ) for RNA-seq data analysis aiming to make the transformed sample mean to be representative of the distribution center since it is not always possible to transform count data to satisfy the normality assumption. Simulation studies showed that for data sets with small (e.g., nCases = nControls = 3) or large sample size (e.g., nCases = nControls = 100) limma based on data from the l , l2 , and r2 transformations performed better than limma based on data from the voom transformation in term of accuracy, FDR, and FNR. For datasets with moderate sample size (e.g., nCases = nControls = 30 or 50), limma with the rv and rv2 transformations performed similarly to limma with the voom transformation. Real data analysis results are consistent with simulation analysis results: limma with the r, l, r2 , and l2 transformation performed better than limma with the voom transformation when sample sizes are small or large; limma with the rv and rv2 transformations performed similarly to limma with the voom transformation when sample sizes are moderate. We also observed from our data analyses that for datasets with large sample size, the gene-selection via the Wilcoxon rank sum test (a non-parametric two sample test method) based on the raw data outperformed limma based on the transformed data.

Excess body-mass index (BMI) has been associated with adverse outcomes in prostate cancer, and hyperinsulinaemia is a candidate mediator, but prospective data are sparse. We assessed the effect of prediagnostic BMI and plasma C-peptide concentration (reflecting insulin secretion) on prostate cancer-specific mortality after diagnosis. This study involved men diagnosed with prostate cancer during the 24 years of follow-up in the Physicians' Health Study. BMI measurements were available at baseline in 1982 and eight years later in 1990 for 2546 men who developed prostate cancer. Baseline C-peptide concentration was available in 827 men. We used Cox proportional hazards regression models controlling for age, smoking, time between BMI measurement and prostate cancer diagnosis, and competing causes of death to assess the risk of prostate cancer-specific mortality according to BMI and C-peptide concentration. Of the 2546 men diagnosed with prostate cancer during the follow-up period, 989 (38·8%) were overweight (BMI 25·0–29·9 kg/m 2) and 87 (3·4%) were obese (BMI ≥30 kg/m 2). 281 men (11%) died from prostate cancer during this follow-up period. Compared with men of a healthy weight (BMI <25 kg/m 2) at baseline, overweight men and obese men had a significantly higher risk of prostate cancer mortality (proportional hazard ratio [HR] 1·47 [95% CI 1·16–1·88] for overweight men and 2·66 [1·62–4·39] for obese men; p trend<0·0001). The trend remained significant after controlling for clinical stage and Gleason grade and was stronger for prostate cancer diagnosed during the PSA screening era (1991–2007) compared with during the pre-PSA screening era (1982–1990) or when using BMI measurements obtained in 1990 compared with those obtained in 1982. Of the 827 men with data available for baseline C-peptide concentration, 117 (14%) died from prostate cancer. Men with C-peptide concentrations in the highest quartile (high) versus the lowest quartile (low) had a higher risk of prostate cancer mortality (HR 2·38 [95% CI 1·31–4·30]; p trend=0·008). Compared with men with a BMI less than 25 kg/m 2 and low C-peptide concentrations, those with a BMI of 25 kg/m 2 or more and high C-peptide concentrations had a four-times higher risk of mortality (4·12 [1·97–8·61]; p interaction=0·001) independent of clinical predictors. Excess bodyweight and a high plasma concentration of C-peptide both predispose men with a subsequent diagnosis of prostate cancer to an increased likelihood of dying of their disease. Patients with both factors have the worst outcome. Further studies are now needed to confirm these findings. The National Institutes of Health research grants CA42182, CA90598, CA58684, CA34944, CA40360, HL26490, HL34595, the National Cancer Institute of Canada, and the Prostate Cancer Foundation, Santa Monica, CA, USA.

Background Recently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. However, AW’s method seems to be quite conservative and has not been fully compared with existing joint tests. Results We proposed three improved joint score tests, namely iAW.Lev, iAW.BF, and iAW.TM, and have made extensive comparisons with the joint likelihood ratio test (jointLRT), the Kolmogorov-Smirnov (KS) test, and the AW test. Systematic simulation studies showed that: 1) the three improved tests performed better (i.e., having larger power, while keeping nominal Type I error rates) than the other three tests for data with outliers and having different variances between cases and controls; 2) for data from normal distributions, the three improved tests had slightly lower power than jointLRT and AW. The analyses of two Illumina HumanMethylation27 data sets GSE37020 and GSE20080 and one Illumina Infinium MethylationEPIC data set GSE107080 demonstrated that three improved tests had higher true validation rates than those from jointLRT, KS, and AW. Conclusions The three proposed joint score tests are robust against the violation of normality assumption and presence of outlying observations in comparison with other three existing tests. Among the three proposed tests, iAW.BF seems to be the most robust and effective one for all simulated scenarios and also in real data analyses.