Metabolic and Cardiovascular Benefits of Apple and Apple-Derived Products: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Background: Quantitative evidence of the metabolic and cardiovascular effects of apples (Malus domestica) is lacking in interventional studies. This study aimed to summarize the available evidence of the beneficial effects of apples and apple-derived products (ADPs) on metabolic and cardiovascular markers. Methods: Peer-reviewed randomized controlled trials (RCTs) were identified from four databases on May 3, 2021 and regularly updated until the end of May 2021. Demographic characteristics, intervention types, and evaluation parameters were extracted. A meta-analysis on the mean difference of change scores was conducted on commonly presented outcomes in the RCTs. Results: The metabolic and cardiovascular effects of diverse regimens, including whole apple, apple extract, and apple juice, were examined in 18 eligible RCTs. Nine common evaluation outcomes were eventually introduced to the meta-analysis, including total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride, glucose, insulin, C-reactive protein, and systolic/diastolic blood pressures. The levels of TC (-2.69 mg/dL; 95% CI: -5.43, 0.04 mg/dL) and LDL (-2.80 mg/dL; 95% CI: -5.78, 0.17 mg/dL) showed a non-significant decreasing tendency after at least a week of apple consumption. Further subgroup analysis, particularly, a comparison with placebo as a control, showed a significant reduction in TC and LDL levels. When stratified by the baseline level, subjects with high TC and LDL level were shown to have more benefits from the apple intake. Intriguingly, apple and ADPs significantly reduced HDL levels to a small extent (-1.04 mg/dL; 95% CI: -1.79, -0.29 mg/dL). The other markers were mostly unaffected by the intervention. Conclusion: Our investigation revealed that apples could improve blood cholesterol levels. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42020215977].

RCRdiff: A fully integrated Bayesian method for differential expression analysis using raw NanoString nCounter data.

The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.

Intravenous immunoglobulins as first-line treatment in idiopathic inflammatory myopathies: a pilot study.

OBJECTIVES: We explored efficacy and safety of IVIg as first-line treatment in patients with an idiopathic inflammatory myopathy. METHODS: In this investigator-initiated phase 2 open-label study, we included 20 adults with a newly diagnosed, biopsy-proven idiopathic inflammatory myopathy, and a disease duration of less than 9 months. Patients with IBM and prior use of immunosuppressants were excluded. The standard treatment regimen consisted of IVIg (Privigen) monotherapy for 9 weeks: a loading dose (2 g/kg body weight) and two subsequent maintenance doses (1 g/kg body weight) with a 3-week interval. The primary outcome was the number of patients with at least moderate improvement on the 2016 ACR/EULAR Total Improvement Score. Secondary outcomes included time to improvement, the number of patients requiring rescue medication and serious adverse events. RESULTS: We included patients with DM (n = 9), immune-mediated necrotizing myopathy (n = 6), non-specific myositis/overlap myositis (n = 4) and anti-synthetase syndrome (n = 1). One patient was excluded from analyses because of minimal weakness resulting in a ceiling effect. Eight patients (8/19 = 42.0%; Clopper-Pearson 95% CI: 19.6, 64.6) had at least moderate improvement by 9 weeks. Of these, six reached improvement by 3 weeks. Seven patients required rescue medication due to insufficient efficacy and prematurely ended the study. Three serious adverse events occurred, of which one was pulmonary embolism. CONCLUSION: First-line IVIg monotherapy led to at least moderate improvement in nearly half of patients with a fast clinical response in the majority of responders. TRIAL REGISTRATION: Netherlands Trial Register identifier, NTR6160.

Biomarker profiles of endothelial activation and dysfunction in rare systemic autoimmune diseases: implications for cardiovascular risk.

OBJECTIVES: Vasculopathy is an important hallmark of systemic chronic inflammatory connective tissue diseases (CICTD) and is associated with increased cardiovascular risk. We investigated disease-specific biomarker profiles associated with endothelial dysfunction, angiogenic homeostasis and (tissue) inflammation, and their relation to disease activity in rare CICTD. METHODS: A total of 38 serum proteins associated with endothelial (dys)function and inflammation were measured by multiplex-immunoassay in treatment-naive patients with localized scleroderma (LoS, 30), eosinophilic fasciitis (EF, 8) or (juvenile) dermatomyositis (34), 119 (follow-up) samples during treatment, and 65 controls. Data were analysed by unsupervised clustering, Spearman correlations, non-parametric t test and ANOVA. RESULTS: The systemic CICTD, EF and dermatomyositis, had distinct biomarker profiles, with 'signature' markers galectin-9 (dermatomyositis) and CCL4, CCL18, CXCL9, fetuin, fibronectin, galectin-1 and TSP-1 (EF). In LoS, CCL18, CXCL9 and CXCL10 were subtly increased. Furthermore, dermatomyositis and EF shared upregulation of markers related to interferon (CCL2, CXCL10), endothelial activation (VCAM-1), inhibition of angiogenesis (angiopoietin-2, sVEGFR-1) and inflammation/leucocyte chemo-attraction (CCL19, CXCL13, IL-18, YKL-40), as well as disturbance of the Angiopoietin-Tie receptor system and VEGF-VEGFR system. These profiles were related to disease activity, and largely normalized during treatment. However, a subgroup of CICTD patients showed continued elevation of CXCL10, CXCL13, galectin-9, IL-18, TNFR2, VCAM-1, and/or YKL-40 during clinically inactive disease, possibly indicating subclinical interferon-driven inflammation and/or endothelial dysfunction. CONCLUSION: CICTD-specific biomarker profiles revealed an anti-angiogenic, interferon-driven environment during active disease, with incomplete normalization under treatment. This warrants further investigation into monitoring of vascular biomarkers during clinical follow-up, or targeted interventions to minimize cardiovascular risk in the long term.

Isolation and Metabolic Assessment of Cancer Cell Mitochondria.

Mitochondrial metabolism plays an essential role in various biological processes of cancer cells. Herein, we established an experimental procedure for the metabolic assessment of mitochondria in cancer cells. We examined procedures for mitochondrial isolation coupled with various mitochondrial extraction buffers in three major cancer cell lines (PANC1, A549, and MDA-MB-231) and identified a potentially optimal and generalized approach. The purity of the mitochondrial fraction isolated by the selected protocol was verified using specific protein markers of cellular components, and the ultrastructure of the isolated mitochondria was also analyzed by transmission electron microscopy. The isolation procedure, involving a bead beater for cell lysis, a modified sucrose buffer, and differential centrifugation, appeared to be a suitable method for the extraction of mitochondria from cancer cells. Electron micrographs indicated an intact two-layer membrane and inner structures of mitochondria isolated by this procedure. Metabolomic and lipidomic analyses were conducted to examine the metabolic phenotypes of the mitochondria-enriched fractions and associated bulk cancer cells. A total of 44 metabolites, including malate and succinate, occurred at significantly higher levels in the mitochondrial fractions, whereas 51 metabolites, including citrate, oxaloacetate, and fumarate of the Krebs cycle and the oncometabolites glutamine and glutamate, were reduced in mitochondria compared to that in the corresponding bulk cells of PANC1. Similar patterns were observed in mitochondria and bulk cells of MDA-MB-231 and A549 cell lines. A clear difference between the lipid profiles of bulk PANC1, MDA-MB-231, and A549 and corresponding mitochondrial fractions of these cell lines was detected by principal component analysis. In conclusion, we developed an experimental procedure for a large-scale metabolic assessment for suborganelle metabolic profiling and multiple omics data integration in cancer cells with broad applications.

Bayesian multiple instance regression for modeling immunogenic neoantigens.

The relationship between tumor immune responses and tumor neoantigens is one of the most fundamental and unsolved questions in tumor immunology, and is the key to understanding the inefficiency of immunotherapy observed in many cancer patients. However, the properties of neoantigens that can elicit immune responses remain unclear. This biological problem can be represented and solved under a multiple instance learning framework, which seeks to model multiple instances (neoantigens) within each bag (patient specimen) with the continuous response (T cell infiltration) observed for each bag. To this end, we develop a Bayesian multiple instance regression method, named BMIR, using a Gaussian distribution to address continuous responses and latent binary variables to model primary instances in bags. By means of such Bayesian modeling, BMIR can learn a function for predicting the bag-level responses and for identifying the primary instances within bags, as well as give access to Bayesian statistical inference, which are elusive in existing works. We demonstrate the superiority of BMIR over previously proposed optimization-based methods for multiple instance regression through simulation and real data analyses. Our method is implemented in R package entitled "BayesianMIR" and is available at https://github.com/inmybrain/BayesianMIR.

Truncated rank correlation (TRC) as a robust measure of test-retest reliability in mass spectrometry data.

In mass spectrometry (MS) experiments, more than thousands of peaks are detected in the space of mass-to-charge ratio and chromatographic retention time, each associated with an abundance measurement. However, a large proportion of the peaks consists of experimental noise and low abundance compounds are typically masked by noise peaks, compromising the quality of the data. In this paper, we propose a new measure of similarity between a pair of MS experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. A comprehensive numerical study suggests that TRC outperforms traditional sample correlation and Kendall's τ. We apply TRC to measuring test-retest reliability of two MS experiments, including biological replicate analysis of the metabolome in HEK293 cells and metabolomic profiling of benign prostate hyperplasia (BPH) patients. An R package trc of the proposed TRC and related functions is available at https://sites.google.com/site/dhyeonyu/software.

Metabolomics Approach Based on Multivariate Techniques for Blood Transfusion Reactions.

Blood transfusions temporarily improve the physical state of the patient but exert widespread effects on immune and non-immune systems. Perioperative allogeneic blood transfusions (ABT) are associated with various risks, including coagulopathy, incompatibility, transmission of infectious agents, and allergic reactions. Nevertheless, little is known about the global metabolic alterations that reflect the possible reactions of blood transfusions. In this study, we investigated metabolite changes generated by ABT in a rat model using metabolomics technology. To further profile the "metabolome" after blood transfusions, we used both liquid chromatography-quadrupole time-of-flight high-definition mass spectrometry and gas chromatography-mass spectrometry. ABT promoted a stimulatory microenvironment associated with a relative increase in glucose transporter 1/4 (GLUT1/GLUT4) expression. Supporting this result, glucose metabolism-related enzyme IRS1 and interleukin-6 (IL-6) were abnormally expressed, and levels of lysophosphatidylcholine (LysoPC) and its related enzyme phospholipase A2 (PLA2) were significantly altered in allogeneic groups compared to those in autologous groups. Finally, amino acid metabolism was also altered following ABT. Taken together, our results show a difference between autologous and allogeneic blood transfusions and demonstrate correlations with cancer-associated metabolic changes. Our data provide endogenous information for a better understanding of blood transfusion reactions.

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer.

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)OS = 2.2, p-value < 0.001), ANXA2 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer.

The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in Helicobacter pylori infection and inflammatory processes. The overexpression of TGFBI and S100A2 was associated with poor disease-free survival while the down-regulation of NR5A2, SLC4A4, and CD177 was linked to worse overall survival of the patients. In conclusion, novel transcriptome signatures to improve the diagnostic accuracy in CRC are introduced for further validations in various clinical settings.

Seronegative patients form a distinctive subgroup of immune-mediated necrotizing myopathy.

Objectives: To investigate the characteristics of different clinico-serologic subgroups of immune-mediated necrotizing myopathy (IMNM). Methods: We retrospectively reviewed data from medical charts of 64 patients diagnosed with IMNM between 2012 and 2017 in 3 neuromuscular referral centers in The Netherlands and 1 in Belgium. Results: Seventeen patients had anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) autoantibodies (Abs), of whom 11 had a history of statin use, 15 had anti-signal recognition particle (SRP) Abs, 2 had anti-melanoma differentiation-associated protein 5 (MDA5) Abs, 22 patients were seronegative, and 9 patients did not have a complete Ab assessment. Moderate to severe disability in HMGCR Ab-positive and anti-SRP Ab-positive IMNM was common (71% and 60%, respectively) despite multimodality treatment. Compared with statin-associated anti-HMGCR Ab-positive IMNM, statin-naive anti-HMGCR Ab-positive IMNM patients were more often men (67% vs 45%), had lower rates of dysphagia (17% vs 45%), and more frequently had third-line therapy (50% vs 9%) and poor to fatal outcome (50% vs 0%). Compared with seropositive IMNM, seronegative IMNM was characterized by female predominance (1:3), frequent occurrence of associated connective tissue disorders (22% vs 9%), and significantly higher rates of extramuscular disease activity (50% vs 16%, p 0.014; 2-sided Fisher exact), also after excluding patients with an associated connective tissue disease (35% vs 7%, p 0.038; 2-sided Fisher exact). Conclusions: Our findings indicate that seronegative IMNM forms a subgroup with distinctive features from seropositive IMNM.

Frequencies and clinical associations of myositis-related antibodies in The Netherlands: A one-year survey of all Dutch patients.

Idiopathic inflammatory myopathies (IIM) are a heterogeneous group of connective tissue diseases, collectively known as myositis. Diagnosis of IIM is challenging while timely recognition of an IIM is of utter importance considering treatment options and otherwise irreversible (severe) long-term clinical complications. With the EULAR/ACR classification criteria (2017) considerable advancement has been made in the diagnostic workup of IIM. While these criteria take into account clinical parameters as well as presence of one autoantibody, anti-Jo-1, several autoantibodies are associated with IIM and are currently evaluated to be incorporated into classification criteria. As individual antibodies occur at low frequency, the development of line blots allowing multiplex antibody analysis has improved laboratory diagnostics for IIM. The Euroline myositis line-blot assay (Euroimmun) allows screening and semi-quantitative measurement for 15 autoantibodies, i.e. myositis specific antibodies (MSA) to SRP, EJ, OJ, Mi-2α, Mi-2ß, TIF1-γ, MDA5, NXP2, SAE1, PL-12, PL-7, Jo-1 and myositis associated antibodies (MAA) to Ku, PM/Scl-75 and PM/Scl-100. To evaluate the clinical significance of detection and levels of these autoantibodies in the Netherlands, a retrospective analysis of all Dutch requests for extended myositis screening within a 1 year period was performed. A total of 187 IIM patients and 632 non-IIM patients were included. We conclude that frequencies of MSA and MAA observed in IIM patients in a routine diagnostic setting are comparable to cohort-based studies. Weak positive antibody levels show less diagnostic accuracy compared to positive antibody levels, except for anti-NXP2. Known associations between antibodies and skin involvement (anti-MDA5, anti-TIF1-γ), lung involvement (anti-Jo-1), and malignancy (anti-TIF1-γ) were confirmed in our IIM study population. The availability of multiplex antibody analyses will facilitate inclusion of additional autoantibodies in clinical myositis guidelines and help to accelerate diagnosing IMM with rare but specific antibodies.

In Vitro Tracking of Intracellular Metabolism-Derived Cancer Volatiles via Isotope Labeling.

Cancer detection relying on the release of volatile biomarkers has been extensively studied, but the individual biochemical processes of the cells from which biogenic volatiles originate have not been thoroughly elucidated to date. Inadequate determination of the metabolic origin of the volatile biomarkers has limited the progress of the scientific and practical applications of volatile biomarkers. To overcome the current limitations, we developed a metabolism tracking approach combining stable isotope labeling and flux analysis of volatiles to trace the intracellular metabolism-derived volatiles and to reveal their relation to cancer metabolic pathways. Specifically, after the 13C labeling of lung cancer cell, the isotopic ratio of whole cellular carbon was measured by nanoscale secondary ion mass spectrometry-based imaging. The kinetic modeling with the time-dependent isotopic ratio determined the period during which cancer cells reach the metabolic steady state, at which time all of the potential volatiles derived from intracellular metabolism were fully enriched isotopically. By measuring the isotopic enrichment of volatiles at the end-stage of isotopic flux, we found that 2-pentadecanone appeared to be derived from the metabolic cascade starting from glucose to fatty acid synthesis. Furthermore, this biosynthetic pathway was determined to be distinct in cancer, as it was upregulated in colon, breast, and pancreatic cancer cells but not in normal cells. The investigation of the metabolic footprint of 2-pentadecanone demonstrates that our novel approach could be applied to trace the metabolic origin of biogenic volatile organic compounds. This analytical strategy represents a potential cutting-edge tool in elucidating the biochemical authenticity of cancer volatiles and further expanding our understanding of the metabolic network of airborne metabolites in vitro.

Comparative study on metabolite level in tissue-specific human mesenchymal stem cells by an ultra-performance liquid chromatography quadrupole time of flight mass spectrometry.

Mesenchymal stem cells (MSCs) are a promising therapeutic option for cell-based therapy due to their immunomodulatory and regenerative properties. They can be isolated from various adult tissues, including bone marrow, fat, dental tissue, and glandular tissue. Although they share common characteristics, little is known about the biological differences between MSC populations derived from different tissues. In this study, we used MS to compare the endogenous metabolite level in the human MSCs originating from the bone marrow, adipose tissue, periodontal ligaments, and salivary glands. Using an optimized metabolomics technique, we verified that human MSCs exhibit differences in the endogenous metabolite level depending on their source material, while the multivariate analysis showed that 5 lysophosphatidylcholines and 3 lysophosphatidylethanolamines can serve as markers for the discrimination between MSC sources and may be related to differences in their differentiation capacity. These results may significantly contribute to further mechanistic studies on the MSCs and provide novel insights into the properties and optimal usage of MSCs from different tissues.

Enhanced construction of gene regulatory networks using hub gene information.

BACKGROUND: Gene regulatory networks reveal how genes work together to carry out their biological functions. Reconstructions of gene networks from gene expression data greatly facilitate our understanding of underlying biological mechanisms and provide new opportunities for biomarker and drug discoveries. In gene networks, a gene that has many interactions with other genes is called a hub gene, which usually plays an essential role in gene regulation and biological processes. In this study, we developed a method for reconstructing gene networks using a partial correlation-based approach that incorporates prior information about hub genes. Through simulation studies and two real-data examples, we compare the performance in estimating the network structures between the existing methods and the proposed method. RESULTS: In simulation studies, we show that the proposed strategy reduces errors in estimating network structures compared to the existing methods. When applied to Escherichia coli, the regulation network constructed by our proposed ESPACE method is more consistent with current biological knowledge than the SPACE method. Furthermore, application of the proposed method in lung cancer has identified hub genes whose mRNA expression predicts cancer progress and patient response to treatment. CONCLUSIONS: We have demonstrated that incorporating hub gene information in estimating network structures can improve the performance of the existing methods.

Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis.

Generally, cancer stem cells have epithelial-to-mesenchymal-transition characteristics and other aggressive properties that cause metastasis. However, there have been no confident markers for the identification of cancer stem cells and comparative methods examining adherent and sphere cells are widely used to investigate mechanism underlying cancer stem cells, because sphere cells have been known to maintain cancer stem cell characteristics. In this study, we conducted a meta-analysis that combined gene expression profiles from several studies that utilized tumorsphere technology to investigate tumor stem-like breast cancer cells. We used our own gene expression profiles along with the three different gene expression profiles from the Gene Expression Omnibus, which we combined using the ComBat method, and obtained significant gene sets using the gene set analysis of our datasets and the combined dataset. This experiment focused on four gene sets such as cytokine-cytokine receptor interaction that demonstrated significance in both datasets. Our observations demonstrated that among the genes of four significant gene sets, six genes were consistently up-regulated and satisfied the p-value of < 0.05, and our network analysis showed high connectivity in five genes. From these results, we established CXCR4, CXCL1 and HMGCS1, the intersecting genes of the datasets with high connectivity and p-value of < 0.05, as significant genes in the identification of cancer stem cells. Additional experiment using quantitative reverse transcription-polymerase chain reaction showed significant up-regulation in MCF-7 derived sphere cells and confirmed the importance of these three genes. Taken together, using meta-analysis that combines gene set and network analysis, we suggested CXCR4, CXCL1 and HMGCS1 as candidates involved in tumor stem-like breast cancer cells. Distinct from other meta-analysis, by using gene set analysis, we selected possible markers which can explain the biological mechanisms and suggested network analysis as an additional criterion for selecting candidates.

ROC Estimation from Clustered Data with an Application to Liver Cancer Data.

In this article, we propose a regression model to compare the performances of different diagnostic methods having clustered ordinal test outcomes. The proposed model treats ordinal test outcomes (an ordinal categorical variable) as grouped-survival time data and uses random effects to explain correlation among outcomes from the same cluster. To compare different diagnostic methods, we introduce a set of covariates indicating diagnostic methods and compare their coefficients. We find that the proposed model defines a Lehmann family and can also introduce a location-scale family of a receiver operating characteristic (ROC) curve. The proposed model can easily be estimated using standard statistical software such as SAS and SPSS. We illustrate its practical usefulness by applying it to testing different magnetic resonance imaging (MRI) methods to detect abnormal lesions in a liver.

Statistical completion of a partially identified graph with applications for the estimation of gene regulatory networks.

We study the estimation of a Gaussian graphical model whose dependent structures are partially identified. In a Gaussian graphical model, an off-diagonal zero entry in the concentration matrix (the inverse covariance matrix) implies the conditional independence of two corresponding variables, given all other variables. A number of methods have been proposed to estimate a sparse large-scale Gaussian graphical model or, equivalently, a sparse large-scale concentration matrix. In practice, the graph structure to be estimated is often partially identified by other sources or a pre-screening. In this paper, we propose a simple modification of existing methods to take into account this information in the estimation. We show that the partially identified dependent structure reduces the error in estimating the dependent structure. We apply the proposed method to estimating the gene regulatory network from lung cancer data, where protein-protein interactions are partially identified from the human protein reference database. The application shows that proposed method identified many important cancer genes as hub genes in the constructed lung cancer network. In addition, we validated the prognostic importance of a newly identified cancer gene, PTPN13, in four independent lung cancer datasets. The results indicate that the proposed method could facilitate studying underlying lung cancer mechanisms and identifying reliable biomarkers for lung cancer prognosis.

Functional interpretation of metabolomics data as a new method for predicting long-term side effects: treatment of atopic dermatitis in infants.

Topical steroids are used for the treatment of primary atopic dermatitis (AD); however, their associated risk of serious complications is great due to the presence of vulnerable lesions in young children with AD. Topical calcineurin inhibitors (TCIs) are steroid-free, anti-inflammatory agents used for topical AD therapy. However, their use is prohibited in infants <2 years of age because of their carcinogenic potential. We conducted a randomized, double-blind trial to evaluate the efficacy of TCIs as a secondary AD treatment for children <2 years of age by comparing 1% pimecrolimus cream with 0.05% desonide cream. We performed urinary metabolomics to predict long-term side effects. The 1% pimecrolimus cream displayed similar efficacy and exceptional safety compared with the 0.05% desonide cream. Metabolomics-based long-term toxicity tests effectively predicted long-term side effects using short-term clinical models. This applicable method for the functional interpretation of metabolomics data sets the foundation for future studies involving the prediction of the toxicity and systemic reactions caused by long-term medication administration.