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Gastric cancer is a disease that develops from the lining of the stomach, whereas Parkinson's disease is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. Although these two diseases seem to be distinct from each other, increasing evidence suggests that they might be linked. To explore the linkage between these two diseases, differentially expressed genes between the diseased people and their normal controls were identified using the barcode algorithm. This algorithm transforms actual gene expression values into barcode values comprised of 1's (expressed genes) and 0's (silenced genes). Once the overlapped differentially expressed genes were identified, their biological relevance was investigated. Thus, using the gene expression profiles and bioinformatics methods, we demonstrate that Parkinson's disease and gastric cancer are indeed linked. This research may serve as a pilot study, and it will stimulate more research to investigate the relationship between gastric cancer and Parkinson's disease from the perspective of gene profiles and their functions.

Objective To identify the predictive factors associated with pleural drainage volume (PDV) after uniportal video-assisted thoracic surgery (VATS) lobectomy for non-small cell lung cancer (NSCLC). Methods A total of 440 consecutive NSCLC patients who underwent uniportal VATS lobectomy were enrolled in this study between November 2016 and July 2019. Thirty-four parameters, including patients’ clinicopathological characteristics and other potential predictors were collected. Daily drainage volume was summed up as PDV. Univariate analysis and multivariate regression models were fitted to identify independent predictive factors for PDV. Results The median PDV was 840 ml during the median drainage duration of 4 days. A strong correlation was observed between PDV and drainage duration (correlation coefficient = 0.936). On univariate analysis, age, forced expiratory volume in 1 s % predicted (FEV1%), left ventricular ejection fraction (LVEF), operation time, serum total protein (TP), and body mass index (BMI) showed a significant correlation with PDV (P value, < 0.001, < 0.001, 0.003, 0.008, 0.028, and 0.045, respectively). Patients with smoking history (P = 0.030) or who underwent lower lobectomy (P = 0.015) showed significantly increased PDV than never smokers or those who underwent upper or middle lobectomy, respectively. On multivariate regression analysis, older age (P< 0.001), lower FEV1% (P< 0.001), lower LVEF (P = 0.011), lower TP (P = 0.013), and lower lobectomy (P = 0.016) were independent predictors of increased PDV. Conclusions Predictive factors of PDV can be identified. Based on these predictors, patients can be treated with tailored individualized safe chest tube management.

Background The most common histological subtypes of esophageal cancer are squamous cell carcinoma (ESCC) and adenocarcinoma (EAC). It has been demonstrated that non-marginal differences in gene expression and somatic alternation exist between these two subtypes; consequently, biomarkers that have prognostic values for them are expected to be distinct. In contrast, laryngeal squamous cell cancer (LSCC) has a better prognosis than hypopharyngeal squamous cell carcinoma (HSCC). Likewise, subtype-specific prognostic signatures may exist for LSCC and HSCC. Long non-coding RNAs (lncRNAs) hold promise for identifying prognostic signatures for a variety of cancers including esophageal cancer and head and neck squamous cell carcinoma (HNSCC). Methods In this study, we applied a novel feature selection method capable of identifying specific prognostic signatures uniquely for each subtype – the Cox-filter method – to The Cancer Genome Atlas esophageal cancer and HSNCC RNA-Seq data, with the objectives of constructing subtype-specific prognostic lncRNA expression signatures for esophageal cancer and HNSCC. Results By incorporating biological relevancy information, the lncRNA lists identified by the Cox-filter method were further refined. The resulting signatures include genes that are highly related to cancer, such as H19 and NEAT1, which possess perfect prognostic values for esophageal cancer and HNSCC, respectively. Conclusions The Cox-filter method is indeed a handy tool to identify subtype-specific prognostic lncRNA signatures. We anticipate the method will gain wider applications.

Background: Exosomes are involved in tumorigenesis, growth and metastasis. However, the prognostic value of exosome-related genes in lung adenocarcinoma (LUAD) remains unclear. Methods: Clinical and transcriptome data from The Cancer Genome Atlas LUAD cohort were used to construct a model based on exosome-related genes, which was validated with LUAD data from the Gene Expression Omnibus (GEO). Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were used to explore underlying mechanisms; the single-sample gene set enrichment analysis score was used to determine immune functions. Results: A 19-exosome-related gene signature for overall survival in LUAD was predictive in both The Cancer Genome Atlas and GEO LUAD cohorts. Immune-related and extracellular matrix-related pathways were enriched in differentially expressed genes. Immune states differed between high- and low-risk groups. Conclusion: The novel signature can be used to predict outcomes in LUAD.

Background Psoriasis is an immune-mediated, inflammatory disorder of the skin with chronic inflammation and hyper-proliferation of the epidermis. Since psoriasis has genetic components and the diseased tissue of psoriasis is very easily accessible, it is natural to use high-throughput technologies to characterize psoriasis and thus seek targeted therapies. Transcriptional profiles change correspondingly after an intervention. Unlike cross-sectional gene expression data, longitudinal gene expression data can capture the dynamic changes and thus facilitate causal inference. Methods Using the iCluster method as a building block, an ensemble method was proposed and applied to a longitudinal gene expression dataset for psoriasis, with the objective of identifying key lncRNAs that can discriminate the responders from the non-responders to two immune treatments of psoriasis. Results Using support vector machine models, the leave-one-out predictive accuracy of the 20-lncRNA signature identified by this ensemble was estimated as 80%, which outperforms several competing methods. Furthermore, pathway enrichment analysis was performed on the target mRNAs of the identified lncRNAs. Of the enriched GO terms or KEGG pathways, proteasome, and protein deubiquitination is included. The ubiquitination-proteasome system is regarded as a key player in psoriasis, and a proteasome inhibitor to target ubiquitination pathway holds promises for treating psoriasis. Conclusions An integrative method such as iCluster for multiple data integration can be adopted directly to analyze longitudinal gene expression data, which offers more promising options for longitudinal big data analysis. A comprehensive evaluation and validation of the resulting 20-lncRNA signature is highly desirable.

Background Colon cancer (CC) is one of the most common malignant tumors, while Parkinson’s disease (PD) is the second most common neurodegenerative disorder. Recent accumulating evidence indicates that these two diseases are associated with each other. Also, from the perspective of long non-coding RNAs, some well-known genes such as H19 and PVT1 can link these two diseases together. Several studies have shown that patients with PD had a decreased risk of developing CC compared with patients without PD. However, controversies surround the relationship between PD and CC, and to date, no concordant conclusion has been drawn. Methods In this study, we aimed to assess the association between these two diseases based on lncRNA-to-lncRNA interactions. Motivated by the weighted gene co-expression network analysis method, a customized procedure was proposed and used to identify differentially correlated edges (DCEs) in the respective interaction networks for PD and CC and explore how these two diseases are linked. Results Of the two sets of DCEs for PD and CC, 16 pairs overlapped. Among them, 15 edges had opposite signs, with positive signs for CC indicating a gain of connectivity, whereas negative signs for PD indicating a loss of connectivity. Conclusions By using the lncRNA expression profiles, and a customized procedure, an answer to the question about how PD and CC are associated is provided.

Aims: To elucidate the association between ferroptosis-related genes and prognosis in patients with lung adenocarcinoma (LUAD). Materials & methods: A ferroptosis-related gene signature was made by lasso regression analysis through the LUAD datasets of the Cancer Genome Atlas. The prognostic value of the multigene signature was externally validated in the GSE72094 dataset from the Gene Expression Omnibus database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were used to explore underlying mechanisms. Results & conclusion: We established a novel ferroptosis-related gene signature for overall survival in LUAD that was predictive in both the training and validation cohorts. Immune-related pathways were significantly enriched, and immune status differed between the high- and low-risk groups. Targeting ferroptosis is a potential therapeutic option in LUAD. These results still need to be confirmed by more studies.

Background At present, the beneficial effect of the ketogenic diet (KD) on weight loss in obese patients is generally recognized. However, a systematic research on the role of KD in the improvement of glycemic and lipid metabolism of patients with diabetes is still found scarce. Methods This meta-study employed the meta-analysis model of random effects or of fixed effects to analyze the average difference before and after KD and the corresponding 95% CI, thereby evaluating the effect of KD on T2DM. Results After KD intervention, in terms of glycemic control, the level of fasting blood glucose decreased by 1.29 mmol/L (95% CI: −1.78 to −0.79) on average, and glycated hemoglobin A1c by 1.07 (95% CI: −1.37 to −0.78). As for lipid metabolism, triglyceride was decreased by 0.72 (95% CI: −1.01 to −0.43) on average, total cholesterol by 0.33 (95% CI: −0.66 to −0.01), and low-density lipoprotein by 0.05 (95% CI: −0.25 to −0.15); yet, high-density lipoprotein increased by 0.14 (95% CI: 0.03−0.25). In addition, patients’ weight decreased by 8.66 (95% CI: −11.40 to −5.92), waist circumference by 9.17 (95% CI: −10.67 to −7.66), and BMI by 3.13 (95% CI: −3.31 to −2.95). Conclusion KD not only has a therapeutic effect on glycemic and lipid control among patients with T2DM but also significantly contributes to their weight loss.

With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method—threshold gradient descent regularization (TGDR)—stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. We proposed a new novel feature selection algorithm for longitudinal outcomes—GEE-TGDR. In the GEE-TGDR method, the corresponding quasilikelihood function of a GEE model is the objective function to be optimized, and the optimization and feature selection are accomplished by the TGDR method. Long noncoding RNAs (lncRNAs) are posttranscriptional and epigenetic regulators and have lower expression levels and are more tissue-specific compared with protein-coding genes. So far, the implication of lncRNAs in psoriasis remains largely unexplored and poorly understood even though some evidence in the literature supports that lncRNAs and psoriasis are highly associated. In this study, we applied the GEE-TGDR method to a lncRNA expression dataset that examined the response of psoriasis patients to immune treatments. As a result, a list including 10 relevant lncRNAs was identified with a predictive accuracy of 70% that is superior to the accuracies achieved by two competitive methods and meaningful biological interpretation. A widespread application of the GEE-TGDR method in omics longitudinal data analysis is anticipated.

Studies have demonstrated that both squamous cell carcinomas (SCCs) and adenocarcinomas (ACs) possess some common molecular characteristics. Evidence has accumulated to support the theory that long non-coding RNAs (lncRNAs) serve as novel biomarkers and therapeutic targets in complex diseases such as cancer. In this study, we aimed to identify pan lncRNA signatures that are common to squamous cell carcinomas or adenocarcinomas with different tissues of origin. With the aid of elastic-net regularized regression models, a 35-lncRNA pan discriminative signature and an 11-lncRNA pan prognostic signature were identified for squamous cell carcinomas, whereas a 6-lncRNA pan discriminative signature and a 5-lncRNA pan prognostic signature were identified for adenocarcinomas. Among them, many well-known cancer relevant genes such as MALAT1 and PVT1 were included. The identified pan lncRNA lists can help experimental biologists generate research hypotheses and adopt existing treatments for less prevalent cancers. Therefore, these signatures warrant further investigation.

Objective To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications. Methods Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes. Results The five-gene diagnostic signature including keratin 5 ( KRT5), mucin 1 ( MUC1), triggering receptor expressed on myeloid cells 1 ( TREM1), complement C3 ( C3) and transmembrane serine protease 2 ( TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide ( ADH1C), alpha-2-glycoprotein 1, zinc-binding ( AZGP1), clusterin ( CLU), cyclin dependent kinase 1 ( CDK1) and paternally expressed 10 ( PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set. Conclusions Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.

Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.

There is an "assessment gap" between the moment a patient's response to treatment is biologically determined and when a response can actually be determined clinically. Patients' biochemical profiles are a major determinant of clinical outcome for a given treatment. It is therefore feasible that molecular-level patient information could be used to decrease the assessment gap. Thanks to clinically accessible biopsy samples, high-quality molecular data for psoriasis patients are widely available. Psoriasis is therefore an excellent disease for testing the prospect of predicting treatment outcome from molecular data. Our study shows that gene-expression profiles of psoriasis skin lesions, taken in the first 4 weeks of treatment, can be used to accurately predict (>80% area under the receiver operating characteristic curve) the clinical endpoint at 12 weeks. This could decrease the psoriasis assessment gap by 2 months. We present two distinct prediction modes: a universal predictor, aimed at forecasting the efficacy of untested drugs, and specific predictors aimed at forecasting clinical response to treatment with four specific drugs: etanercept, ustekinumab, adalimumab, and methotrexate. We also develop two forms of prediction: one from detailed, platform-specific data and one from platform-independent, pathway-based data. We show that key biomarkers are associated with responses to drugs and doses and thus provide insight into the biology of pathogenesis reversion.

Background Monotonically expressed genes (MEGs) are genes whose expression values increase or decrease monotonically as a disease advances or time proceeds. Non-small cell lung cancer (NSCLC) is a multistage progression process resulting from genetic sequences mutations, the identification of MEGs for NSCLC is important. Results With the aid of a feature selection algorithm capable of identifying MEGs – the MFSelector method – two sets of potential MEGs were selected in this study: the MEGs across the different pathologic stages and the MEGs across the risk levels of death for the NSCLC patients at early stages. For the lung adenocarcinoma (AC) subtypes no statistically significant MEGs were identified across pathologic stages, however dozens of MEGs were identified across the risk levels of death. By contrast, for the squamous cell lung carcinoma (SCC) there were no statistically significant MEGs as either stage or risk level advanced. Conclusions The pathologic stage of non-small cell lung cancer patients at early stages has no prognostic value, making the identification of prognostic gene signatures for them more meaningful and highly desirable.

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable.

Background Feature selection and gene set analysis are of increasing interest in the field of bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be utilized to conduct feature selection. Methods We adopted a gene set analysis method, the significance analysis of microarray gene set reduction (SAMGSR) algorithm, to carry out feature selection for longitudinal gene expression data. Results Using a real-world application and simulated data, it is demonstrated that the proposed SAMGSR extension outperforms other relevant methods. In this study, we illustrate that a gene’s expression profiles over time can be regarded as a gene set and then a suitable gene set analysis method can be utilized directly to select relevant genes associated with the phenotype of interest over time. Conclusions We believe this work will motivate more research to bridge feature selection and gene set analysis, with the development of novel algorithms capable of carrying out feature selection for longitudinal gene expression data. Electronic supplementary material The online version of this article (10.1186/s12911-018-0685-8) contains supplementary material, which is available to authorized users.

Aims Neuromyelitis optica spectrum disorders (NMOSD) is an inflammatory demyelinating autoimmune disorder in the central nervous system (CNS), which is mainly mediated by aquaporin 4 antibodies (AQP4-Ab). Interleukin (IL)-39 is a new pro-inflammatory cytokine which belongs to the IL-12 cytokine family. However, the role of IL-39 in patients with NMOSD is not completely understood. Therefore, the aim of this study is to explore the possible implication of IL-39 in the pathogenesis of NMOSD. Methods In this study, 50 patients with NMOSD, 20 patients with relapsing-remitting multiple sclerosis (RRMS), 30 patients with non-inflammatory neurological disorders (NND) and 78 healthy controls (HCs) were recruited. The levels of serum IL-39 were measured using the enzyme-linked immunosorbent assay (ELISA). Results Our study showed serum IL-39 levels in patients with NMOSD were significantly higher than that in RRMS patients, NND patients and HCs, and positively correlated with Expanded Disability Status Scale (EDSS) score. Conclusion These findings suggested that IL-39 may play a pro-inflammatory role in the pathogenesis of NMOSD and correlate with disease severity.

The origin of large genomes that underlies the long standing "C-value enigma" is only partially explained by selfish DNA. We investigated insertions and deletions (indels) of nucleotides and discussed their relevance in size evolution of random biological sequences (RBS) and genomes. By developing a probabilistic model of RBS based on size evolution of expandable sites in a thought perfect genome, it was found that insertion bias engenders exponential increase of average RBS sizes. When combined with existing large segments of genome that are not subject to selection pressure (e.g. selfish DNA), such insertion bias results in explosive expansion of genomes, and therefore helps explain the "C value enigma" besides selfish DNA. Such increase of RBS size is caused by the fundamental asymmetry of indels, with insertions result in more available sites and deletions result in less deletable nucleotides. In qualitative agreement with the size distribution of known genomes, tails of RBS size distributions exhibit exponential decay with probabilities of larger RBS segments being smaller. Unsurprisingly, a slight deletion bias (higher deletions probabilities) results in a slow decrease of average RBS size and may lead to their eventual vanishing. Contrary to intuition, strictly balanced insertion and deletion results in linearly increasing instead of completely fixed RBS size. Nonetheless, such slow linear increase of average RBS sizes with time are small in magnitude and are consequently not influential on genome size evolution, and certainly not a major contributor for the "C-value enigma". Our model suggested that insertion bias of nucleotides may provide complementary explanation for large genomes besides selfish DNA. The fundamental indel asymmetry is applicable for all forms of genomic insertions and deletions. Long-lasting exponential increase of genome size present energy and material requirement that is impossible to sustain. We therefore concluded that if there were explosively accelerating expansion caused by significant effective insertion bias for any survival species, it must have occurred sporadically. Our model also provided an explanation for the observed proportional evolution of genome size.

Among non-small cell lung cancer (NSCLC), adenocarcinoma (AC), and squamous cell carcinoma (SCC) are two major histology subtypes, accounting for roughly 40% and 30% of all lung cancer cases, respectively. Since AC and SCC differ in their cell of origin, location within the lung, and growth pattern, they are considered as distinct diseases. Gene expression signatures have been demonstrated to be an effective tool for distinguishing AC and SCC. Gene set analysis is regarded as irrelevant to the identification of gene expression signatures. Nevertheless, we found that one specific gene set analysis method, significance analysis of microarray-gene set reduction (SAMGSR), can be adopted directly to select relevant features and to construct gene expression signatures. In this study, we applied SAMGSR to a NSCLC gene expression dataset. When compared with several novel feature selection algorithms, for example, LASSO, SAMGSR has equivalent or better performance in terms of predictive ability and model parsimony. Therefore, SAMGSR is a feature selection algorithm, indeed. Additionally, we applied SAMGSR to AC and SCC subtypes separately to discriminate their respective stages, that is, stage II versus stage I. Few overlaps between these two resulting gene signatures illustrate that AC and SCC are technically distinct diseases. Therefore, stratified analyses on subtypes are recommended when diagnostic or prognostic signatures of these two NSCLC subtypes are constructed.

In contrast to feature selection and gene set analysis, bi-level selection is a process of selecting not only important gene sets but also important genes within those gene sets. Depending on the order of selections, a bi-level selection method can be classified into three categories – forward selection, which first selects relevant gene sets followed by the selection of relevant individual genes; backward selection which takes the reversed order; and simultaneous selection, which performs the two tasks simultaneously usually with the aids of a penalized regression model. To test the existence of subtype-specific prognostic genes for non-small cell lung cancer (NSCLC), we had previously proposed the Cox-filter method that examines the association between patients’ survival time after diagnosis with one specific gene, the disease subtypes, and their interaction terms. In this study, we further extend it to carry out forward and backward bi-level selection. Using simulations and a NSCLC application, we demonstrate that the forward selection outperforms the backward selection and other relevant algorithms in our setting. Both proposed methods are readily understandable and interpretable. Therefore, they represent useful tools for the researchers who are interested in exploring the prognostic value of gene expression data for specific subtypes or stages of a disease.

Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible.

Summary Background Psoriasis vulgaris is an inflammatory immune-mediated disease, with lesional skin characterized by sharply demarcated, erythematous scaly plaques. Uninvolved psoriatic skin appears clinically similar to normal skin. However, it has been hypothesized that inflammatory cytokines, e.g. interleukin (IL)-17, may affect any organ or tissue having a vascular supply; thus, distant uninvolved skin could be exposed to increased circulating IL-17. Objectives To establish comparative genomic profiles between noninvolved skin and normal skin, in particular, determining immune abnormalities in distant uninvolved skin. Methods We performed a meta-analysis on three gene array studies, comparing the nonlesional (NL) psoriatic skin transcriptome with normal gene expression. We investigated immunological features of noninvolved skin, particularly linked to IL-17 signalling. Results We detected 252 differentially expressed gene transcripts in uninvolved skin compared with normal skin; multiple immune-related genes, including IL-17-downstream genes, were upregulated. Increased expression of IL-17-signature genes (e.g. DEFB4 and S100A7) was associated with an increased number of CD3+, CD8+ and DC-LAMP+ cells in NL skin vs. normal controls. Inducible T-cell costimulator (ICOS) expression was detected only in a few T-cells within NL skin. Conclusions Our data described the genomic profile in NL skin, characterizing the immune activation that was mainly attributed to IL-17 signalling.

The focus of analyzing data from microarray experiments has shifted from the identification of associated individual genes to that of associated biological pathways or gene sets. In bioinformatics, a feature selection algorithm is usually used to cope with the high dimensionality of microarray data. In addition to those algorithms that use the biological information contained within a gene set as a priori to facilitate the process of feature selection, various gene set analysis methods can be applied directly or modified readily for the purpose of feature selection. Significance analysis of microarray to gene-set reduction analysis (SAM-GSR) algorithm, a novel direction of gene set analysis, is one of such methods. Here, we explore the feature selection property of SAM-GSR and provide a modification to better achieve the goal of feature selection. In a multiple sclerosis (MS) microarray data application, both SAM-GSR and our modification of SAM-GSR perform well. Our results show that SAM-GSR can carry out feature selection indeed, and modified SAM-GSR outperforms SAM-GSR. Given pathway information is far from completeness, a statistical method capable of constructing biologically meaningful gene networks is of interest. Consequently, both SAM-GSR algorithms will be continuously revaluated in our future work, and thus better characterized.

Background Atopic dermatitis (AD) is a common inflammatory skin disease with limited treatment options. Several microarray experiments have been conducted on lesional/LS and non-lesional/NL AD skin to develop a genomic disease phenotype. Although these experiments have shed light on disease pathology, inter-study comparisons reveal large differences in resulting sets of differentially expressed genes (DEGs), limiting the utility of direct comparisons across studies. Methods We carried out a meta-analysis combining 4 published AD datasets to define a robust disease profile, termed meta-analysis derived AD (MADAD) transcriptome. Results This transcriptome enriches key AD pathways more than the individual studies, and associates AD with novel pathways, such as atherosclerosis signaling (IL-37, selectin E/SELE). We identified wide lipid abnormalities and, for the first time in vivo, correlated Th2 immune activation with downregulation of key epidermal lipids (FA2H, FAR2, ELOVL3), emphasizing the role of cytokines on the barrier disruption in AD. Key AD “classifier genes” discriminate lesional from nonlesional skin, and may evaluate therapeutic responses. Conclusions Our meta-analysis provides novel and powerful insights into AD disease pathology, and reinforces the concept of AD as a systemic disease. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0133-x) contains supplementary material, which is available to authorized users.

Psoriasis is a complex inflammatory disease mediated by tumor necrosis factor (TNF)-α and cytokines secreted by specialized T-cell populations, e.g., IL-17, IL-22, and IFN-γ. The mechanisms by which innate and adaptive immune cytokines regulate inflammation in psoriasis are not completely understood. We sought to investigate the effects of TNF-α and IL-17 on keratinocyte (KC) gene profile, to identify genes that might be coregulated by these cytokines and determine how synergistically activated genes relate to the psoriasis transcriptome. Primary KCs were stimulated with IL-17 or TNF-α alone, or in combination. KC responses were assessed by gene array analysis, followed by reverse transcriptase-PCR confirmation for significant genes. We identified 160 genes that were synergistically upregulated by IL-17 and TNF-α, and 196 genes in which the two cytokines had at least an additive effect. Synergistically upregulated genes included some of the highest expressed genes in psoriatic skin with an impressive correlation between IL-17/TNF-α-induced genes and the psoriasis gene signature. KCs may be key drivers of pathogenic inflammation in psoriasis through integrating responses to TNF-α and IL-17. Our data predict that psoriasis therapy with either TNF or IL-17 antagonists will produce greater modulation of the synergistic/additive gene set, which consists of the most highly expressed genes in psoriasis skin lesions.

Aims/Introduction The aims of the present study were to evaluate the diagnostic value of fasting plasma glucose, 2-h postload plasma glucose and glycosylated hemoglobin (HbA1c) measurements in the screening of diabetes and prediabetes, and to determine the cut-off point of HbA1c in the diagnosis of diabetes and prediabetes in a Chinese population. Materials and Methods A total of 7,611 individuals aged 40 years or older, who did not have a prior history of diabetes, were randomly selected in the Changchun area. For each participant, a questionnaire was completed, and a physical examination and an oral glucose tolerance test were carried out. The values of fasting plasma glucose, 2-h postload plasma glucose and HbA1c were compared by area under the receiver operating characteristic curves. The Youden index was used to identify the optimal cut-off point of HbA1c in the diagnosis of diabetes and prediabetes. Results The prevalence of newly diagnosed diabetes and prediabetes was 12.71% and 29.39%, respectively. In participants with newly diagnosed diabetes, the area under the receiver operating characteristic curve was 0.8368 for fasting plasma glucose, 0.9330 for 2-h postload plasma glucose and 0.8064 for HbA1c; whereas for prediabetes, these values were 0.8022, 0.9288 and 0.6895, respectively. In addition, an HbA1c threshold of 6.3% showed the highest Youden index (0.4799) for detecting diabetes; furthermore, an HbA1c threshold of 5.8% showed the highest Youden index (0.2866) for detecting prediabetes. Conclusions HbA1c ≥6.3% (45 mmol/mol) and between 5.8% and 6.2% (40–44 mmol/mol) were the optimal cut-off values for the diagnosis of diabetes and prediabetes, respectively, in a Chinese population.

The existence of fundamental differences between lung adenocarcinoma (AC) and squamous cell carcinoma (SCC) in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR) algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.

Background Non-small cell lung cancer (NSCLC) is the predominant histological type of lung cancer, accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of NSCLC patients, however, it is an imprecise predictor of the prognosis of an individual patient. Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two major histology subtypes of NSCLC. It has been demonstrated that fundamental differences exist in their underlying mechanisms, which motivated us to postulate the existence of specific genes related to the prognosis of each histology subtype. Results In this article, we propose a simple filter feature selection algorithm with a Cox regression model as the base. Applying this method to real-world microarray data identifies a histology-specific prognostic gene signature. Furthermore, the resulting 32-gene (32/12 for AC/SCC) prognostic signature for early-stage AC and SCC samples has superior predictive ability relative to two relevant prognostic signatures, and has comparable performance with signatures obtained by applying two state-of-the art algorithms separately to AC and SCC samples. Conclusions Our proposal is conceptually simple, and straightforward to implement. Furthermore, it can be easily adapted and applied to a range of other research settings. Reviewers This article was reviewed by Leonid Hanin (nominated by Dr. Lev Klebanov), Limsoon Wong and Jun Yu. Electronic supplementary material The online version of this article (doi:10.1186/s13062-015-0051-z) contains supplementary material, which is available to authorized users.

The aim of the present study was to compare correlation coefficients between anthropometric indices and blood glucose level, and to determine optimal cutoff points of obesity indices in a Chinese population.2419 males and 5140 females participated in this study in Changchun city. Weight, height, WC (waist circumference) and hip circumference were measured; and BMI (body mass index), WHtR (waist height ratio), WHR (waist hip ratio) and BAI (body adiposity index) were calculated by formulas. FPG (fasting plasma glucose), PPG (post-load plasma glucose), HbA1c (glycosylated hemoglobin) and FSI (fasting serum insulin) were measured.WHtR had the highest partial correlation coefficients with blood glucose and HbA1c in both males and females; however, BAI had the lowest coefficients. AUC (area under receiver operating characteristic curves) for WHtR in screening type 2 diabetes mellitus were higher than other indices. We confirmed that the optimal cutoff points of obesity for WHtR, WHR, WC, BMI and BAI were 0.55, 0.92, 88.1, 25.9 and 28.1 in males, and 0.52, 0.85, 81.6, 24.6 and 31.0 in females, respectively.Central obesity correlates more closely to glucose metabolism in Chinese population than general obesity. WHtR is the best index to predict type 2 diabetes mellitus in Chinese adults.

Bioinformatics, 29 (22), ISSN:1367-4803, ISSN:1460-2059

BACKGROUND Tumor treatment is the mainstay of therapy for paraneoplastic neurologic disorders (PNDs), but it is only effective in some cases and other treatment options are limited. OBJECTIVE To evaluate the short-term use of a combination of prednisone and tacrolimus for acute neurologic worsening in PND in which intracellular antigens are targeted. DESIGN Retrospective single-center case series of patients with PND treated with tacrolimus. SETTING The Rockefeller University Hospital, a research hospital in New York, New York. PATIENTS Twenty-six patients with PND with high titer (≥1:1000) anti-HuD, anti-Yo, or anti-CRMP5 autoantibodies were enrolled. Patients were referred from Memorial Sloan Kettering Cancer Center or self-referred. Two patients discontinued intervention owing to adverse events. INTERVENTIONS Patients were treated with tacrolimus, 0.15-0.30 mg/kg per day, in 2 divided oral doses with 60 mg per day of oral prednisone, tapered off during 1 to 4 weeks. MAIN OUTCOME MEASURES The primary outcome measure was median survival. Neurologic examinations before and after treatment as well as adverse events are described. RESULTS Median survival time was 52 months from time of diagnosis. Some patients experienced neurologic improvement that was functionally meaningful. The incidence of adverse events was similar to that generally reported with tacrolimus. CONCLUSIONS A short course of prednisone and tacrolimus to target central nervous system T cells in patients with PND with acute neurologic decline in which intracellular antigens are targeted was well tolerated and warrants further study. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00378326.

FK506 (Tacrolimus) is a potent inhibitor of calcineurin that blocks IL2 production and is widely used to prevent transplant rejection and treat autoimmunity. FK506 treatment of dendritic cells (FKDC) limits their capacity to stimulate T cell responses. FK506 does not prevent DC survival, maturation, or costimulatory molecule expression, suggesting that the limited capacity of FKDC to stimulate T cells may be due to inhibition of calcineurin signaling in the DC. Instead, we demonstrate that DC inhibit T cells by sequestering FK506 and continuously releasing the drug over several days. T cells encountering FKDC proliferate but fail to upregulate the survival factor bcl-xl and die, and IL2 restores both bcl-xl and survival. In mice, FKDC act in an antigen-specific manner to inhibit T-cell mediated autoimmune arthritis. This establishes that DCs can act as a cellular drug delivery system to target antigen specific T cells. DOI: http://dx.doi.org/10.7554/eLife.00105.001, eLife digest Although our health depends on our immune system's ability to recognize and attack foreign material, this same response can cause the body to reject an organ transplant or even to spontaneously attack itself (this is called autoimmune disease). To help prevent rejection, patients who receive donated organs are given immunosuppressant drugs, with a compound called FK506, or Tacrolimus, the most commonly used. However, FK506 can have a number of serious side effects, including high blood pressure, kidney damage and diabetes. The job of starting an immune response falls in large part to a type of white blood cell called the dendritic cell, which patrols the body in search of cells in trouble—such as those infected with viruses. Dendritic cells are efficient at engulfing dying cells, which they break down and display fragments of on their cell surface. These fragments—which are known as antigens—are presented directly to T cells, which trigger a cascade of additional immune responses leading ultimately to the destruction of infected cells. In some cases of autoimmune disease, however, T cells begin to mistake the body's own cells for infected cells and to launch attacks against healthy tissue. Evidence suggests that immunosuppressive drugs such as FK506 can help to tone down these inappropriate immune responses. However, the use of FK506 to treat autoimmune disease has been limited by its side effects. Now, Orange et al. have shown that dendritic cells can be exploited to deliver drugs such as FK506 in a targeted and controlled manner. When the researchers loaded dendritic cells with FK506, they found that the cells sequestered the drug and then released it slowly in quantities that were sufficient to inhibit T-cell responses for at least 72 hr. Using a mouse model of rheumatoid arthritis—an autoimmune disease characterized by inflammation and destruction of joint tissue—Orange and co-workers demonstrated that their novel drug delivery system could be therapeutically useful. They loaded dendritic cells displaying the antigen that triggers the mouse immune system to attack joint tissue, with FK506, and used the resulting cells to treat arthritic mice. Mice that received these cells showed less severe arthritis than control animals treated with dendritic cells that had not been loaded with FK506. Moreover, the total dose of FK506 that the mice were exposed to was very low, with the result that they showed no evidence of the side effects typically seen with this drug. This proof-of-concept study suggests that dendritic cells could be used for the gradual and controlled delivery of drugs to specific target cells within the immune system. By precisely targeting relevant immune cells, it should be possible to use much lower drug doses, and thereby reduce side effects. Follow-up studies are now required to determine whether dendritic cells can be used as vehicles for the delivery of other drugs to treat a range of diseases. DOI: http://dx.doi.org/10.7554/eLife.00105.002

Background The interaction between Src homology 2 domain-containing protein tyrosine phosphatase (SHP-2) of gastric epithelial cells and cagA from H. pylori plays a crucial role in developments of gastric atrophy and gastric cancer. This study aimed to investigate the association of haplotype tagging SNPs (htSNPs) in the PTPN11 gene encoding SHP-2 with gastric atrophy and gastric cancer in Chinese population. Methods The subjects comprised 414 patients with gastric cancer, 109 individuals with gastric atrophy and 923 healthy controls. Blood was collected from October 2008 to October 2010. Five htSNPs rs2301756, rs12423190, rs12229892, rs7958372 and rs4767860 from the PTPN11 gene were selected and genotyped by Taqman assay. Serum Ig G antibodies to H. pylori were detected by ELISA. Gastric atrophy was screened by the levels of serum pepsinogenIandII, and confirmed by endoscopy and histopatholgical examinations. Odds ratio (ORs) and 95% confidence intervals (CIs) were calculated by a multivariate logistic regression. Results Among H. pylori seropositive subjects, age and gender-adjusted OR of gastric atrophy was 2.47 (95%CI 1.13-4.55, P = 0.02) for CC genotype compared with CT/TT genotypes, suggesting a recessive model of genetic risk for rs12423190. The prevalence of H. pylori seropositivity were significantly higher in groups of gastric cancer and gastric atrophy compared to the control group (70.3% vs. 75.2% vs. 49.7%, P Conclusions Our study provides the first evidence that rs12423190 polymorphism of the PTPN11 gene is significantly associated with an increased risk of gastric atrophy in H. pylori infected Chinese Han population, suggesting that rs12423190 polymorphism could be used as a useful marker of genetic susceptibility to gastric atrophy among H. pylori infected subjects. The biological roles of this polymorphism require a further investigation.

The cause of psoriasis, a common chronic inflammatory skin disease, is not fully understood. Microarray experiments have been widely used in recent years to identify genes associated with psoriasis pathology, by comparing expression levels of lesional (LS) with adjacent non-lesional (NL) skin. It is commonly observed that the differentially expressed genes (DEGs) differ greatly across experiments, due to variations introduced in the microarray experiment pipeline. Therefore, a statistically based meta-analytic approach, which combines the results of individual studies, is warranted. In this study, a meta-analysis was conducted on 5 microarray data sets, including 193 LS and NL pairs. We termed this the Meta-Analysis Derived (MAD) transcriptome. In "MAD-5" transcriptome, 677 genes were up-regulated and 443 were down-regulated in LS skin compared to NL skin. This represents a much larger set than the intersection of DEGs of these 5 studies, which consisted of 100 DEGs. We also analyzed 3 of the studies conducted on the Affymetrix hgu133plus2 chips and found a greater number of DEGs (1084 up- and 748 down-regulated). Top canonical pathways over-represented in the MAD transcriptome include Atherosclerosis Signaling and Fatty Acid Metabolism, while several "new" genes identified are involved in Cardiovascular Development and Lipid Metabolism. These findings highlight the relationship between psoriasis and systemic manifestations such as the metabolic syndrome and cardiovascular disease. Then, the Meta Threshold Gradient Descent Regularization (MTGDR) algorithm was used to select potential markers distinguishing LS and NL skin. The resulting set (20 genes) contained many genes that were part of the residual disease genomic profile (RDGP) or "molecular scar" after successful treatment, and also genes subject to differential methylation in LS tissues. To conclude, this MAD transcriptome yielded a reference list of reliable psoriasis DEGs, and represents a robust pool of candidates for further discovery of pathogenesis and treatment evaluation.

The zinc finger antiviral protein (ZAP) is a host factor that mediates inhibition of viruses in the Filoviridae, Retroviridae and Togaviridae families. We previously demonstrated that ZAP blocks replication of Sindbis virus (SINV), the prototype Alphavirus in the Togaviridae family at an early step prior to translation of the incoming genome and that synergy between ZAP and one or more interferon stimulated genes (ISGs) resulted in maximal inhibitory activity. The present study aimed to identify those ISGs that synergize with ZAP to mediate Alphavirus inhibition. Using a library of lentiviruses individually expressing more than 350 ISGs, we screened for inhibitory activity in interferon defective cells with or without ZAP overexpression. Confirmatory tests of the 23 ISGs demonstrating the largest infection reduction in combination with ZAP revealed that 16 were synergistic. Confirmatory tests of all potentially synergistic ISGs revealed 15 additional ISGs with a statistically significant synergistic effect in combination with ZAP. These 31 ISGs are candidates for further mechanistic studies. The number and diversity of the identified ZAP-synergistic ISGs lead us to speculate that ZAP may play an important role in priming the cell for optimal ISG function.

Summary. Background: Comparing a patient’s bleeding symptoms with those of healthy individuals is an important component of the diagnosis of bleeding disorders, but little is known about whether bleeding symptoms in healthy individuals vary by sex, race, ethnicity, age, or aspirin use. Objectives, Patients/Methods: We developed a comprehensive, ontology-backed, Web-based questionnaire to collect bleeding histories from 500 healthy adults. The mean age was 43 years (range 19–86 years), 63% were female, 19% were Hispanic, 37% were African-American, 43% were Caucasian, 8% were Asian, and 4% were multiracial. Results: 18 of the 36 symptoms captured occurred with

Background Studies of patients with paraneoplastic neurologic disorders (PND) have revealed that apoptotic tumor serves as a potential potent trigger for the initiation of naturally occurring tumor immunity. The purpose of this study was to assess the feasibility, safety, and immunogenicity of an apoptotic tumor-autologous dendritic cell (DC) vaccine. Methods and Findings We have modeled PND tumor immunity in a clinical trial in which apoptotic allogeneic prostate tumor cells were used to generate an apoptotic tumor-autologous dendritic cell vaccine. Twenty-four prostate cancer patients were immunized in a Phase I, randomized, single-blind, placebo-controlled study to assess the safety and immunogenicity of this vaccine. Vaccinations were safe and well tolerated. Importantly, we also found that the vaccine was immunogenic, inducing delayed type hypersensitivity (DTH) responses and CD4+ and CD8+ T cell proliferation, with no effect on FoxP3+ regulatory T cells. A statistically significant increase in T cell proliferation responses to prostate tumor cells in vitro (p = 0.002), decrease in prostate specific antigen (PSA) slope (p = 0.016), and a two-fold increase in PSA doubling time (p = 0.003) were identified when we compared data before and after vaccination. Conclusions An apoptotic cancer cell vaccine modeled on naturally occurring tumor immune responses in PND patients provides a safe and immunogenic tumor vaccine. (ClinicalTrials.gov number NCT00289341). Trial Registration ClinicalTrials.gov NCT00289341

Background Many studies have been published on the association between single nucleotide polymorphisms (SNP) near the IL28B gene and response to the combined treatments of pegylated-interferon (PegIFN) and ribavirin (RBV) in chronic HCV-infected patients, but without identical conclusions. The aim of this study was to assess impact of the IL28B polymorphisms on the effect of HCV standard treatment using meta-analysis based method. Methods Association studies between polymorphisms of rs12979860 or rs8099917 and response to PegIFN/RBV treatment in chronic HCV patients were retrieved from PubMed. Data of qualified studies on sustained virological response (SVR) in different genotypes were extracted and analyzed using meta-analysis method in Stata 10 software. Results Thirty-four papers, containing 46 independent studies, were included in the analysis. In the HCV G1/4 patients without treatment history, individuals carrying rs12979860 CC genotype were more likely to achieve SVR (OR 3.97, 95%CI 3.29–4.80) compared to those carrying CT/TT genotypes. Similar results were observed in the HCV G1/4 patients with unsuccessful or unknown treatment history (OR 3.76, 95%CI 2.67–5.28) or in the patients co-infected with human immunodeficiency virus (OR 5.20, 95%CI 3.04–8.90). However, associations could not be observed in HCV G2/3 patients. For rs8099917, similar results were obtained for genotype TT compared to genotypes TG/GG, indicating that TT genotype was significantly associated with better treatment response in patients infected with genotype 1 or 4 HCV, but not genotype 2 or 3 HCV. Conclusion Polymorphisms of rs12979860 and rs8099917 near IL28B only associate with the treatment response to PegIFN/RBV in patients infected with HCV genotype 1 or 4 but not with genotype 2 or 3, irrespective of the previous treatment history or HIV co-infected status. Therefore, identification of IL28B genotypes is necessary only in patients infected with relatively difficult-to-treat genotype 1 or 4 HCV.