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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.

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.

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.

ObjectiveTo explore longitudinal change patterns of key laboratory tests in patients with COVID-19, and to identify independent prognostic factors by examining the associations between laboratory findings and outcomes of patients.MethodsThe multicenter study prospectively included 59 patients with COVID-19 treated at Jilin province from January 21, 2020 to May 5, 2020. Laboratory tests were included haematological, biochemical, and immunological tests.ResultsLaboratory findings, the characteristics of epidemiological and demographic data were extracted from electronic medical records. Eosinopenia was shown in 52.6% cases at onset, and the average value of eosinophil continued to significantly increase thereafter. Lymphopenia was found in 40.4% cases at onset, and the average value of lymphocyte was slowly elevated after day 5. Thrombocytopenia was shown in 12.3% cases at onset, and the average value of mean platelet volume was decreased sharply after day 7. The values of aspartate aminotransferase, lactate dehydrogenase, creatine kinase, creatinine kinase-muscle/brain activity, and cardiac troponin I, serum cardiac markers, were beyond the upper limit of RI from 6.1% to 30.6% at onset. The abnormity of liver function tests, kidney function tests, electrolytes was 2.0%∼59.2%, 2.0%∼4.1%, 6.0%∼30.0%, respectively. Eosinophil, platelet and carbondioxide combining power were selected as the prognostic factors.ConclusionsThe haematological, biochemical, and immunological tests were found significant abnormity at onset and longitudinal change patterns in the patients with COVID-19. Age, Eosinophil, PLT and CO2 may used to predict the recovery probability. Risk stratification and management could be improved for the patients with COVID-19 according to temporal trajectories of laboratory tests.Article SummaryThe longitudinal change patterns of the laboratory characteristics of affected patients were important to identify prognosis. Eosinophil, platelet and carbondioxide combining power may be independent predictors of recovery in patients with COVID-19.

Background Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer which is the leading cause of death in cancer patients. Circadian clock disruption has been listed as a likely carcinogen. However, whether the expression of circadian genes affects overall survival (OS) in LUAD patients remains unknown. In this article, we identified a circadian gene signature to predict overall survival in LUAD. Methods RNA sequencing (HTSeq-FPKM) data and clinical characteristics were obtained for a cohort of LUAD patients from The Cancer Genome Atlas (TCGA). A multigene signature based on differentially expressed circadian clock-related genes was generated for the prediction of OS using Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis, and externally validated using the GSE72094 dataset from the GEO database. Results Five differentially expressed genes (DEGs) were identified to be significantly associated with OS using univariate Cox proportional regression analysis (P < 0.05). Patients classified as high risk based on these five DEGs had significantly lower OS than those classified as low risk in both the TGCA cohort and GSE72094 dataset (P < 0.001). Multivariate Cox regression analysis revealed that the five-gene-signature based risk score was an independent predictor of OS (hazard ratio > 1, P < 0.001). Receiver operating characteristic (ROC) curves confirmed its prognostic value. Gene set enrichment analysis (GSEA) showed that Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to cell proliferation, gene damage repair, proteasomes, and immune and autoimmune diseases were significantly enriched. Conclusion A novel circadian gene signature for OS in LUAD was found to be predictive in both the derivation and validation cohorts. Targeting circadian genes is a potential therapeutic option in LUAD.

Lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) are two major subtypes of non-small cell lung cancer (NSCLC). Previous studies have demonstrated that fundamental differences exist in the underlying mechanisms of tumor development, growth and invasion between these subtypes. The investigation of differentially-expressed genes (DEGs) between these two NSCLC subtypes is useful for determining and understanding such differences. The present study aimed to identify those DEGs using meta-analysis and the data from four microarray experiments, consisting of 164 AC and 161 SCC samples. Raw gene expression values were converted into the probability of expression (POE) representing the differentially-expressed probability of a gene and expression barcode values representing its expression status. The results indicated that when applying a meta-analysis using barcode values, heterogeneity in genes across studies was less severe than when applying a meta-analysis using POE values. DEGs in each meta-analysis method overlapped substantially (P=1.3×10−4), but the barcode method yielded a lower global false discovery rate. Based on this and several other performance statistics, it was concluded that the barcode approach outperformed the POE method. Finally, using those DEGs, ontology and pathway analyses were conducted. A number of genes and enriched pathways were found to be closely associated with NSCLC.

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.

Background: We aimed to describe the laboratory characteristics of patients with laboratory confirmed coronavirus disease 2019 (COVID-19), and to compare the laboratory data between recovered and non-recovered patients. Methods: This is a multi-center study and 68 COVID-19 patients were recruited in Jilin Province, China, from January 21, 2020 to February 21, 2020. Laboratory tests were conducted at admission. Outcomes were followed up until February 21, 2020. Results: Of the 68 patients, 63 were diagnosed as mild and 5 as moderate or severe. After follow-up, there are 28 and 40 patients in recovered and non-recovered group, respectively. Lymphocytes, including immature leukocyte subpopulation, were significantly increased after day 10 in recovered patients. Platelets and thrombocytocrit were significantly increased, while mean platelet volume was reduced markedly from day 6 in recovered patients. High-sensitivity C-reactive protein was elevated at onset and continued to decline at day 7 in recovered patients. Cardiac troponin I is always higher from onset to recovery in recovered patients, yet it is sharply declined below the upper limit of reference interval after day 10. Cholinesterase and alanine aminotransferase were higher during the recovery process in recovered patients than in non-recovered patients. Both eosinophil and age were identified as independent predictors for recovery. Conclusions: Some markers had different change patterns between the recovered and non-recovered patients. Eosinophil may serve as an independent predictor for recovery in addition to age. The monitoring of the dynamic level of markers can give more effective clues for the judgment of the progress in COVID-19 patients. © 2021 AME Publishing Company. All rights reserved.

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.

Lung cancer (LC) is a leading cause of cancer-associated mortalities worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) account for ~70% of all cases of LC. Since AC and SCC are two distinct diseases, their corresponding prognostic genes associated with patient survival time are expected to be different. To date, only a few studies have distinguished patients with good prognosis from those with poor prognosis for each specific subtype. In the present study, the Cox filter model, a feature selection algorithm that identifies subtype-specific prognostic genes to incorporate pathway information and eliminate redundant genes, was adopted. By applying the proposed model to data on non-small cell lung cancer (NSCLC), it was demonstrated that both redundant gene elimination and search space restriction can improve the predictive capacity and the model stability of resulting prognostic gene signatures. To conclude, a pre-filtering procedure that incorporates pathway information for screening likely irrelevant genes prior to complex downstream analysis is recommended. Furthermore, a feature selection algorithm that considers redundant gene elimination may be preferable to one without such a consideration.

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.

Background It has been demonstrated that a pathway-based feature selection method that incorporates biological information within pathways during the process of feature selection usually outperforms a gene-based feature selection algorithm in terms of predictive accuracy and stability. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method. Methods In SAMGSR, whether a gene is selected is mainly determined by its expression difference between the phenotypes, and partially by the number of pathways to which this gene belongs. It ignores the topology information among pathways. In this study, we propose a weighted version of the SAMGSR algorithm by constructing weights based on the connectivity among genes and then combing these weights with the test statistics. Results Using both simulated and real-world data, we evaluate the performance of the proposed SAMGSR extension and demonstrate that the weighted version outperforms its original version. Conclusions To conclude, the additional gene connectivity information does faciliatate feature selection. Reviewers This article was reviewed by Drs. Limsoon Wong, Lev Klebanov, and, I. King Jordan. Electronic supplementary material The online version of this article (doi:10.1186/s13062-016-0152-3) contains supplementary material, which is available to authorized users.

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.

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.

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.

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.

Background As microarray technology has become mature and popular, the selection and use of a small number of relevant genes for accurate classification of samples has arisen as a hot topic in the circles of biostatistics and bioinformatics. However, most of the developed algorithms lack the ability to handle multiple classes, arguably a common application. Here, we propose an extension to an existing regularization algorithm, called Threshold Gradient Descent Regularization (TGDR), to specifically tackle multi-class classification of microarray data. When there are several microarray experiments addressing the same/similar objectives, one option is to use a meta-analysis version of TGDR (Meta-TGDR), which considers the classification task as a combination of classifiers with the same structure/model while allowing the parameters to vary across studies. However, the original Meta-TGDR extension did not offer a solution to the prediction on independent samples. Here, we propose an explicit method to estimate the overall coefficients of the biomarkers selected by Meta-TGDR. This extension permits broader applicability and allows a comparison between the predictive performance of Meta-TGDR and TGDR using an independent testing set. Results Using real-world applications, we demonstrated the proposed multi-TGDR framework works well and the number of selected genes is less than the sum of all individualized binary TGDRs. Additionally, Meta-TGDR and TGDR on the batch-effect adjusted pooled data approximately provided same results. By adding Bagging procedure in each application, the stability and good predictive performance are warranted. Conclusions Compared with Meta-TGDR, TGDR is less computing time intensive, and requires no samples of all classes in each study. On the adjusted data, it has approximate same predictive performance with Meta-TGDR. Thus, it is highly recommended.

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.

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.

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.