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Epidemiological studies have shown that the levels of serum adipokine such as leptin and resistin are associated with the risk of developing systemic lupus erythematosus (SLE). Nevertheless, whether either leptin or resistin has causal impacts on the risk of SLE is still unknown. In this study, two-sample univariable MR analyses and multivariable MR analysis were performed to explore the causal relationships between adipokines and SLE. Additionally, the potential causal effects of SLE on major adipokines were evaluated using reverse MR analyses. The results of inverse-variance weighted (IVW), weighted median, weighted mode and MR‒Egger methods concordantly supported that major adipokines have no causal effects on the risk of SLE. In the multivariable MR IVW analysis with leptin and resistin as covariates, neither leptin (odds ratio (OR) = 3.093, P = 0.067) nor resistin (OR = 0.477, P = 0.311) was identified as an independent risk factor for SLE, which is in line with the univariable MR results. In conclusion, our analyses revealed no evidence to support that these three major adipokines are risk factors for SLE.

BackgroundObservational studies have demonstrated an association between white blood cells (WBC) subtypes and type 2 diabetes (T2D) risk. However, it is unknown whether this relationship is causal. We used Mendelian randomization (MR) to investigate the causal effect of WBC subtypes on T2D and glycemic traits.MethodsThe summary data for neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were extracted from a recent genome-wide association study (n = 173,480). The DIAGRAM and MAGIC consortia offered summary data pertaining to T2D and glycemic characteristics, including fasting glucose (FG) (n = 133,010), glycosylated hemoglobin (HbA1c) (n = 46,368), and homeostatic model assessment-estimated insulin resistance (HOMA-IR) (n = 37,037). A series of MR analyses (univariable MR, multivariable MR, and reverse MR) were used to investigate the causal association of different WBC subtypes with T2D and glycemic traits.ResultsUsing the inverse-variance weighted method, we found one standard deviation increases in genetically determined neutrophil [odd ratio (OR): 1.086, 95% confidence interval (CI): 0.877-1.345], lymphocyte [0.878 (0.766-1.006)], monocyte [1.010 (0.906-1.127)], eosinophil [0.995 (0.867-1.142)], and basophil [0.960 (0.763-1.207)] were not causally associated with T2D risk. These findings were consistent with the results of three pleiotropy robust methods (MR-Egger, weighted median, and mode-based estimator) and multivariable MR analyses. Reverse MR analysis provided no evidence for the reverse causation of T2D on WBC subtypes. The null causal effects of WBC subtypes on FG, HbA1c, and HOMA-IR were also identified.ConclusionsWBCs play no causal role in the development of insulin resistance and T2D. The observed association between these factors may be explained by residual confounding.

Lung adenocarcinoma (LUAD) has a poor prognosis. Circadian genes such as TIMELESS have been associated with several pathologies, including cancer. The expression of TIMELESS and the relationship between TIMELESS, infiltration of tumors and prognosis in LUAD requires further investigation. In this study, we investigated the expression of TIMELESS and its association with survival across several types of human cancer using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Program. Noncoding RNAs (ncRNAs) regulating overexpression of TIMELESS in lung adenocarcinoma (LUAD) were explored with expression, correlation, and survival analyses. Immune cell infiltration and biomarkers were analyzed between different TIMELESS expression levels. The relationship between TIMELESS expression and immunophenoscores, which were used to predict response to immunotherapy, was evaluated. TIMELESS was identified as a potential oncogene in LUAD. NcRNA analysis showed MIR4435-2HG/hsa-miR-1-3p may interact with TIMELESS in a competitive endogenous RNA network in LUAD tumor tissues. Most immune cells were significantly decreased in TCGA LUAD tumor tissues with high TIMELESS expression except for CD4+T cells and Th2 cells. TIMELESS expression in LUAD tumor tissues was significantly negatively correlated with neutrophil biomarkers, dendritic cell biomarkers (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C) and an immunophenoscore that predicted outcomes associated with the use of immune checkpoint inhibitors. These findings imply that ncRNAs-mediated TIMELESS overexpression in LUAD tumor tissues correlated with poor prognosis, reduced immune cell infiltration in the tumor microenvironment, and poor response to immune checkpoint inhibitors.

As breast cancer is a multistage progression disease resulting from a genetic sequence of mutations, understanding the genes whose expression values increase or decrease monotonically across pathologic stages can provide insightful clues about how breast cancer initiates and advances. Utilizing variational autoencoder (VAE) networks in conjunction with traditional statistical testing, we successfully ascertain long non-coding RNAs (lncRNAs) that exhibit monotonically differential expression values in breast cancer. Subsequently, we validate that the identified lncRNAs really present monotonically changed patterns. The proposed procedure identified 248 monotonically decreasing expressed and 115 increasing expressed lncRNAs. They correspond to a total of 65 and 33 genes respectively, which possess unique known gene symbols. Some of them are associated with breast cancer, as suggested by previous studies. Furthermore, enriched pathways by the target mRNAs of these identified lncRNAs include the Wnt signaling pathway, human papillomavirus (HPV) infection, and Rap 1 signaling pathway, which have been shown to play crucial roles in the initiation and development of breast cancer. Additionally, we trained a VAE model using the entire dataset. To assess the effectiveness of the identified lncRNAs, a microarray dataset was employed as the test set. The results obtained from this evaluation were deemed satisfactory. In conclusion, further experimental validation of these lncRNAs with a large-sized study is warranted, and the proposed procedure is highly recommended.

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.

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.

INTRODUCTION:The widespread application of microarray experiments to cancer research is astounding including lung cancer, one of the most common fatal human tumors. Among non-small cell lung carcinoma (NSCLC), there are two major histological types of NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC). RESULTS:In this paper, we proposed to integrate a visualization method called Radial Coordinate Visualization (Radviz) with a suitable classifier, aiming at discriminating two NSCLC subtypes using patients' gene expression profiles. Our analyses on simulated data and a real microarray dataset show that combining with a classification method, Radviz may play a role in selecting relevant features and ameliorating parsimony, while the final model suffers no or least loss of accuracy. Most importantly, a graphic representation is more easily understandable and implementable for a clinician than statistical methods and/or mathematic equations. CONCLUSION:To conclude, using the NSCLC microarray data presented here as a benchmark, the comprehensive understanding of the underlying mechanism associated with NSCLC and of the mechanisms with its subtypes and respective stages will become reality in the near future.

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.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.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.

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.

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.

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.

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.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.An apoptotic cancer cell vaccine modeled on naturally occurring tumor immune responses in PND patients provides a safe and immunogenic tumor vaccine.ClinicalTrials.gov NCT00289341.