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Background Despite studies confirming that patients with inflammatory bowel disease (IBD) present with dyslipidaemia, the associations between IBD and the serum lipid profile have not been determined. The present study aimed to investigate the causal relationship between the serum lipid profile and IBD risk and elucidate the nature of the interactions between them.Methods Two-sample Mendelian randomisation (MR) analysis was performed to investigate the causal links between total cholesterol (TC), total triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A (Apo A), apolipoprotein B (Apo B) and lipoprotein (a) (Lp(a)) and IBD. The study was carried out using the R TwoSampleMR and Mendelian randomisation packages.Results All MR methods, including the weighted median, weighted mode, inverse-variance weighted model, MR-PRESSO, contamination mixture and MR Egger, supported a null causal relationship between TG, TC, HDL-C, LDL-C, Apo A, Apo B and Lp(a) and between IBD, Crohn’s disease and ulcerative colitis. Null causal effects of lipid indices on IBD were validated through independent genome-wide association studies (GWAS), indicating that the findings are robust.Conclusion Our findings suggest that none of the seven lipid indices may be a potential risk factor for the onset of IBD. However, additional research is needed since our MR analyses cannot assess the potential non-linear causal relationship between serum lipids and IBD.

As a key adipokine, leptin has been extensively investigated for its potential role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, concordant conclusions have not been attained. In this study, we investigated the relationship between leptin and COPD using an integrative analysis that combined a Mendelian randomization (MR) study with transcriptomic data analysis. Here, the MR analysis was performed on the online platform MR-Base, and the bioinformatics analyses were performed with the aid of R Bioconductor packages. No evidence was found by the integrative analysis to support the association of the two attributes. All methods detected a null causal effect of leptin on COPD in the MR analysis. In particular, when the genetically predicted leptin level increased one unit, the risk of developing COPD was estimated as 0.999 (p = 0.943), 0.920 (p = 0.516), 1.002 (p = 0.885), and 1.002 (p = 0.906) by the Inverse Variance Weighted (IVW), MR-Egger, weighted median, and weighted mode method, respectively. Furthermore, no leptin-associated genes except one were identified as being differentially expressed between COPD and control in bioinformatics analysis. The observed association between leptin and COPD in previous observational studies may be attributable to unmeasured confounding effects or reverse causation.

Abstract Lung cancer, a major contributor to cancer-related fatalities worldwide, involves a complex pathogenesis. Cathepsins, lysosomal cysteine proteases, play roles in various physiological and pathological processes, including tumorigenesis. Observational studies have suggested an association between cathepsins and lung cancer. However, the causal link between the cathepsin family and lung cancer remains undetermined. This study employed Mendelian randomization analyses to investigate this causal association. The univariable Mendelian randomization analysis results indicate that elevated cathepsin H levels increase the overall risk of lung cancer, adenocarcinoma, and lung cancer among smokers. Conversely, reverse Mendelian randomization analyses suggest that squamous carcinoma may lead to increased cathepsin B levels. A multivariable analysis using nine cathepsins as covariates reveals that elevated cathepsin H levels lead to an increased overall risk of lung cancer, adenocarcinoma, and lung cancer in smokers. In conclusion, cathepsin H may serve as a marker for lung cancer, potentially inspiring directions in lung cancer diagnosis and treatment.

AimThis study aimed to examine the association of liver enzymes, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl-transferase (GGT), with type 2 diabetes (T2D) risk, particularly their dose-response relationship.MethodsThis cross-sectional study enrolled participants aged >20 years old who underwent physical examination at our local hospital from November 2022 to May 2023. A generalized additive model (GAM) was fit to assess the dose-response relationship between liver enzymes and T2D risk. Furthermore, data from the UK Biobank (n=217,533) and National Health and Nutrition Examination Survey (NHANES 2011-2018; n= 15,528) were analyzed to evaluate whether the dose-response relationship between liver enzymes and T2D differed by population differences.ResultsA total of 14,100 participants were included (1,155 individuals with T2D and 12,945 individuals without diabetes) in the analysis. GAM revealed a non-linear relationship between liver enzymes and T2D risk (Pnon-linear < 0.001). Specifically, T2D risk increased with increasing ALT and GGT levels (range, 50 IU/L. Elevated AST within a certain range (range, 35 IU/L) became a risk factor for T2D. The UK Biobank and NHANES data analysis also showed a similar non-linear pattern between liver enzymes and T2D incidence.ConclusionLiver enzymes were non–linearly associated with T2D risk in different populations, including China, the UK, and the US. Elevated ALT and GGT levels, within a certain range, could increase T2D risk. More attention should be given to liver enzyme levels for early lifestyle intervention and early T2D prevention. Further studies are necessary to explore the mechanism of the non-linear association between liver enzymes and T2D risk.

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.

ABSTRACTObservational studies have been conducted to investigate the correlation between adiponectin and diabetic retinopathy (DR), but no consistent relationship has been established. In this study, we employed an integrative analysis that combined Mendelian randomization (MR) and bioinformatics analyses to comprehensively explore the association between DR and adiponectin, aiming to provide a unified answer of their relationship. Using the inverse-variance weighted (IVW) method, the odd ratio (OR) of developing DR per 1 mg/dL increment in genetically predicted log-transformed adiponectin concentration was estimated to be 0.949 (P = 0.557). Other robust MR methods produced consistent results, confirming the absence of a causal effect of adiponectin on DR. Additionally, the expression levels of the six adiponectin-related genes showed no significant differences among normal controls, individuals with diabetes but without DR, and those with DR Furthermore, the biological pathways enriched by these genes were not strongly relevant to DR. At both the individual gene and pathway levels, there were no overlaps between the adiponectin-related genes and the differentially expressed genes, indicating a lack of association between adiponectin and DR based on gene expression profiles. In summary, the integrative analysis, which combined MR and bioinformatics data mining, yielded compelling evidence supporting the notion that adiponectin is not a risk factor for DR.

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.

AimsAlanine aminotransferase (ALT) is positively related to diabetes risk in observational studies, whereas Mendelian randomization supports a linear causal association. In contrast, the relationship between ALT and diabetic nephropathy, and diabetic retinopathy is counter-intuitive in observational studies. Furthermore, no MR study has examined their causal association. The study aimed to investigate whether genetically determined ALT has a causal effect on diabetic nephropathy and diabetic retinopathy.MethodsGenetic instruments associated with ALT (P < 5×10-8) were obtained from a recent genome-wide association study (GWAS) that included 437,267 individuals of European ancestry. Summary data of diabetic microvascular complications were derived from the FinnGen study (3,283 cases and 181,704 controls for diabetic nephropathy, and 14,584 cases and 176,010 controls for diabetic retinopathy, both were of European ancestry). Effect estimation and pleiotropy testing were performed using inverse variance weighted (IVW), MR-Egger regression, weighted median, and mode-based estimator methods. We additionally performed sensitivity analysis excluding proxy single nucleotide polymorphisms (SNPs) or lowering the GWAS significance threshold (P < 5×10-7) to test the robustness of the results.ResultsBased on IVW, a 2-fold increase in genetically determined ALT level was positively associated with diabetic nephropathy (odd ratio, [95% confidence interval], 1.73 [1.26-2.37], P = 0.001) and diabetic retinopathy (1.29 [1.08-1.54], P = 0.005), but a null causal association in three pleiotropy robust methods, namely, MR-Egger, weighted median and mode-based estimator. We obtained similar results in the sensitivity analysis of excluding proxy SNPs or lowering the GWAS significance threshold.ConclusionsWith caution, we concluded that ALT plays no linear causal role in developing both diabetic nephropathy and diabetic retinopathy. Further investigations are required to test the hypothesis of a non-linear causal association.

AimThe association between gamma-glutamyl transferase (GGT) and type 2 diabetes mellitus (T2DM) is controversial. In this study, we investigated the association between GGT and the risk of T2DM using real-world data, Mendelian randomization (MR) analysis, and literature mining.MethodsA cross-sectional study enrolled 3,048 participants (>40 years) from a community in Northeastern China was conducted. A generalized additive model was used to examine the relation between GGT and T2DM. A two-sample MR was performed to investigate the causal effect of GGT (61,089 individuals, mostly of European ancestry) on T2DM (29,193 cases and 182,573 controls of European ancestry).ResultsGGT was related to glucose metabolism indicators, such as fasting plasma glucose and glycosylated hemoglobin (P < 0.05). The odds ratios (ORs) [95% confidence interval (95% CI), P] for T2DM across the GGT categories (14–16, 17–20, 21–25, 26–35, ≥36) were 1.14 [(0.88-1.47), P = 0.330], 1.55 [(1.22-1.98), P < 0.001], 1.87 [(1.47-2.28), P < 0.001], 1.97 [(1.55-2.52), P < 0.001], and 2.29 [(1.78-2.94), P < 0.001] versus GGT ≤ 13 category after adjusting for potential confounding factors. A generalized additive model identified a non-linear correlation between GGT and T2DM and indicated that the risk of T2DM almost levelled out when GGT exceeded 34 IU/L. The MR analysis showed that the odds of having T2DM for a one-time increase in genetically determined GGT was 0.998 [(0.995-1.002), P = 0.34].ConclusionsOur analysis of observational study suggested that GGT, its increment, within a certain range, is indicative of the development of T2DM. However, MR analysis provided no evidence that GGT is a linear causal factor of T2DM. Further investigation is required to determine if GGT exerts a non-linear causal effect on T2DM.

Abstract 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.

Abstract 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.

The question of whether or not intermittent fasting diets improve the clinical indicators of glycolipid metabolism remains unclear. This study systematically reviewed the relevant clinical trials to evaluate the effects of intermittent fasting diet on glucose and lipid metabolism and insulin sensitivity in patients with metabolic syndrome. To evaluate the effect of intermittent fasting diet intervention on patients with disorders of glucose and lipid metabolism, random-effect or fixed-effect meta-analysis models were used to calculate the average difference before and after intermittent fasting diet intervention and the corresponding 95% confidence intervals (CIs). After intermittent fasting diet intervention, in terms of glucose metabolism, fasting blood glucose reduced by 0.15 mmol/L (95% CI: −0.23; −0.06), glycosylated hemoglobin reduced by 0.08 (95% CIs: −0.25; −0.10), insulin plasma levels reduced by 13.25 uUI (95% CIs: −16.69; −9.82), and HOMA-IR decreased by 0.31 on an average (95% CIs: −0.44; −0.19). In addition, BMI decreased by 0.8 kg/m2 (95% CIs: −1.32; −0.28), body weight reduced by 1.87 kg (95% CIs: −2.67; −1.07), and the waist circumference decreased by 2.08 cm (95% CIs: −3.06; −1.10). Analysis of lipid metabolism showed that intermittent fasting diet intervention effectively reduced the total cholesterol level by 0.32 mmol/L (95% CIs: −0.60; −0.05), low-density lipoprotein level by 0.22 mmol/L (95% CIs: −0.37; −0.07), and triglyceride level by 0.04 mmol/L (95% CIs: −0.15; −0.07). Intermittent fasting diets have certain therapeutic effects on blood glucose and lipids in patients with metabolic syndrome and significantly improve insulin resistance. It may be considered as an auxiliary treatment to prevent the occurrence and development of chronic diseases.

Abstract 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 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.

Abstract 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.

Abstract 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.

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 one of the most common neurological disabilities of the central nervous system. Immune-modulatory therapy with Interferon-β (IFN-β) is a commonly used first-line treatment to prevent MS patients from relapses. Nevertheless, a large proportion of MS patients on IFN-β therapy experience their first relapse within 2 years of treatment initiation. Feature selection, a machine learning strategy, is routinely used in the fields of bioinformatics and computational biology to determine which subset of genes is most relevant to an outcome of interest. The majority of feature selection methods focus on alterations in gene expression levels. In this study, we sought to determine which genes are most relevant to relapse of MS patients on IFN-β therapy. Rather than the usual focus on alterations in gene expression levels, we devised a feature selection method based on alterations in gene-to-gene interactions. In this study, we applied the proposed method to a longitudinal microarray dataset and evaluated the IFN-β effect on MS patients to identify gene pairs with differentially correlated edges that are consistent over time in the responder group compared to the non-responder group. The resulting gene list had a good predictive ability on an independent validation set and explicit biological implications related to MS. To conclude, it is anticipated that the proposed method will gain widespread interest and application in personalized treatment research to facilitate prediction of which patients may respond to a specific regimen.

The emerging field of pathway-based feature selection that incorporates biological information conveyed by gene sets/pathways to guide the selection of relevant genes has become increasingly popular and widespread. In this study, we adapt a gene set analysis method – the significance analysis of microarray gene set reduction (SAMGSR) algorithm to carry out feature selection for longitudinal microarray data, and propose a pathway-based feature selection algorithm – the two-level SAMGSR method. By using simulated data and a real-world application, we demonstrate that a gene’s expression profiles over time can be considered as a gene set. Thus a suitable gene set analysis method can be utilized or modified to execute the selection of relevant genes for longitudinal omics data. We believe this work paves the way for more research to bridge feature selection and gene set analysis with the development of novel pathway-based feature selection algorithms.

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.

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.

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.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-associated mortality worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two primary histological subtypes of NSCLC, accounting for ~70% of lung cancer cases. Increasing evidence suggests that AC and SCC differ in the composition of genes and molecular characteristics. Previous research has focused on distinguishing AC from SCC or predicting the NSCLC patient survival rates using gene expression profiles, usually with the aid of a feature selection method. The present study conducted a pre-filtering to identify the genes that have significant expression values and a high connection with other genes in the gene network, and then used the radial coordinate visualization method to identify relevant genes. By applying the proposed procedure to NSCLC data, it was demonstrated that there is a clear segmentation between AC and SCC, however not between patients with a good prognosis and bad prognosis. The focus of discriminating AC and SCC differs from survival prediction and there are almost no overlaps between the two gene signatures. Overall, a supervised learning method is preferred and future studies aiming to identify prognostic gene signatures with an increased prediction efficiency are required. [ABSTRACT FROM AUTHOR]

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=1.3x10-4), 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background Over the last decade, metabolomics has evolved into a mainstream enterprise utilized by many laboratories globally. Like other "omics" data, metabolomics data has the characteristics of a smaller sample size compared to the number of features evaluated. Thus the selection of an optimal subset of features with a supervised classifier is imperative. We extended an existing feature selection algorithm, threshold gradient descent regularization (TGDR), to handle multi-class classification of "omics" data, and proposed two such extensions referred to as multi-TGDR. Both multi-TGDR frameworks were used to analyze a metabolomics dataset that compares the metabolic profiles of hepatocellular carcinoma (HCC) infected with hepatitis B (HBV) or C virus (HCV) with that of cirrhosis induced by HBV/HCV infection; the goal was to improve early-stage diagnosis of HCC. Results We applied two multi-TGDR frameworks to the HCC metabolomics data that determined TGDR thresholds either globally across classes, or locally for each class. Multi-TGDR global model selected 45 metabolites with a 0% misclassification rate (the error rate on the training data) and had a 3.82% 5-fold cross-validation (CV-5) predictive error rate. Multi-TGDR local selected 48 metabolites with a 0% misclassification rate and a 5.34% CV-5 error rate. Conclusions One important advantage of multi-TGDR local is that it allows inference for determining which feature is related specifically to the class/classes. Thus, we recommend multi-TGDR local be used because it has similar predictive performance and requires the same computing time as multi-TGDR global, but may provide class-specific inference. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]