Mendelian randomization analyses reveal no genetic causal effects of major adipokines on systemic lupus erythematosus.

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.

Exploring the causal relationship between Takayasu arteritis and inflammatory bowel disease using Mendelian randomization.

Takayasu arteritis (TA) and inflammatory bowel disease (IBD) are two distinct diseases; however, previous studies have reported many cases of IBD-TA coexistence. Additionally, the incidence of IBD in patients with TA is estimated to be significantly higher than the incidence in the general population. Therefore, the two diseases are anticipated to be linked. Mendelian randomization (MR) analysis assesses whether an exposure might causally affect an outcome by using genetic variants inherited randomly at conception, thereby reducing the impact of confounding and reverse causality. The present study aimed to investigate the potential causal relationship between TA and IBD using MR analysis. Two-sample MR analysis, in which TA and IBD were regarded as the exposure and outcome, respectively, was conducted to investigate whether the two diseases are causally related using the R TwoSampleMR package. Summary GWAS data of TA consisted of 516 Turkish cohorts and 462 controls, and 119 patients and 993 controls of European ancestry. Summary data of IBD was from a sub-study of the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC) that comprised 31,665 cases and 33,977 controls of European ancestry. Additionally, separate MR analyses stratified by the two major subtypes of IBD, Crohn's disease (CD) and ulcerative colitis (UC), were performed. Various statistical tests, including the intercept of MR-Egger regression, funnel plots, Cochran's Q tests, and leave-one-out sensitivity analyses, were employed to assess the presence of heterogeneity and horizontal pleiotropy among single nucleotide polymorphisms (SNPs). In the primary analysis using the inverse-variance weighted (IVW) method, the risk of developing IBD for a patient with TA compared to a non-TA control increased 1.053 times (Odds Ratio (OR) = 1.053, P = 0.065). The MR-Egger method (OR = 1.025, P = 0.470) yielded results consistent with this null finding. However, both the weighted median method (OR = 1.038, P = 0.002) and the weighted mode method (OR = 1.051, P = 0.009) identified a significant harmful causal effect. The MR outcomes from separate subgroup analyses slightly diverged from those of IBD and TA. Specifically, for CD, three methods indicated that TA is a risk factor: IVW estimated the OR as 1.045 (P = 0.032), MR-Egger as 0.997 (P = 0.957), weighed median as 1.028 (P = 0.021), and weighted mode as 1.031 (P = 0.022), respectively. This study represents one of the initial investigations into the potential causal association between TA and IBD. With three MR methods, including the primary IVW approach, indicating a notable effect on TA on CD, our analysis findings offer some indication that TA could be a contributing risk factor for CD.

White blood cells and type 2 diabetes: A Mendelian randomization study.

BACKGROUND: Observational 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. METHODS: The 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. RESULTS: Using 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. CONCLUSIONS: WBCs play no causal role in the development of insulin resistance and T2D. The observed association between these factors may be explained by residual confounding.

Association between liver enzymes and type 2 diabetes: a real-world study.

Aim: This 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. Methods: This 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. Results: A 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 (P non-linear < 0.001). Specifically, T2D risk increased with increasing ALT and GGT levels (range, <50 IU/L) and then plateaued when ALT and GGT levels were >50 IU/L. Elevated AST within a certain range (range, <35 IU/L) decreased the risk of T2D, whereas mildly elevated AST (>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. Conclusion: Liver 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.

ncRNAs-mediated TIMELESS overexpression in lung adenocarcinoma correlates with reduced tumor immune cell infiltration and poor prognosis.

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.

Clinical Evidence of the Relationship Between Alanine Aminotransferase and Diabetic Kidney Disease.

Aim: Multiple studies have investigated the association between alanine aminotransferase (ALT) and diabetes mellitus (DM); however, only a few studies have specifically examined the relationship between ALT and diabetic kidney disease (DKD). This study aimed to investigate the relationship between ALT and DKD using clinical data. Methods: A cross-sectional study was conducted on 668 individuals that included non-DM (N=281), DM without DKD (N=160), and DKD (N=227) patients. A generalized additive model (GAM) was used to examine the dose-response relationship between ALT and DKD risk. We also analyzed the data from the US National Health and Nutrition Examination Survey (NHANES) 2015-2018 using the same statistical methods; 4481, 1110, and 671 individuals were included in the non-DM, DM without DKD, and DKD groups, respectively. Results: The changes in ALT activity among the non-DM, DM without DKD, and DKD groups showed a similar pattern in both our clinical data and the NHANES dataset. ALT activity increases with the onset of DM, whereas ALT activity decreases when DM progresses to DKD. The GAM revealed a nonlinear U-shaped relationship between ALT and DKD risk in the two datasets, and the lowest range of ALT was 40-50 IU/L. Both lower (<40 IU/L) and higher (>50 IU/L) ALT activity were found to be positively associated with DKD risk. Conclusion: A U-shaped nonlinear association between ALT and DKD was found in our clinical data and NHANES data. DKD risk was increased by both lower or higher ALT activity. To confirm the causality of nonlinear relationship, larger prospective studies or Mendelian randomization analysis are required.

Mendelian randomization analyses explore the relationship between cathepsins and lung cancer.

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.

Integrative Analyses of Mendelian Randomization and Transcriptomic Data Reveal No Association between Leptin and Chronic Obstructive Pulmonary Disease.

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.

Identification of monotonically expressed long non-coding RNA signatures for breast cancer using variational autoencoders.

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.

Integrative analysis of Mendelian randomization and gene expression profiles reveals a null causal relationship between adiponectin and diabetic retinopathy.

Observational 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.

The association of alanine aminotransferase and diabetic microvascular complications: A Mendelian randomization study.

Aims: Alanine 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. Methods: Genetic 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. Results: Based 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. Conclusions: With 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.

Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer's Disease.

Lung cancer is the most common malignancy and is responsible for the largest cancer-related mortality worldwide. Alzheimer's disease is a degenerative neurological disease that burdens healthcare worldwide. While the two diseases are distinct, several transcriptomic studies have demonstrated they are linked. However, no concordant conclusion on how they are associated has been drawn. Since these studies utilized conventional bioinformatics methods, such as the differentially expressed gene (DEG) analysis, it is naturally expected that the proportion of DEGs having either the same or inverse directions in lung cancer and Alzheimer's disease is substantial. This raises the inconsistency. Therefore, a novel bioinformatics method capable of determining the direction of association is desirable. In this study, the moderated t-tests were first used to identify DEGs that are shared by the two diseases. For the shared DEGs, separate autoencoder (AE) networks were trained to extract a one-dimensional representation (pseudogene) for each disease. Based on these pseudogenes, the association direction between lung cancer and Alzheimer's disease was inferred. AE networks based on 266 shared DEGs revealed a comorbidity relationship between Alzheimer's disease and lung cancer. Specifically, Spearman's correlation coefficient between the predicted values using the two AE networks for the Alzheimer's disease test set was 0.825 and for the lung cancer test set was 0.316. Novel bioinformatics methods such as an AE network may help decipher how distinct diseases are associated by providing the refined representations of dysregulated genes.

To explore association between gamma-glutamyl transferase and type 2 diabetes using a real-world study and mendelian randomization analysis.

Aim: The 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. Methods: A 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). Results: GGT 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]. Conclusions: Our 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.

Effect of Intermittent Fasting Diet on Glucose and Lipid Metabolism and Insulin Resistance in Patients with Impaired Glucose and Lipid Metabolism: A Systematic Review and Meta-Analysis.

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.

Exploring exosome data to identify prognostic gene signatures for lung adenocarcinoma.

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

Lay abstract Exosome products are related to tumorigenesis, growth and metastasis, and also have potential prognostic value in lung cancer. The data and information for lung adenocarcinoma patients retrieved from databases were used to develop a risk score model. The molecular mechanisms and immune system activity in high- and low-risk groups of patients with lung adenocarcinoma based on the median risk score were obviously different. The risk score model contained 19 related genes. Patients with low-risk scores had better prognoses. The novel risk score model can be used to predict the outcome for patients with lung adenocarcinoma.

Identification of a circadian gene signature that predicts overall survival in lung adenocarcinoma.

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.

GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies.

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

An ensemble of the iCluster method to analyze longitudinal lncRNA expression data for psoriasis patients.

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.

Neutrophil-Lymphocyte Ratio as a Prognostic Parameter in NSCLC Patients Receiving EGFR-TKIs: A Systematic Review and Meta-Analysis.

OBJECTIVE: To research the impact of neutrophil-lymphocyte ratio (NLR) as a prognostic parameter in non-small-cell lung cancer (NSCLC) patients treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). METHODS: We searched the databases such as the American Society of Clinical Oncology (ASCO), EMBASE, PubMed, the European Society of Medical Oncology (ESMO), Wanfang, and CNKI for articles illustrating the impact of pretreatment NLR on survival data in NSCLC patients undergoing EGFR-TKIs treatment. We did a meta-analysis for overall survival (OS) and progression-free survival (PFS). RESULTS: We recruited 10 studies in our meta-analysis. Our study suggested that patients with low NLR had better PFS (hazard ratio (HR) = 1.67, 95% confidence interval (CI) = (1.16-2.39), and P value = 0.005) and OS (HR = 1.66, 95% CI = (1.08-2.55), and P value = 0.02) in comparison to patients with high NLR. CONCLUSION: In conclusion, our meta-analysis revealed that lower NLR predicted a better survival (PFS and OS) in patients receiving the treatment of EGFR-TKIs.

An edge-based statistical analysis of long non-coding RNA expression profiles reveals a negative association between Parkinson's disease and colon cancer.

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.