The Causal Association Between Medication Intake and Increased Risk of Psoriasis.

INTRODUCTION: Psoriasis is a chronic, inflammatory, and papulo-squamous skin disorder without a radical cure. Although previous observational analyses have discovered a relationship between medication intake and increased risk of psoriasis, they are susceptible to confounders. OBJECTIVES: We intend to ascertain if there is a causal association between specific medication intake and increased risk of psoriasis by utilizing the Mendelian randomization (MR) method. METHODS: We obtained the genome-wide association study (GWAS) data for medication intake (23 types, N = 1809) from UK Biobank samples. And we sourced the GWAS data for psoriasis from the 8th release of the FinnGen database, which included 8,075 psoriasis cases and 330,975 healthy control cases. Then a two-sample MR study was performed to determine their causal association, and inverse-variance-weighted MR (IVW-MR) was applied to calculate the effect estimates. RESULTS: The IVW-MR analysis uncovered a positive correlation between the intake of HMG CoA reductase inhibitors and the increased risk of psoriasis (odds ratio [OR] = 1.167, 95% confidence interval [CI] = 1.084-1.257). Similarly, the use of thyroid preparations (OR=1.080, 95% CI=1.026-1.138), nonsteroidal anti-inflammatory and antirheumatic products (OR=1.406, 95% CI=1.037-1.908), anilides (OR=1.379, 95% CI=1.004-1.894), antihistamines for systemic use (OR=1.341, 95% CI=1.104-1.630), and antihypertensives (OR=1.099, 95% CI=1.016-1.190) were associated with an increased risk of psoriasis. We did not find evidence from IVW-MR for other associations. CONCLUSIONS: Our study offers a causal testimony that the intake of HMG CoA reductase inhibitors, thyroid preparations, nonsteroidal anti-inflammatory and antirheumatic products, anilides, antihistamines for systemic use, and antihypertensives will potentially increase the risk of psoriasis.

The Association between Dietary Sugar Intake and Nephrolithiasis: Results from National Health and Nutrition Examination Survey 2007-2018.

BACKGROUND: Dietary sugar intake is gradually considered a risk factor for many diseases. A sugary diet was positively associated with risk of nephrolithiasis, but the specific relationships remain undefined. OBJECTIVES: To determine associations between risk of nephrolithiasis and dietary sugar intake. METHODS: This cross-sectional study involved 21,590 participants based on the National Health and Nutrition Examination Survey from 2007 to 2018. Amounts of dietary sugar intake (g/d) were the main exposure, including total sugar intake, added sugar intake, and food sources. Associations were analyzed by logistic regression models and restricted cubic splines using complex weighted designs. RESULTS: Weighted mean intake [standard error] of total sugar and added sugar were 111.2 [2.0] g/d and 73.7 [1.9] g/d in participants with nephrolithiasis, respectively. In the fully adjusted regression model, compared to those in quartile 1, the population in quartile 4 of total sugar intake showed a significant risk of nephrolithiasis [odds ratio (OR): 1.23; 95% confidence interval (CI): 1.00-1.51]; OR for added sugar intake was 1.56 (95% CI: 1.25-1.94). The risks of nephrolithiasis increased steadily when total sugar and added sugar intake exceeded ∼150 g/d and 63 g/d in restricted cubic spline analyses, respectively. The highest sugar intake from beverages was associated with an increased risk of nephrolithiasis (OR for total sugar: 1.36; 95% CI: 1.07-1.72; OR for added sugar: 1.37; 95% CI: 1.09-1.73). Added sugar intake from meat, egg, and oil was significantly associated with risk of nephrolithiasis (quartile 4, OR: 1.22; 95% CI: 1.02-1.47), whereas total sugar intake from dairy products was in reverse (quartile 4, OR: 0.67; 95% CI: 0.54-0.82). CONCLUSIONS: Total and added sugar intake, sugar intake from beverages, and added sugar intake from meat, egg, and oil were associated with an increased risk of nephrolithiasis, whereas total sugar intake from dairy products was negatively associated.

A Machine Learning Analysis of Prognostic Genes Associated With Allograft Tolerance After Renal Transplantation.

In this study, we aimed to identify transplantation tolerance (TOL)-related gene signature and use it to predict the different types of renal allograft rejection performances in kidney transplantation. Gene expression data were obtained from the Gene Expression Omnibus (GEO) database, differently expressed genes (DEGs) were performed, and the gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted. The machine learning methods were combined to analyze the feature TOL-related genes and verify their predictive performance. Afterward, the gene expression levels and predictive performances of TOL-related genes were conducted in the context of acute rejection (AR), chronic rejection (CR), and graft loss through heatmap plots and the receiver operating characteristic (ROC) curves, and their respective immune infiltration results were also performed. Furthermore, the TOL-related gene signature for graft survival was conducted to discover gene immune cell enrichment. A total of 25 TOL-related DEGs were founded, and the GO and KEGG results indicated that DEGs mainly enriched in B cell-related functions and pathways. 7 TOL-related gene signature was constructed and performed delightedly in TOL groups and different types of allograft rejection. The immune infiltration analysis suggested that gene signature was correlated with different types of immune cells. The Kaplan-Meier (KM) survival analysis demonstrated that BLNK and MZB1 were the prognostic TOL-related genes. Our study proposed a novel gene signature that may influence TOL in kidney transplantation, providing possible guidance for immunosuppressive therapy in kidney transplant patients.

A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer.

BACKGROUND: Inflammation and long noncoding RNAs (lncRNAs) are gradually becoming important in the development of bladder cancer (BC). Nevertheless, the potential of inflammatory response-related lncRNAs (IRRlncRNAs) as a prognostic signature remains unexplored in BC. METHODS: The Cancer Genome Atlas (TCGA) provided RNA expression profiles and clinical information of BC samples, and GSEA Molecular Signatures database provided 1171 inflammation-related genes. IRRlncRNAs were identified using Pearson correlation analysis. After that, consensus clustering was performed to form molecular subtypes. After performing least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, a risk model constructed based on the prognostic IRRlncRNAs was validated in an independent cohort. Kaplan-Meier (KM) analysis, univariate and multivariate Cox regression, clinical stratification analysis, and time-dependent receiver operating characteristic (ROC) curves were utilized to assess clinical effectiveness and accuracy of the risk model. In clusters and risk model, functional enrichment was investigated using GSEA and GSVA, and immune cell infiltration analysis was demonstrated by ESTIMATE and CIBERSORT analysis. RESULTS: A total of 174 prognostic IRRlncRNAs were confirmed, and 406 samples were divided into 2 clusters, with cluster 2 having a significantly inferior prognosis. Moreover, cluster 2 exhibited a higher ESTIMATE score, immune infiltration, and PD-L1 expression, with close relationships with the inflammatory response. Further, 12 IRRlncRNAs were identified and applied to construct the risk model and divide BC samples into low-risk and high-risk groups successfully. KM, ROC, and clinical stratification analysis demonstrated that the risk model performed well in predicting prognosis. The risk score was identified as an independently significant indicator, enriched in immune, cell cycle, and apoptosis-related pathways, and correlated with 9 immune cells. CONCLUSION: We developed an inflammatory response-related subtypes and steady prognostic risk model based on 12 IRRlncRNAs, which was valuable for individual prognostic prediction and stratification and outfitted new insight into inflammatory response in BC.

A Novel Inflammation-Related Gene Signature for Overall Survival Prediction and Comprehensive Analysis in Pediatric Patients with Wilms Tumor.

Wilms tumor (WT) is a common pediatric renal cancer, with a poor prognosis and high-risk recurrence in some patients. The inflammatory microenvironment is gradually gaining attention in WT. In this study, novel inflammation-related signatures and prognostic model were explored and integrated using bioinformatics analysis. The mRNA profile of pediatric patients with WT and inflammation-related genes (IRGs) were acquired from Therapeutically Available Research to Generate Effective Treatments (TARGET) and Gene Set Enrichment Analysis (GSEA) databases, respectively. Then, a novel prognostic model founded on 7-IRGs signature (BICC1, CSPP1, KRT8, MYCN, NELFA, NXN, and RNF113A) was established by the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression to stratify pediatric patients with WT into high- and low-risk groups successfully. And a stable performance of the prognostic risk model was verified in predicting overall survival (OS) by receiver-operating characteristic (ROC) curves, Kaplan-Meier (KM) curves, and independent prognostic analysis (p < 0.05). In addition, a novel nomogram integrating risk scores with good robustness was developed and validated by C-index, ROC, and calibration plots. The potential function and pathway were explored via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA, with mainly inflammation and immune-related biological processes. The higher-risk scores, the lower immune infiltration, as shown in the single-sample GSEA (ssGSEA) and tumor microenvironment (TME) analysis. The drug sensitivity analysis showed that regulating 7-IRGs signature has a significant correlation with the chemotherapy drugs of WT patients. In summary, this study defined a prognostic risk model and nomogram based on 7-IRGs signature, which may provide novel insights into clinical prognosis and inflammatory study in WT patients. Besides, enhancing immune infiltration based on inflammatory response and regulating 7-IRGs signature are beneficial to ameliorating the efficacy in WT patients.