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.

Characteristic Genes and Immune Infiltration Analysis for Acute Rejection after Kidney Transplantation.

Background: Renal transplantation can significantly improve the survival rate and quality of life of patients with end-stage renal disease, but the probability of acute rejection (AR) in adult renal transplant recipients is still approximately 12.2%. Machine learning (ML) is superior to traditional statistical methods in various clinical scenarios. However, the current AR model is constructed only through simple difference analysis or a single queue, which cannot guarantee the accuracy of prediction. Therefore, this study identified and validated new gene sets that contribute to the early prediction of AR and the prognosis prediction of patients after renal transplantation by constructing a more accurate AR gene signature through ML technology. Methods: Based on the Gene Expression Omnibus (GEO) database and multiple bioinformatic analyses, we identified differentially expressed genes (DEGs) and built a gene signature via LASSO regression and SVM analysis. Immune cell infiltration and immunocyte association analyses were also conducted. Furthermore, we investigated the relationship between AR genes and graft survival status. Results: Twenty-four DEGs were identified. A 5 gene signature (CPA6, EFNA1, HBM, THEM5, and ZNF683) were obtained by LASSO analysis and SVM analysis, which had a satisfied ability to differentiate AR and NAR in the training cohort, internal validation cohort and external validation cohort. Additionally, ZNF683 was associated with graft survival. Conclusion: A 5 gene signature, particularly ZNF683, provided insight into a precise therapeutic schedule and clinical applications for AR patients.