Objective. Asthma (AS) is a chronic inflammatory disease of the airway, and macrophages contribute to AS remodeling. Our study aims at screening macrophage-related gene signatures to build a risk prediction model and explore its predictive abilities in AS diagnosis. Methods. Three microarray datasets were downloaded from the GEO database. The Limma package was used to screen differentially expressed genes (DEGs) between AS and controls. The ssGSEA algorithm was used to determine immune cell proportions. The Pearson correlation coefficient was computed to select the macrophage-related DEGs. The LASSO and RFE algorithms were implemented to filter the macrophage-related DEG signatures to establish a risk prediction model. Receiver operating characteristic (ROC) curves were used to assess the diagnostic ability of the prediction model. Finally, the qPCR was used to detect the expression of selected differential genes in sputum from healthy people and asthmatic patients. Results. We obtained 1,189 DEGs between AS and controls from the combined datasets. By evaluating immune cell proportions, macrophages showed a significant difference between the two groups, and 439 DEGs were found to be associated with macrophages. These genes were mainly enriched in the gene ontology-biological process of immune and inflammatory responses, as well as in the KEGG pathways of cytokine-cytokine receptor interaction and biosynthesis of antibiotics. Finally, 10 macrophage-related DEG signatures (EARS2, ATP2A2, COLGALT1, GART, WNT5A, AK5, ZBTB16, CCL17, ADORA3, and CXCR4) were screened as an optimized gene set to predict AS diagnosis, and they showed diagnostic abilities with AUCs of 0.968 and 0.875 in ROC curves of combined and validation datasets, respectively. The mRNA expressions of EARS2, ATP2A2, COLGALT1, and GART in the control group were higher than in AS group, while the expressions of WNT5A, AK5, ZBTB16, CCL17, ADORA3, and CXCR4 in the control group were lower than that in the AS group. Conclusion. We proposed a diagnostic model based on 10 macrophage-related genes to predict AS risk.\.
Background: The aim of two-sample Mendelian randomization (MR) with a large sample size was to explore the causal cholelithiasis impact on acute pancreatitis and pancreatic cancer. Methods: We performed the two-sample MR analysis with two models. Publicly available summary-level information for cholelithiasis was acquired from the Genome-Wide Summary Association Studies (GWAS) of FinnGen Biobank. The inverse variance weighted (IVW) method was the main method to obtain the MR estimates. Other methods were also used as supplementary methods, including MR-Egger, maximum likelihood, MR-Robust Adjusted Profile Score (MR-RAPS), weighted median, penalised weighted median method, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method. Results: After the selection of genetic instrumental variables (IVs), 11 single nucleotide polymorphisms (SNPs) (Model 1) and 22 SNPs (Model 2) were used to explore the effect of cholelithiasis on acute pancreatitis, and 10 SNPs (Model 1) and 24 SNPs (Model 2) on pancreatic cancer. The findings obtained by the fixed-effect IVW method with both Model 1 and Model 2 showed that genetically predicted cholelithiasis was significantly related to the elevated acute pancreatitis risk (Model 1: OR: 1.001, 95% CI: 1.000–1.002, P<0.001; Model 2: OR: 1.001, 95% CI: 1.000–1.002, P<0.001). Moreover, cholelithiasis would also raise the pancreatic cancer risk (Model 1: OR: 1.676, 95% CI: 1.228–2.288, P=0.001; Model 2: OR: 1.432, 95% CI: 1.116–1.839, P=0.005). Conclusion: Genetically predicted cholelithiasis was significantly related to the elevated risk of acute pancreatitis and pancreatic cancer. More attention should be paid to patients with cholelithiasis for the primary prevention of pancreatic-related diseases.
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