Association of asthma and lung cancer risk: A pool of cohort studies and Mendelian randomization analysis.

BACKGROUND: Over the past 2 decades, population-based studies have shown an increased association between asthma and the risk of lung cancer. However, the causal links between these 2 conditions remain poorly understood. METHODS: We conducted a comprehensive search of various databases, including PubMed, Embase, Web of Science, and Cochrane Library, up until May 04, 2023. Only articles published in English were included in our study. We performed a meta-analysis using random-effects models to calculate the odds ratio (OR) and corresponding 95% confidence interval (CI). Subgroup analyses were conducted based on study design, gender, and histologic types. We also conducted a 2-sample Mendelian randomization (MR) using the genome-wide association study pooled data (408,422 people) published by the UK Biobank to explore further the potential causal relationship between asthma and lung cancer. RESULTS: Our meta-analysis reviewed 24 population-based cohort studies involving 1072,502 patients, revealing that asthma is significantly associated with an increased risk of lung cancer (OR = 1.29, 95% CI 1.19-1.38) in all individuals. Subgroup analysis showed a significantly higher risk of lung cancer in females with asthma (OR = 1.23, 95% CI 1.01-1.49). We found no significant association between asthma and lung adenocarcinoma (LUAD) (OR = 0.76, 95% CI 0.54-1.05), lung squamous carcinomas (LUSC) (OR = 1.09, 95% CI 0.79-1.50), or small-cell lung cancer (SCLC) (OR = 1.00, 95% CI 0.68-1.49). Interestingly, our MR analysis supported an increasing causality between asthma and lung cancer (OR = 1.11, 95% CI 1.04-1.17, P = .0008), specifically in those who ever smoker (OR = 1.09, 95% CI 1.01-1.16, P = .0173) and LUSC pathological type (OR = 1.15, 95% CI 1.05-1.26, P = .0038). CONCLUSION: Through meta-analysis, our study confirms that patients with asthma have a higher risk of developing lung cancer. Our MR study further support an increasing causal relationship between asthma and the risk of lung cancer, particularly in smokers and LUSC. Future studies examining the link between asthma and the risk of developing lung cancer should consider the bias of controlled and uncontrolled asthma.

Identification of signature genes for renal ischemia‒reperfusion injury based on machine learning and WGCNA.

Background: As an inevitable event after kidney transplantation, ischemia‒reperfusion injury (IRI) can lead to a decrease in kidney transplant success. The search for signature genes of renal ischemia‒reperfusion injury (RIRI) is helpful in improving the diagnosis and guiding clinical treatment. Methods: We first downloaded 3 datasets from the GEO database. Then, differentially expressed genes (DEGs) were identified and applied for functional enrichment analysis. After that, we performed three machine learning methods, including random forest (RF), Lasso regression analysis, and support vector machine recursive feature elimination (SVM-RFE), to further predict candidate genes. WGCNA was also executed to screen candidate genes from DEGs. Then, we took the intersection of candidate genes to obtain the signature genes of RIRI. Receiver operating characteristic (ROC) analysis was conducted to measure the predictive ability of the signature genes. Kaplan‒Meier analysis was used for association analysis between signature genes and graft survival. Verifying the expression of signature genes in the ischemia cell model. Results: A total of 117 DEGs were screened out. Subsequently, RF, Lasso regression analysis, SVM-RFE and WGCNA identified 17, 25, 18 and 74 candidate genes, respectively. Finally, 3 signature genes (DUSP1, FOS, JUN) were screened out through the intersection of candidate genes. ROC analysis suggested that the 3 signature genes could well diagnose and predict RIRI. Kaplan‒Meier analysis indicated that patients with low FOS or JUN expression had a longer OS than those with high FOS or JUN expression. Finally, we validated using the ischemia cell model that compared to the control group, the expression level of JUN increased under hypoxic conditions. Conclusions: Three signature genes (DUSP1, FOS, JUN) offer a good prediction for RIRI outcome and may serve as potential therapeutic targets for RIRI intervention, especially JUN. The prediction of graft survival by FOS and JUN may improve graft survival in patients with RIRI.