Risk of severe pulmonary embolism in cancer patients receiving bevacizumab: Results from a meta-analysis of published and unpublished data.

To evaluate the association between severe pulmonary embolism events and bevacizumab, we conducted the first meta-analysis evaluating the incidence and risk of pulmonary embolism associated with bevacizumab-based therapy. We searched PubMed, EMBASE, Cochrane Library, and ClinicalTrials.gov up to September 2016 for randomized controlled trials comparing bevacizumab with no bevacizumab on cancer patients. Incidence rates, relative risks, and 95% confidence intervals were calculated using fixed- or random-effects models. The primary end point was the association of bevacizumab with pulmonary embolism. Subgroup analyses were performed according to tumor type, dose, and publication status. In total, 23 randomized controlled trials were included. For patients receiving bevacizumab, the overall incidence of severe pulmonary embolism events was 1.76% (95% confidence interval = 1.25%-2.27%). Cancer patients treated with bevacizumab did not increase the risk of pulmonary embolism events (relative risk = 1.00, 95% confidence interval = 0.80-1.25). No significant differences in pulmonary embolism incidence or risk among subgroup analyses were observed. No evidence of publication bias was observed. This study suggested that bevacizumab may not increase the risk of pulmonary embolism in cancer patients.

A comprehensive analysis of genes associated with hypoxia and cuproptosis in pulmonary arterial hypertension using machine learning methods and immune infiltration analysis: AHR is a key gene in the cuproptosis process.

Background: Pulmonary arterial hypertension (PAH) is a serious condition characterized by elevated pulmonary artery pressure, leading to right heart failure and increased mortality. This study investigates the link between PAH and genes associated with hypoxia and cuproptosis. Methods: We utilized expression profiles and single-cell RNA-seq data of PAH from the GEO database and genecad. Genes related to cuproptosis and hypoxia were identified. After normalizing the data, differential gene expression was analyzed between PAH and control groups. We performed clustering analyses on cuproptosis-related genes and constructed a weighted gene co-expression network (WGCNA) to identify key genes linked to cuproptosis subtype scores. KEGG, GO, and DO enrichment analyses were conducted for hypoxia-related genes, and a protein-protein interaction (PPI) network was created using STRING. Immune cell composition differences were examined between groups. SingleR and Seurat were used for scRNA-seq data analysis, with PCA and t-SNE for dimensionality reduction. We analyzed hub gene expression across single-cell clusters and built a diagnostic model using LASSO and random forest, optimizing parameters with 10-fold cross-validation. A total of 113 combinations of 12 machine learning algorithms were employed to evaluate model accuracy. GSEA was utilized for pathway enrichment analysis of AHR and FAS, and a Nomogram was created to assess risk impact. We also analyzed the correlation between key genes and immune cell types using Spearman correlation. Results: We identified several diagnostic genes for PAH linked to hypoxia and cuproptosis. PPI networks illustrated relationships among these hub genes, with immune infiltration analysis highlighting associations with monocytes, macrophages, and CD8 T cells. The genes AHR, FAS, and FGF2 emerged as key markers, forming a robust diagnostic model (NaiveBayes) with an AUC of 0.9. Conclusion: AHR, FAS, and FGF2 were identified as potential biomarkers for PAH, influencing cell proliferation and inflammatory responses, thereby offering new insights for PAH prevention and treatment.

A highly significant association between Cathepsin S gene polymorphisms rs12068264 and chronic obstructive pulmonary disease susceptibility in Han Chinese population.

Cathepsin S (CTSS) and Sirtuin-1 (SIRT1) played crucial roles in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the associations between the polymorphisms of CTSS as well as SIRT1 and COPD in Asian population remain elusive. In the present study, one single nucleotide polymorphism (SNP) in rs12068264 was discovered (in 385 individuals) to be associated with the susceptibility of COPD in a Chinese Han population. The genotyping was performed using improved multiplex ligase detection reaction (iMLDR) technique. Subjects with T allele of rs12068264 in CTSS gene had an increased risk of COPD (T compared with C: odds ratio (OR) = 1.351, 95% confidence interval (95% CI): 1.008-1.811, P=0.044) compared with C allele. Subjects with TT genotype at rs12068264 had a higher risk of COPD in a recessive model (TT compared with TC + CC: OR = 2.30, 95% CI: 1.06-4.989, P=0.035). Compared with the C variant of rs12068264, the homozygous carriers of the TT genotype had higher procalcitonin (PCT) levels. Finally, haplotype analysis demonstrated that the SNPs in the CTSS and SIRT1 gene had no statistical differences between patients with COPD and the controls. In conclusion, the genetic polymorphisms of CTSS were associated with the susceptibility of COPD in a Chinese Han population, which may be helpful in understanding genetic mechanisms underlying the pathogenesis of COPD.