Previous research has identified that air pollution is associated with various respiratory diseases, but few studies have investigated the function served by particulate matter 2.5 (PM2.5) in these diseases. PM2.5 is known to cause epigenetic and microenvironmental alterations in lung cancer, including tumor‑associated signaling pathway activation mediated by microRNA dysregulation, DNA methylation, and increased levels of cytokines and inflammatory cells. Autophagy and apoptosis of tumor cells may also be detected in lung cancer associated with PM2.5 exposure. A number of mechanisms are involved in triggering and aggravating asthma and COPD, including PM2.5‑induced cytokine release and oxidative stress. The present review is an overview of the underlying molecular mechanisms of PM2.5‑induced pathogenesis in lung cancer and chronic airway inflammatory diseases.

Background: Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer‐related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis. Method: The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters. Result: A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year. Conclusion: A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

We report the clinical characteristics, viral shedding duration, and contact tracing for asymptomatic carriers of SARS-CoV-2 in Wuhan, China. The asymptomatic carriers were relatively young (median age: 34.5 years). Chest computed tomography showed no abnormalities. The nasopharyngeal swab was an optimum specimen for RNA testing. The median viral shedding duration was 11.5 days. Notably, 2 months of viral shedding duration were reported in two nurses, which was much longer than previously reported or than usually thought. The transmissibility of SARS-CoV-2 by asymptomatic carriers during the studied period in Wuhan appeared to be weak. Only one patient (1/12) was found to have transmitted the virus to another person. Early asymptomatic carrier detection, isolation, and contact tracing could be useful to mitigate the spread of the disease.

The importance of ambient particulate matter (PM2.5) in lung cancer progression is well established; however, the precise mechanisms by which PM2.5 modulates lung cancer development have not yet been determined. The present study demonstrated increased mRNA and protein expression levels of vascular endothelial growth factor in PM2.5‑induced macrophages. However, no significant changes to the expression levels of angiogenic cytokines (vascular endothelial growth factor A, matric metallopeptidase 9, basic fibroblast growth factor and platelet‑derived growth factor) were observed in the Lewis lung carcinoma (LLC) cell line in response to acute PM2.5 exposure. PM2.5‑induced activated macrophages were revealed to upregulate angiogenic cytokine expression following the acute exposure of LLC cells to PM2.5‑induced macrophage supernatant. In vivo, the pro‑angiogenic and macrophage accumulation functions of PM2.5 were supported by the establishment of a polyvinyl alcohol sponge implantation mouse model. Furthermore, PM2.5 was demonstrated to increase angiogenesis and macrophage recruitment in mice that were subcutaneously injected with LLCs. These results indicated that PM2.5 increases angiogenesis, and macrophages are crucial mediators of PM2.5‑induced angiogenesis in lung cancer. These findings may provide novel insights for the development of lung cancer treatment strategies.