This study aimed to obtain further in-depth information on the value of metagenomic next-generation sequencing (mNGS) for diagnosing pulmonary aspergillosis in non-neutropenic patients. We did a retrospective study, in which 33 non-neutropenic patients were included, of which 12 were patients with pulmonary aspergillosis and 21 were diagnosed with non-pulmonary aspergillosis. Fungi and all other co-pathogens in bronchoalveolar lavage fluid (BALF) (27 cases), blood (6 cases), and/or pleural fluid (1 case) samples were analyzed using mNGS. One of the patients submitted both BALF and blood samples. We analyzed the clinical characteristics, laboratory tests, and radiologic features of pulmonary aspergillosis patients and compared the diagnostic accuracy, including sensitivity, specificity, positive predictive value, and negative predictive value of mNGS with conventional etiological methods and serum (1,3)-β-D-glucan. We also explored the efficacy of mNGS in detecting mixed infections and co-pathogens. We further reviewed modifications of antimicrobial therapy for patients with pulmonary aspergillosis according to the mNGS results. Finally, we compared the detection of Aspergillus in BALF and blood samples from three patients using mNGS. In non-neutropenic patients, immunocompromised conditions of non-pulmonary aspergillosis were far less prevalent than in patients with pulmonary aspergillosis. More patients with pulmonary aspergillosis received long-term systemic corticosteroids (50% vs. 14.3%, p < 0.05). Additionally, mNGS managed to reach a sensitivity of 91.7% for diagnosing pulmonary aspergillosis, which was significantly higher than that of conventional etiological methods (33.3%) and serum (1,3)-β-D-glucan (33.3%). In addition, mNGS showed superior performance in discovering co-pathogens (84.6%) of pulmonary aspergillosis; bacteria, bacteria-fungi, and bacteria-PJP-virus were most commonly observed in non-neutropenic patients. Moreover, mNGS results can help guide effective treatments. According to the mNGS results, antimicrobial therapy was altered in 91.7% of patients with pulmonary aspergillosis. The diagnosis of Aspergillus detected in blood samples, which can be used as a supplement to BALF samples, seemed to show a higher specificity than that in BALF samples. mNGS is a useful and effective method for the diagnosis of pulmonary aspergillosis in non-neutropenic patients, detection of co-pathogens, and adjustment of antimicrobial treatment.

There is a known link between DNA methylation and cancer immunity/immunotherapy; however, the effect of DNA methylation on immunotherapy in lung adenocarcinoma (LUAD) remains to be elucidated. In the current study, we aimed to screen key markers for prognostic analysis of LUAD based on DNA methylation regulatory factor clustering. We classified LUAD using the NMF clustering method, and as a result, we obtained 20 DNA methylation regulatory genes. These 20 regulatory genes were used to determine the pattern of DNA methylation regulation, and patients were grouped for further analysis. The risk score model was analyzed in the TCGA dataset and an external validation set, and the correlation between the risk score and DNA methylation regulatory gene expression was explored. We analyzed the correlation between the prognostic model and immune infiltration and checkpoints. Finally, we analyzed the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functions of the prognosis model and established the nomogram model and decision tree model. The survival analyses of ClusterA and ClusterB were significantly different. Survival analysis showed that patients with a high risk score had a poor prognosis. Survival models (tobacco, T, N, M, stage, sex, age, status, and risk score) were abnormally correlated with T cells and macrophages. The higher the risk score associated with smoking was and the higher the stage was, the more severe the LUAD and the more maladjusted the immune system were. Immune infiltration and abnormal expression of immune checkpoint genes in the prognostic model of LUAD were associated with the risk score. The prognostic models were mainly enriched in the cell cycle and DNA replication. Characterization of DNA methylation regulatory patterns is helpful to improve our understanding of the immune microenvironment in LUAD and to guide the development of a more personalized immunotherapy strategy in the future.

The diagnosis of pulmonary aspergillosis is a critical step in initiating prompt treatment and improving patients’ prognosis. Currently, microbiological analysis of pulmonary aspergillosis involves fungal smear and culture, serum (1,3)-β-D-glucan (G) or galactomannan (GM) tests, and polymerase chain reaction (PCR). However, these methods have limitations. Recent studies have demonstrated that polymorphisms in pentraxin3 (PTX3), a soluble pattern recognition receptor, are associated with increased susceptibility to invasive aspergillosis. mNGS, a new microbial diagnostic method, has emerged as a promising alternative. It has high sensitivity in identifying pulmonary aspergillosis and can accurately distinguish species. Additionally, it outperforms other methods in detecting mixed infections and instructing the adjustment of antimicrobial treatments. As a result, mNGS has the potential to be adopted as the gold standard for the diagnosis of pulmonary aspergillosis.