Epithelium-derived cystatin SN inhibits house dust mite protease activity in allergic asthma.

BACKGROUND: Allergen source-derived proteases are a critical factor in the formation and development of asthma. The cysteine protease activity of house dust mite (HDM) disrupts the epithelial barrier function. The expression of cystatin SN (CST1) is elevated in asthma epithelium. CST1 inhibits the cysteine protease activity. We aimed to elucidate the role of epithelium-derived CST1 in the development of asthma caused by HDM. METHODS: CST1 protein levels in sputum supernatants and serum of patients with asthma and healthy volunteers were measured by ELISA. The ability of CST1 protein to suppress HDM-induced bronchial epithelial barrier function was examined in vitro. The effects of exogenous CST1 protein on abrogating HDM-induced epithelial barrier function and inflammation were examined in mice in vivo. RESULTS: CST1 protein levels were higher in sputum supernatants (142.4 ± 8.95 vs 38.87 ± 6.85 ng/mL, P < 0.0001) and serum (1129 ± 73.82 vs 703.1 ± 57.02 pg/mL, P = 0.0035) in patients with asthma than in healthy subjects. The levels were significantly higher in patients with not well- and very poorly controlled asthma than those with well-controlled asthma. Sputum and serum CST1 protein levels were negatively correlated with lung function in asthma. CST1 protein levels were significantly lower in the serum of HDM-specific IgE (sIgE)-positive asthmatics than in sIgE-negative asthmatics. The HDM-induced epithelial barrier function disruption was suppressed by recombinant human CST1 protein (rhCST1) in vitro and in vivo. CONCLUSION: Our data indicated that human CST1 protein suppresses asthma symptoms by protecting the asthmatic bronchial epithelial barrier through inhibiting allergenic protease activity. CST1 protein may serve as a potential biomarker for asthma control.

Global analysis of iron metabolism-related genes identifies potential mechanisms of gliomagenesis and reveals novel targets.

AIMS: This study aimed to investigate key regulators of aberrant iron metabolism in gliomas, and evaluate their effect on biological functions and clinical translational relevance. METHODS: We used transcriptomic data from multiple cross-platform glioma cohorts to identify key iron metabolism-related genes (IMRGs) based on a series of bioinformatic and machine learning methods. The associations between IMRGs and prognosis, mesenchymal phenotype, and genomic alterations were analyzed in silico. The performance of the IMRGs-based signature in predicting temozolomide (TMZ) treatment sensitivity was evaluated. In vitro and in vivo experiments were used to explore the biological functions of these key IMRGs. RESULTS: HMOX1, LTF, and STEAP3 were identified as the most essential IMRGs in gliomas. The expression levels of these genes were strongly related to clinicopathological and molecular features. The robust IMRG-based gene signature could be used for prognosis prediction. These genes facilitate mesenchymal transformation, driver gene mutations, and oncogenic alterations in gliomas. The gene signature was also associated with TMZ resistance. HMOX1, LTF, and STEAP3 knockdown in glioma cells significantly reduced cell proliferation, colony formation, migration, and malignant invasion. CONCLUSION: The study presented a comprehensive view of key regulators underpinning iron metabolism in gliomas and provided new insights into novel therapeutic approaches.

A Diagnostic Classifier Based on Circulating miRNA Pairs for COPD Using a Machine Learning Approach.

Chronic obstructive pulmonary disease (COPD) is highly underdiagnosed, and early detection is urgent to prevent advanced progression. Circulating microRNAs (miRNAs) have been diagnostic candidates for multiple diseases. However, their diagnostic value has not yet been fully established in COPD. The purpose of this study was to develop an effective model for the diagnosis of COPD based on circulating miRNAs. We included circulating miRNA expression profiles of two independent cohorts consisting of 63 COPD and 110 normal samples, and then we constructed a miRNA pair-based matrix. Diagnostic models were developed using several machine learning algorithms. The predictive performance of the optimal model was validated in our external cohort. In this study, the diagnostic values of miRNAs based on the expression levels were unsatisfactory. We identified five key miRNA pairs and further developed seven machine learning models. The classifier based on LightGBM was selected as the final model with the area under the curve (AUC) values of 0.883 and 0.794 in test and validation datasets, respectively. We also built a web tool to assist diagnosis for clinicians. Enriched signaling pathways indicated the potential biological functions of the model. Collectively, we developed a robust machine learning model based on circulating miRNAs for COPD screening.

Langerin-expressing dendritic cells in pulmonary immune-related diseases.

Dendritic cells (DCs) are "frontline" immune cells dedicated to antigen presentation. They serve as an important bridge connecting innate and adaptive immunity, and express various receptors for antigen capture. DCs are divided into various subclasses according to their differential expression of cell surface receptors and different subclasses of DCs exhibit specific immunological characteristics. Exploring the common features of each sub-category has became the focus of many studies. There are certain amounts of DCs expressing langerin in airways and peripheral lungs while the precise mechanism by which langerin+ DCs drive pulmonary disease is unclear. Langerin-expressing DCs can be further subdivided into numerous subtypes based on the co-expressed receptors, but here, we identify commonalities across these subtypes that point to the major role of langerin. Better understanding is required to clarify key disease pathways and determine potential new therapeutic approaches.

Molecular and Clinicopathological Characterization of a Prognostic Immune Gene Signature Associated With MGMT Methylation in Glioblastoma.

Background: O6-methylguanine-DNA methyltransferase (MGMT) methylation status affects tumor chemo-resistance and the prognosis of glioblastoma (GBM) patients. We aimed to investigate the role of MGMT methylation in the regulation of GBM immunophenotype and discover an effective biomarker to improve prognosis prediction of GBM patients. Methods: A total of 769 GBM patients with clinical information from five independent cohorts were enrolled in the present study. Samples from the Cancer Genome Atlas (TCGA) dataset were used as the training set, whereas transcriptome data from the Chinese Glioma Genome Atlas (CGGA) RNA-seq, CGGA microarray, GSE16011, and the Repository for Molecular Brain Neoplasia (REMBRANDT) cohort were used for validation. A series of bioinformatics approaches were carried out to construct a prognostic signature based on immune-related genes, which were tightly related to the MGMT methylation status. In silico analyses were performed to investigate the influence of the signature on immunosuppression and remodeling of the tumor microenvironment. Then, the utility of this immune gene signature was analyzed by the development and evaluation of a nomogram. In vitro experiments were further used to verify the immunologic function of the genes in the signature. Results: We found that MGMT unmethylation was closely associated with immune-related biological processes in GBM. Sixty-five immune genes were more highly expressed in the MGMT unmethylated than the MGMT-methylated group. An immune gene-based risk model was further established to divide patients into high and low-risk groups, and the prognostic value of this signature was validated in several GBM cohorts. Functional analyses manifested a universal up-regulation of immune-related pathways in the high-risk group. Furthermore, the risk score was highly correlated to the immune cell infiltration, immunosuppression, inflammatory activities, as well as the expression levels of immune checkpoints. A nomogram was developed for clinical application. Knockdown of the five genes in the signature remodeled the immunosuppressive microenvironment by restraining M2 macrophage polarization and suppressing immunosuppressive cytokines production. Conclusions: MGMT methylation is strongly related to the immune responses in GBM. The immune gene-based signature we identified may have potential implications in predicting the prognosis of GBM patients and mechanisms underlying the role of MGMT methylation.

Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling.

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

Effects of long-term macrolide therapy at low doses in stable COPD.

Background: Chronic obstructive pulmonary disease (COPD) is currently the fourth largest fatal disease in the world, and is expected to rise to third place by 2020. Frequent acute exacerbations lead to increased mortality. Some suggestions for prophylactic use of macrolides in preventing COPD exacerbations have been raised, but there are still several issues that need to be addressed, such as target population, the course of treatment, therapeutic dose, and so on. Objective: To evaluate, via exploratory meta-analysis, the efficacy of long-term macrolide therapy at low doses in stable COPD. Methods: A systematic literature search was performed in PubMed, Embase, and Cochrane database from inception to March 28, 2019. Randomized controlled trials (RCT) which reported long-term use of macrolides in prevention of COPD were eligible. Results: A total of 10 articles were included in this study. It was found that there was a 23% relative risk reduction in COPD exacerbations among patients taking macrolides compared to placebo (P<0.01). The median time to first exacerbation was effectively prolonged among patients taking macrolides vs placebo (P<0.01). Sub-group analysis showed erythromycin was advantageous and older patients were less responsive to macrolides. Conclusions: Long-term low dose usage of macrolides could significantly reduce the frequency of the acute exacerbation of COPD. The treatment was well tolerated, with few adverse reactions, but it was not suitable for the elderly. It is recommended that this treatment regimen could be used in patients with GOLD grading C or D, because they have a higher risk of acute exacerbation and mortality. It needs to be further discussed whether this treatment should last for 12 months or longer.