Bacterial interactome disturbance in chronic obstructive pulmonary disease clinical stability and exacerbations.

RATIONALE: Our understanding of airway dysbiosis in chronic obstructive pulmonary disease (COPD) remains incomplete, which may be improved by unraveling the complexity in microbial interactome. OBJECTIVES: To characterize reproducible features of airway bacterial interactome in COPD at clinical stability and during exacerbation, and evaluate their associations with disease phenotypes. METHODS: We performed weighted ensemble-based co-occurrence network analysis of 1742 sputum microbiomes from published and new microbiome datasets, comprising two case-control studies of stable COPD versus healthy control, two studies of COPD stability versus exacerbation, and one study with exacerbation-recovery time series data. RESULTS: Patients with COPD had reproducibly lower degree of negative bacterial interactions, i.e. total number of negative interactions as a proportion of total interactions, in their airway microbiome compared with healthy controls. Evaluation of the Haemophilus interactome showed that the antagonistic interaction networks of this established pathogen rather than its abundance consistently changed in COPD. Interactome dynamic analysis revealed reproducibly reduced antagonistic interactions but not diversity loss during COPD exacerbation, which recovered after treatment. In phenotypic analysis, unsupervised network clustering showed that loss of antagonistic interactions was associated with worse clinical symptoms (dyspnea), poorer lung function, exaggerated neutrophilic inflammation, and higher exacerbation risk. Furthermore, the frequent exacerbators (≥ 2 exacerbations per year) had significantly reduced antagonistic bacterial interactions while exhibiting subtle compositional changes in their airway microbiota. CONCLUSIONS: Bacterial interactome disturbance characterized by reduced antagonistic interactions, rather than change in pathogen abundance or diversity, is a reproducible feature of airway dysbiosis in COPD clinical stability and exacerbations, which suggests that we may target interactome rather than pathogen alone for disease treatment.

[Progress of researches on anti-inflammatory mechanism of acupuncture underlying amelioration of chronic respiratory diseases].

In recent years, acupuncture has gained great progress in the treatment of chronic respiratory diseases (CRD), and the clinical effect is remarkable, but its underlying mechanisms are relatively complex, with the anti-inflammatory effect being the primary aspect. Based on the literature both at home and abroad, we found that the anti-inflammatory mechanism of acupuncture mainly involves chemokines, kinase-related pathways, helper T cells, epigenetic modification, autophagy, vagal-mediated cholinergic anti-inflammatory pathway, etc. The researches on some anti-inflammatory mechanisms are still in the initial stage, the relationship among various pathways, and the key factors affecting the effect of acupuncture, such as acupoint selection, stimulation intensity and needling depth, etc. warrant further exploration in the future.

Signature based on RNA-binding protein-related genes for predicting prognosis and guiding therapy in non-small cell lung cancer.

Background: Studies have reported that RNA-binding proteins (RBPs) are dysregulated in multiple cancers and are correlated with the progression and prognosis of disease. However, the functions of RBPs in non-small cell lung cancer (NSCLC) remain unclear. The present study aimed to explore the function of RBPs in NSCLC and their prognostic and therapeutic value. Methods: The mRNA expression profiles, DNA methylation data, gene mutation data, copy number variation data, and corresponding clinical information on NSCLC were downloaded from The Cancer Genome Atlas, Gene Expression Omnibus, and the University of California Santa Cruz Xena databases. The differentially expressed RBPs were identified between tumor and control tissues, and the expression and prognostic value of these RBPs were systemically investigated by bioinformatics analysis. A quantitative polymerase chain reaction (qPCR) was performed to validate the dysregulated genes in the prognostic signature. Results: A prognostic RBP-related signature was successfully constructed based on eight RBPs represented as a risk score using least absolute shrinkage and selection operator (LASSO) regression analysis. The high-risk group had a worse overall survival (OS) probability than the low-risk group (p < 0.001) with 1-, 3-, and 5-year area under the receiver operator characteristic curve values of 0.671, 0.638, and 0.637, respectively. The risk score was associated with the stage of disease (p < 0.05) and was an independent prognostic factor for NSCLC when adjusted for age and UICC stage (p < 0.001, hazard ratio (HR): 1.888). The constructed nomogram showed a good predictive value. The P53, focal adhesion, and NOD-like receptor signaling pathways were the primary pathways in the high-risk group (adjusted p value <0.05). The high-risk group was correlated with increased immune infiltration (p < 0.05), upregulated relative expression levels of programmed cell death 1 (PD1) (p = 0.015), cytotoxic T-lymphocyte-associated protein 4 (CTLA4) (p = 0.042), higher gene mutation frequency, higher tumor mutational burden (p = 0.034), and better chemotherapy response (p < 0.001). The signature was successfully validated using the GSE26939, GSE31210, GSE30219, and GSE157009 datasets. Dysregulation of these genes in patients with NSCLC was confirmed using the qPCR in an independent cohort (p < 0.05). Conclusion: An RBP-related signature was successfully constructed to predict prognosis in NSCLC, functioning as a reference for individualized therapy, including immunotherapy and chemotherapy.

Diagnostic performance of mycological tests for invasive pulmonary aspergillosis in non-haematological patients: protocol for a systematic review and meta-analysis.

INTRODUCTION: Increasing numbers of patients with non-haematological diseases are infected with invasive pulmonary aspergillosis (IPA), with a high mortality reported which is mainly due to delayed diagnosis. The diagnostic capability of mycological tests for IPA including galactomannan test, (1,3)-ß-D-glucan test, lateral flow assay, lateral flow device and PCR for the non-haematological patients remains unknown. This protocol aims to conduct a systematic review and meta-analysis of the diagnostic performance of mycological tests to facilitate the early diagnosis and treatments of IPA in non-haematological diseases. METHODS AND ANALYSIS: Database including PubMed, CENTRAL and EMBASE will be searched from 2002 until the publication of results. Cohort or cross-sectional studies that assessing the diagnostic capability of mycological tests for IPA in patients with non-haematological diseases will be included. The true-positive, false-positive, true-negative and false-negative of each test will be extracted and pooled in bivariate random-effects model, by which the sensitivity and specificity will be calculated with 95% CI. The second outcomes will include positive (negative) likelihood ratio, area under the receiver operating characteristic curve and diagnostic OR will also be computed in the bivariate model. When applicable, subgroup analysis will be performed with several prespecified covariates to explore potential sources of heterogeneity. Factors that may impact the diagnostic effects of mycological tests will be examined by sensitivity analysis. The risk of bias will be appraised by the Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS-2). ETHICS AND DISSEMINATION: This protocol is not involved with ethics approval, and the results will be peer-reviewed and disseminated on a recognised journal. PROSPERO REGISTRATION NUMBER: CRD42021241820.

Identification of Survival-Associated Gene Signature in Lung Cancer Coexisting With COPD.

Background: Chronic obstructive pulmonary disease (COPD) and lung cancer often coexist, which is associated with a worse prognosis. Thousands of biomarkers related to the survival of lung cancer have been investigated. However, those which can predict the survival of lung cancer coexisting with COPD are currently lacking. The present study aimed to identify novel gene signatures to predict the survival of patients with lung cancer coexisting COPD. Method: RNA-sequence data of lung cancer and control accompanying with matched clinical information were retrieved from the Cancer Genome Atlas (TCGA). Differently expressed genes (DEGs) associated with lung cancer coexisting COPD were screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. Univariate and multivariate Cox regression analyses were applied to identify survival-associated DEGs and to construct survival-associated gene signature. Kaplan-Meier survival analysis and calibration plots of the nomogram were performed to test the predictive accuracy of the gene signature. qPCR was performed to validate the genes in the prognostic signature. Results: Sequence data from 70 patients with lung cancer coexisting COPD, 127 with lung cancer alone and 108 control tissues were included for analysis. A total of 2424 DEGs were identified when comparing lung cancer coexisting COPD with controls. The biological process was primarily associated with DNA-binding transcription activator activity, peptidase inhibitor activity, endopeptidase inhibitor activity, et al. KEGG pathways were mainly enriched in neuroactive ligand-receptor interaction, cell cycle, and Staphylococcus aureus infection. A survival-associated gene signature consisting of CEACAM5, RASAL1, CSTL1, CNGB1, and SLC4A3 was identified and represented as risk score. The high-risk score group had significantly worse survival than the low-risk score group (P < 0.001). Areas under receiver operating characteristic curves were 0.943, 0.773, 0.888 for predicting overall survival at 1-, 3-, and 5-year, respectively. The risk score was an independent predictor of survival, independent of clinical factors. High conformity of the actual survival and the nomogram-predicted probability of survival by applying the risk score. Upregulation of the five genes in patients with lung cancer coexisting COPD were confirmed by qPCR in an independent cohort. Conclusion: Our study constructed and validated a novel prognostic gene signature for predicting survival of patient with lung cancer coexisting COPD, which may contribute to the clinical treatment decisions.

High expression of SPP1 in patients with chronic obstructive pulmonary disease (COPD) is correlated with increased risk of lung cancer.

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airway inflammation and fixed airflow obstruction. Patients with COPD have increased risk of lung cancer (LC), and the coexistence of both diseases is associated with poorer survival. However, the mechanisms predisposing patients with COPD to LC development and poor prognosis remain unclear. Gene expression profiles were downloaded from the Gene Expression Omnibus. Twenty-two data sets were included (n = 876). We identified 133 DEGs and 145 DEGs in patients with COPD and LC compared with healthy controls, respectively. There were 1544 DEGs in patients with LC and coexisting COPD compared with COPD, and these DEGs are mainly involved in the cell cycle, DNA replication, p53 signalling and insulin signalling. The biological processes primarily associated with these DEGs are oxidation reduction and apoptosis. SPP1 was the only overlapping DEG that was up-regulated in patients with COPD and/or LC, and this was validated by qPCR in an independent cohort. The area under the curve value for SPP1 was 0.893 (0.822-0.963) for the prediction of LC in patients with COPD. High expression of SPP1 in patients with LC was associated with shorter survival time. Up-regulation of SPP1 may be associated with increased risk of LC in patients with COPD and therefore may have potential as a therapeutic target for LC in patients with COPD.

Endotype-driven prediction of acute exacerbations in chronic obstructive pulmonary disease (EndAECOPD): protocol for a prospective cohort study.

INTRODUCTION: Current strategies for the prevention of acute exacerbations in chronic obstructive pulmonary disease (COPD) are primarily based on clinical measurements but fail to target the pathophysiological mechanisms, namely endotypes, of the disease. Studies identifying endotypes underlying exacerbation susceptibility and discovering specific biomarkers may lead to the development of targeted therapeutics but are lacking. This study aims to assess a broad spectrum of biomarkers at multiple biological levels (genetics, airway inflammation and respiratory microbiome) for their ability in predicting acute exacerbations of COPD, thus enables high-resolution disease endotyping and may lead to precision treatment of the disease. METHODS AND ANALYSIS: In this prospective cohort study, participants with stable COPD (n=600) will be recruited and assessed for demographics, symptom scores, spirometry, medication use and comorbidities at baseline. Blood will be obtained for genotyping variants in a panel of nine genes. Induced sputum will be collected for the profile of microbiota using 16S rRNA gene sequencing, quantification of bacterial load, inflammatory mediators assay and sputum cytometry. Participants will be followed up for their exacerbations till 12 months and reassessed for the clinical measurements as baseline. The primary outcomes are total number of exacerbations, severe exacerbations, moderate exacerbations and time to first exacerbation. The secondary outcomes are changes in lung function and symptom scores. The effect of biomarkers representing genetic variants, airway inflammation and respiratory microbiome on predicting the frequent exacerbator phenotype and exacerbation frequency will be analysed with multivariable modelling, and time to first exacerbation with a Cox regression model. ETHICS AND DISSEMINATION: The study has been approved by the Clinical Trial and Biomedical Ethics Committee of West China Hospital of Sichuan University (No. 2018-298). The results of the study will be published on peer-reviewed journals. TRIAL REGISTRATION NUMBER: ChiCTR1800019063.