Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1264467 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR33612400.fastq.gz | Fastqsanger.gz | |||
| SRR33612401.fastq.gz | Fastqsanger.gz | |||
| SRR33612402.fastq.gz | Fastqsanger.gz | |||
| SRR33612404.fastq.gz | Fastqsanger.gz |
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Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description:Comprehensive proteomic analysis of the protein expression landscape of bronchoalveolar lavage fluid during invasive pulmonary aspergillosis in murine and human samples. 38 murine BALF samples (10 Aspergillus fumigatus infected mice without immunosuppression and without invasive pulmonary aspergillosis (IPA), 19 immunosuppressed and infected mice with IPA and 9 immunosuppressed animals without infection) were analysed for their global protein expression. In addition, 54 human BALF specimen from patients with probable IPA (23 samples), proven IPA (4 cases) and 27 control samples from patients with unrelated pulmonary diseases were analysed for their global protein composition. Host responses and Aspergillus fumigatus-specific proteins detectable in BALF were studied.
2020-10-14 | PXD016664 | Pride
Project description:Abstract<br>BACKGROUND: Gene expression profiling (GEP) in cells obtained from peripheral blood has demonstrated to be a very useful approach for biomarker discovery and for studying molecular pathogenesis of prevalent diseases. While there is limited literature availble on gene expression markers associated to Chronic Obstructive Pulmonary Disease (COPD), the transcriptomic picture associated to critical respiratory illness in this disease is not known to the present moment. <br>RESULTS: By using Agilent microarray chips, we have profiled gene expression signatures in whole blood of 28 COPD patients hospitalized with distinct degree of respiratory compromise.12 of them needed of admission to the ICU, while 16 were admitted to the Respiratory Medicine Service. GeneSpring GX 11.0 software was used for performing statistical comparison of transcript levels between ICU and non ICU patients. Ingenuity pathway analysis 8.5 (IPA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed to select, annotate and visualize genes by function and pathway (gene ontology). T-test evidenced 1501 genes differentially expressed between ICU and non ICU patients. IPA and KEGG analysis of the most representative biological functions revealed that ICU patients showed increased levels of neutrohil gene transcripts, being [cathepsin G (CTSG)], [elastase, neutrophil expressed (ELANE)], [proteinase 3 (PRTN3)], [myeloperoxidase (MPO)], [cathepsin D (CTSD)], [defensin, alpha 3, neutrophil-specific (DEFA3)], azurocidin 1 (AZU1)], [bactericidal/permeability-increasing protein (BPI)] the most representative ones. Proteins codified by these genes form part of the azurophilic granules of neutrophils and are involved in both antimicrobial defence and tissue damage. This ?neutrophil signature? was paralleled by necessity of advanced respiratory and vital support, and presence of bacterial infection.<br>CONCLUSION: study of transcriptomic signatures in blood suggests a central role of neutrophil proteases in COPD patients with critical respiratory illness. Measurement / modulation of the expression of these genes could represent an option for clinical monitoring and treatment of severe COPD exacerbations. <br><br>Keywords: COPD, critical, expression, gene, microarray, neutrophil, proteases.<br><br>
2013-03-12 | E-MEXP-3589 | biostudies-arrayexpress
Project description:mNGS for BALF diagnostics in patients with lower respiratory tract infections
| PRJEB54732 | ENA