Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1075918 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27950949.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.
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Project description:To clarify the profile of in BALF exosome collected from mice infected with influenza virus, we infected 100000 pfu of A/Puerto Rico/8/1934 (PR8) strain. BALF was collected at 24, 48, and 72 hour post infection (hpi). For comparison of the profile of the miRNA in BALF exosome induced by innate immune response, we also intranasally inoculated mice with 50 μg of poly(I:C) and collected BALF at 72 hour post inoculation. We found that some miRNAs were common to both influenza virus infectiona and poly(I:C) inoculation, suggesting that exosomal miRNAs in BALF may function in the innate immune response to virus infection.
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