In a recent study published in Scientific Reports, researchers comparatively investigated the lung microbiomes of patients with benign and malignant pulmonary diseases.
Study: Prediction of lung cancer using novel biomarkers based on microbiome profiling of bronchoalveolar lavage fluid. Image Credit: SewCreamStudio/Shutterstock.com
Background
Lung cancer is a prevalent disease causing significant global deaths, and early detection is crucial for improving prognosis. Lung biopsies are essential for diagnosis and treatment but are invasive and can cause severe complications.
Computed tomography (CT) scans cannot distinguish between pneumonia-like consolidation and lung cancers with necrosis. Blood biomarkers like carcinoembryonic antigen (CEA) and cytokeratin 19 are used but haven't been validated, warranting novel biomarkers for lung cancer diagnosis and biopsy decision criteria.
About the study
In the present study, researchers obtained bronchoalveolar lavage fluid (BALF) from individuals with pulmonary cancer or other pulmonary conditions, including pneumonia, bronchiectasis, and interstitial lung disease, to identify microbial differences between lung cancers and benign diseases of the lungs. They also established a lung cancer prediction model.
The team included 24 lung cancer patients and 24 individuals with benign pulmonary conditions undergoing bronchoscopy at the Chungnam National University Hospital from June 2021 to June 2022 for analysis.
They obtained BALF from the lung lesions of the participants. In particular, the team collected 3.0 mL of BALF from every patient, centrifuged them at 4.0°celsius for 30 minutes, added 1.0 mL of ribonucleic acid (RNA)/deoxyribonucleic acid (DNA) shield to the samples, and stored them at −80 °C in microcentrifuge tubes.
The researchers extracted DNA from BALF sampled from the participants to perform a polymerase chain reaction (PCR). To evaluate the association of pulmonary microbiomes with pulmonary cancer, they subjected the samples to 16S ribosomal RNA (rRNA) sequencing and metagenomics analysis.
They defined pneumonia using the radiographical and clinical findings reported by pulmonologists. The team included pneumonia patmalignancy and bronchoscopy due to mass-like consolidations, requiring differentiation from malignancy, and excluding those with prior exposure to glucocorticoids and broad-spectrum antimicrobials.
The team profiled taxonomic data at phylum and genus levels and compared the microbial communities of pneumonia and pulmonary cancer patients.
The analysis of the composition of microbiomes (ANCOM) investigated the differential abundance of pulmonary cancer-associated microbes.
They performed random forest modeling to predict lung cancer. They ran the training with 33 patients and tested the model on 15 patients with ten cross-validations.
Results
The study found significant differences in alpha and beta microbial diversities between individuals with benign pulmonary conditions and pulmonary cancer. Lung cancer patients showed the highest Firmicutes abundance (33%), while Bacteroidetes were most abundant in benign-type pulmonary conditions (31%).
The BALF microbiomes in lung cancer showed high alpha diversity, with higher evenness observed in the Shannon index and features in pulmonary cancer samples compared to benign pulmonary conditions.
The Firmicutes/Bacteroidetes (F/B) ratio was higher in pulmonary cancer patients than in individuals with benign-type pulmonary conditions. All participants contained Actinobacteria, Fusobacteria, and Proteobacteria in their lungs.
The most differentially abundant microbiota taxon was unclassified_SAR202_clade, belonging to the Chloroflexi phylum.
The model accurately distinguished individuals with benign pulmonary conditions from those with pulmonary cancer [micro-area under the receiver-operating characteristic curve (AUC) of 0.98 and macro-AUC of 0.99], indicating the BALF microbiome may be a novel biomarker for lung cancer detection.
The mean participant age was 66 years, with 77% being male. The mean body mass index (BMI) was 22 kg/m2. Most patients had stage III or IV lung cancer. Histochemical subtypes included adenocarcinoma (29%), squamous cell carcinoma (54%), and small cell carcinoma (17%).
Among the patients, 29% showed increased programmed cell death ligand 1 (PD-L1) expression, while 58% showed negligible PD-L1 expression.
Most individuals with benign pulmonary conditions had pneumonia (46%), primarily of the low-severity, community-acquired type, treated in outpatient clinics.
Prevotella_7 abundance was the highest in benign pulmonary disease patients (15%), while Streptococcus abundance was the highest in pulmonary cancer patients (13%). Streptococcus contributed the most to beta diversity among individuals with benign pulmonary conditions and pulmonary cancer.
The team observed the least alpha diversity in bronchoalveolar lavage fluid sampled from pneumonia patients than those with other benign-type pulmonary conditions and pulmonary cancer.
The SAR202 genetic clade belonging to the Chloroflexi phylum was the most abundant in pulmonary cancer patients, with elevated counts of most microbiota, including Chloroflexus, Sva0996_marine group, and Dadabacteriales, compared to individuals with benign pulmonary diseases.
Conclusion
Overall, the study findings showed higher microbial diversity in microbiomes of pulmonary cancer patients than in individuals with benign pulmonary conditions.
Firmicutes were the most enriched bacterial species in pulmonary cancer patients, while individuals with benign pulmonary diseases showed the highest Bacteroidetes abundance.
The SAR202 genetic clade from the Chloroflexi phylum was significantly higher in pulmonary cancer patients.
Machine learning prediction using BALF microbiome characteristics differentiated lung cancer from benign diseases, indicating that the BALF microbiome may be a novel biomarker for lung cancer detection.
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