Bacterial taxa associated with lung cancer cases in Southeast Asians: a pilot case-control study

Lung cancer is the leading cause of cancer-related mortality worldwide, yet its underlying mechanisms remain unclear. Disruptions in the respiratory microbiome may promote inflammation and carcinogenesis. This study aimed to comprehensively compare genus-level sputum microbiota between lung cancer patients and healthy controls in a multiethnic Southeast Asian population. Sputum samples were collected from lung cancer patients across three Singapore hospitals. Socio-demographic data were obtained via questionnaire. We analyzed 16S rRNA amplicon sequences from 70 lung cancer patients and 47 healthy controls from a separate local cohort, using identical sequencing protocols to minimize batch effects. Alpha- and beta-diversity metrics, random forest models, and ANCOM-BC2 were used to identify microbial features associated with lung cancer and host characteristics. Lung cancer cases showed significantly reduced genus richness compared to controls. Beta-diversity (Aitchison distance) differed by case-control status, sex, age, and smoking history. ANCOM-BC2 identified Lactobacillus as differentially enriched in cases. However, this enrichment did not pass the pseudo-count test among never-smokers. Further sex-stratified analysis revealed that the Lactobacillus enrichment was driven primarily by male cases. Conversely, [Eubacterium] nodatum group, Mogibacterium, and Campylobacter emerged as robust inverse signatures for lung cancer, with their depletion consistently supported across stratified and unstratified differential abundance analyses, random forest modeling, and Wilcoxon rank-sum tests. However, these findings may still be subjected to residual confounding by exogenous factors, such as medication use, which could not be adequately adjusted for between case-control groups. This pilot case-control study, conducted in a multiethnic Southeast Asian population, identified distinct respiratory microbiota signatures associated with lung cancer using robust differential abundance and machine-learning methods, providing preliminary evidence for a potential role of the respiratory microbiome to lung carcinogenesis, warranting validation in larger, longitudinal studies.