Postoperative recurrence remains a prevalent and lethal threat to survival in early-stage non-small cell lung cancer (NSCLC), affecting approximately 20% of patients within five years. While emerging evidence implicates the lung microbiota and microbiota-related metabolites in carcinogenesis, their prognostic value for predicting NSCLC recurrence remains underexplored. Using preoperative bronchoalveolar lavage fluid from 72 patients with stage I–II NSCLC (17 recurrence and 55 non-recurrence), we performed microbial 2bRAD-M sequencing to profile bacterial, fungal, and archaeal communities, along with untargeted metabolomics. Recurrence-associated microbial signatures were identified through machine learning approaches and network analysis. Patients with recurrence exhibited significantly reduced alpha diversity, shorter recurrence-free survival but enhanced microbial interactions compared with recurrence-free controls. We identified two predictive microbial signatures (32-species and 30-genus) that significantly outperformed conventional clinical factors, achieving mean area-under-the-curve of the receiver operating characteristic curve values of 0.92 (species-level) and 0.81 (genus-level), respectively. Notably, four potential recurrence-related drivers, namely the genera Paenibacillus, Cupriavidus, and Pseudomonas, along with the species Pseudomonas aeruginosa, were significantly increased in recurrence patients and associated with poorer survival. An integrated metabolomic analysis revealed that two microbiota-related metabolites, indole-3-lactic acid and pyridoxal, potentially mediated elevated recurrence risk through the tryptophan and vitamin B6 metabolic pathways. These findings elucidate the prognostic role of microbiota and metabolites in the lower respiratory tract of early-stage NSCLC, and highlight their dual potential as biomarkers and intervention targets.
Gu et al. (Thu,) studied this question.