Gut mycobiome as a potential non-invasive tool in early detection of lung adenocarcinoma: a cross-sectional study

• Published in: BMC medicine Vol. 21; no. 1; pp. 1 - 10
• Main Authors: Liu, Qingyan, Zhang, Weidong, Pei, Yanbin, Tao, Haitao, Ma, Junxun, Li, Rong, Zhang, Fan, Wang, Lijie, Shen, Leilei, Liu, Yang, Jia, Xiaodong, Hu, Yi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 31.10.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Adenocarcinoma; Algorithms; Analysis; Antibiotics; Biodiversity; Bioinformatics; Biological diversity; Biological markers; Biomarkers; Biomedicine; Cancer therapies; Candida; Care and treatment; Composition; Confounding (Statistics); Cross-sectional studies; Data mining; Diagnosis; Early-stage diagnosis; Feces; Fungal signature; Fungi; Gender; Genera; Gut mycobiome; Health aspects; Health care; Invasiveness; Lung adenocarcinoma; Lung cancer; Lung diseases; Lungs; Machine learning; Medical diagnosis; Medical prognosis; Medicine; Medicine & Public Health; Methods; Microbiota; Microbiota (Symbiotic organisms); Non-invasive biomarker; Performance prediction; Prediction models; Research Article; Saccharomyces; Software; Somatotropin; Statistical analysis; Supervised learning; Supervised machine learning; Tumors; Veganism; China; Beijing China; United States > US; Early-stage diagnosis; Supervised machine learning; Lung adenocarcinoma; Fungal signature; Non-invasive biomarker; Gut mycobiome
• ISSN: 1741-7015; 1741-7015
• DOI: 10.1186/s12916-023-03095-z

Background The gut mycobiome of patients with lung adenocarcinoma (LUAD) remains unexplored. This study aimed to characterize the gut mycobiome in patients with LUAD and evaluate the potential of gut fungi as non-invasive biomarkers for early diagnosis. Methods In total, 299 fecal samples from Beijing, Suzhou, and Hainan were collected prospectively. Using internal transcribed spacer 2 sequencing, we profiled the gut mycobiome. Five supervised machine learning algorithms were trained on fungal signatures to build an optimized prediction model for LUAD in a discovery cohort comprising 105 patients with LUAD and 61 healthy controls (HCs) from Beijing. Validation cohorts from Beijing, Suzhou, and Hainan comprising 44, 17, and 15 patients with LUAD and 26, 19, and 12 HCs, respectively, were used to evaluate efficacy. Results Fungal biodiversity and richness increased in patients with LUAD. At the phylum level, the abundance of Ascomycota decreased, while that of Basidiomycota increased in patients with LUAD. Candida and Saccharomyces were the dominant genera, with a reduction in Candida and an increase in Saccharomyces , Aspergillus , and Apiotrichum in patients with LUAD. Nineteen operational taxonomic unit markers were selected, and excellent performance in predicting LUAD was achieved (area under the curve (AUC) = 0.9350) using a random forest model with outcomes superior to those of four other algorithms. The AUCs of the Beijing, Suzhou, and Hainan validation cohorts were 0.9538, 0.9628, and 0.8833, respectively. Conclusions For the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis.