ArticleBMC medicine2023
Gut mycobiome as a potential non-invasive tool in early detection of lung adenocarcinoma: a cross-sectional study.
Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Predicting cancer-related mycobiome aspects in gastrointestinal cancers: a systematic review.Frontiers in medicine · 2024Pooled it
- Tracking fungal pathogens on cancer: Oncomicrobes or opportunistic bystanders?PLoS pathogens · 2026Article
- The gut mycobiome and inter-kingdom microbial networks are linked to COPD severity in lung cancer patients.Scientific reports · 2026Article
- [Role of Gut Mycobiome in Lung Cancer Immunotherapy: Mechanisms, Challenges and Translational Prospects].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Characteristics of the gut mycobiome in patients with membranous nephropathy.Scientific reports · 2026Article
- The Dynamic Gut Microbiota: Monitoring Alterations During Lung Cancer Progression for Diagnosis and Precision Medicine.International journal of molecular sciences · 2026Review
- Profiling of the mycobiome and metabolome: a comparative study of benign pulmonary nodules and lung adenocarcinoma.Frontiers in cellular and infection microbiology · 2026Article
- Gut-Lung Microbiota Axis Shapes the Immune Microenvironment and Immunotherapeutic Response in Lung Cancer.International journal of biological sciences · 2026Review
- The role of gut mycobiome in responses to cancer immunotherapy.Gut microbes · 2025Review
- The Human Mycobiome: Composition, Immune Interactions, and Impact on Disease.International journal of molecular sciences · 2025Review
- Mycobiome: an underexplored kingdom in cancer.Microbiology and molecular biology reviews : MMBR · 2025Review
- The Gut Mycobiome for Precision Medicine.Journal of fungi (Basel, Switzerland) · 2025Review
- Exploring non-invasive biomarkers for pulmonary nodule detection based on salivary microbiomics and machine learning algorithms.Scientific reports · 2025Article
- Unraveling the role of histone acetylation in sepsis biomarker discovery.Frontiers in molecular biosciences · 2025Article
- Metagenomics reveals unique gut mycobiome biomarkers in major depressive disorder - a non-invasive method.Frontiers in cellular and infection microbiology · 2025Article
- Dual signaling cascade regulating gut-lung axis in Interleukin-6/Interleukin-17 for NSCLC immuno pathogenesis.Frontiers in immunology · 2025Review
- The gut-lung axis: the impact of the gut mycobiome on pulmonary diseases and infections.Oxford open immunology · 2024Review
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsIn 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.
resultsFungal 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.
conclusionsFor the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis.
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