SynthesisCancer medicine2023
Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis.
Synthesis in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Intratumoral and fecal microbiota reveals microbial markers associated with gastric carcinogenesis.Frontiers in cellular and infection microbiology · 2024Pooled it
- Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis.Cancer medicine · 2023Pooled it
- Integrated analysis of the diabetic foot ulcer microbiome and host transcriptome supports a microenvironment-microbiota-host repair framework.Endocrine · 2026Article
- Harnessing Gut Microbiota to Enhance Immunotherapy in NSCLC: From Mechanisms to Translational Applications.Cancer medicine · 2026Review
- Gut microbiota impact on lung diseases: a mini review of clinical evidence.Infection and immunity · 2026Review
- Lung microbiota analysis in early-stage lung adenocarcinoma.Microbiology spectrum · 2026Article
- Gut-Lung Microbiota Axis Shapes the Immune Microenvironment and Immunotherapeutic Response in Lung Cancer.International journal of biological sciences · 2026Review
- Respiratory Microbiota Associations with Asthma Across American and Emirati Adults: A Comparative Analysis.Applied microbiology (Basel, Switzerland) · 2025Article
- The Current Roadmap of Lung Cancer Biology, Genomics and Racial Disparity.International journal of molecular sciences · 2025Review
- One-step diagnosis of infection and lung cancer using metagenomic sequencing.Respiratory research · 2025Article
- Revealing gut microbiota biomarkers associated with melanoma immunotherapy response and key bacteria-fungi interaction relationships: evidence from metagenomics, machine learning, and SHAP methodology.Frontiers in immunology · 2025Article
- Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches.Frontiers in cellular and infection microbiology · 2025Article
- Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large-scale meta-analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine-learning classifier.
methodsIn total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony-related functions.
resultsThe α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top-ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota-associated functions between cancer-affected and healthy individuals that were primarily associated with metabolic disturbances.
conclusionsGM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC-specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.
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