SEARCHCROSS-SECTIONALoncology, pulmonology

cross-sectional·oncology, pulmonology, microbiology, infectious disease, thoracic surgery·PMC10067086

Screening of potential microbial markers for lung cancer using metagenomic sequencing

Cancer Medicine · 12 authors, 3 centres

AI SUMMARY

FIDELITY 92%

POPULATION60 patients with suspicious lung nodules (0.8–3.0 cm) undergoing bronchoscopy at Tianjin Chest Hospital (29 lung cancer, 31 benign lung disease)

INTERVENTIONMetagenomic next-generation sequencing of bronchoalveolar lavage fluid (BALF) to characterize microbial composition and metabolic pathways

COMPARISONLung cancer (malignant) group vs. benign lung disease group

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TL;DR

This study used metagenomic sequencing of bronchoalveolar lavage fluid (BALF) to identify microbial biomarkers that distinguish lung cancer from benign lung disease. The authors found that a random forest model combining five key bacterial genera (Klebsiella, Mycobacterium, Pedobacter, Prevotella, Xanthomonas) plus the tumor marker neuron-specific enolase (NSE) achieved superior diagnostic performance with an AUC of 0.959, 100% sensitivity, and 85.7% specificity. These findings suggest that specific lung microbiome signatures may serve as potential diagnostic tools and therapeutic targets for lung cancer.

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**Background:** Lung cancer is the most prevalent cancer in China, with 816,563 new cases and 714,699 deaths in 2020. Emerging evidence links the lung microbiome to tumorigenesis through mechanisms including toxin secretion, immune barrier disruption, and oncogenic metabolite release. Previous studies using 16S rRNA sequencing have identified microbial differences in lung cancer, but metagenomic sequencing—which captures bacteria, viruses, and fungi—has been underutilized. This study aimed to screen microbial biomarkers in BALF (chosen over saliva to avoid oral contamination) using metagenomic sequencing to distinguish lung cancer from benign lung disease.

**Methods:** Between February and September 2021, 60 patients with suspicious lung nodules (0.8–3.0 cm) were enrolled at Tianjin Chest Hospital, including 29 with lung cancer (12 adenocarcinoma, 11 squamous cell carcinoma, 6 small cell lung cancer) and 31 with benign lung diseases (most commonly sarcoidosis). Exclusion criteria included acute lung infections, second primary tumors, COPD, pulmonary fibrosis, bronchiectasis, and antibiotic use within 1 month. BALF was collected via standardized bronchoscopy with a negative control (NC) wash. Genomic DNA was extracted using QIAamp DNA Microkit, libraries were constructed with QIAseq Ultralow Input Library Kit, and sequencing was performed on a Nova6000 PE150 platform. Raw reads were quality-filtered (Q<30, <35 bp removed), human reads were removed with Bowtie2, and taxonomic profiling used Kraken2 and Bracken. Contaminants were removed if RPM >50 in NC or ≥1/3 of total NC RPM. Alpha diversity (Chao1, Shannon, Gini–Simpson) and beta diversity (PCA, NMDS, MRPP) were assessed. LEfSe (LDA>2) and Metastat identified differentially abundant taxa. Metabolic pathway analysis used HUMAnN3 with MetaCyc and KEGG (GSVA, adjusted p<0.05, |log2FC|>0.2). Random forest classifiers with leave-one-out cross-validation were built using 40 differentially abundant genera and three tumor markers (CEA, NSE, CYF21-1).

**Key Results:** No significant differences in age, gender, BMI, or smoking status existed between groups. Tumor marker levels (CEA, NSE, CYF21-1) differed significantly. Alpha diversity was generally lower in malignant samples, with Chao1 index significantly different at genus level (p value not explicitly stated in text). MRPP confirmed intergroup differences exceeded intragroup differences. PCA showed malignant samples clustered more tightly than benign samples. MetaCyc analysis revealed amino acid-related metabolism (e.g., L-arginine, L-ornithine biosynthesis) was enriched in benign samples, while carbohydrate-related pathways (starch/sucrose metabolism, pentose/glucuronate interconversions, galactose metabolism) were enriched in malignant samples. LEfSe identified differentially abundant genera including Prevotella, Klebsiella, Mycobacterium, Gordonia, and Sphingobium. Metastat top genera included Achromobacter (benign: 0.000012±0.000008, malignant: 0.003713±0.002897, p=0.000999), Chryseobacterium, Herbaspirillum, Pedobacter, Thermomonas, Undibacterium, Caulobacter, Novosphingobium, Prevotella (benign: 0.189897±0.037106, malignant: 0.064073±0.018263, p=0.001998), and Dechloromonas. Random forest feature importance (MDA>5 or MDG>2) selected five key genera: Klebsiella, Mycobacterium, Pedobacter, Prevotella, and Xanthomonas. The five-genus classifier alone achieved AUC=0.898. Adding NSE yielded the best performance: AUC=0.959, specificity=85.7%, sensitivity=100%, cut-off=0.386. Comparators included all 40 genera (AUC=0.847), five genera+CEA (AUC=0.837), five genera+CYF21-1 (AUC=0.898), and five genera+NSE+CYF21-1 (AUC=0.878).

**Clinical Implications:** The five-genus+NSE model outperformed previously published diagnostic models (Jin et al. AUC=0.882; Cheng et al. AUC=0.845), suggesting strong potential as a clinical diagnostic tool. The differential enrichment of amino acid vs. carbohydrate metabolism pathways points to microbial involvement in lung cancer metabolic reprogramming. Specific genera (e.g., Prevotella linked to ERK/PI3K upregulation; Mycobacterium associated with prior TB and PD-1/PDL1 signaling) may represent therapeutic targets. Limitations include small sample size (n=60), single-center design, lack of validation cohort, no histological subtype stratification, and cross-sectional design preventing causal inference. BALF is less accessible than saliva, which may limit clinical translation. Further multi-center studies with larger cohorts are needed.

PICO

PPOPULATION

60 patients with suspicious lung nodules (0.8–3.0 cm) undergoing bronchoscopy at Tianjin Chest Hospital (29 lung cancer, 31 benign lung disease)

IINTERVENTION

Metagenomic next-generation sequencing of bronchoalveolar lavage fluid (BALF) to characterize microbial composition and metabolic pathways

OOUTCOME

Microbial diversity indices, differentially abundant taxa, metabolic pathway enrichment, and diagnostic performance (AUC, sensitivity, specificity) of microbial+tumor marker classifiers

STUDY TYPE

cross-sectional

SPECIALTY

oncology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

92% · A

ORIGINAL PAPER

PMC10067086

Screening of potential microbial markers for lung cancer using metagenomic sequencing

Cancer Medicine

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ABSTRACT

Abstract Introduction Lung cancer is the most prevalent cancer with high mortality in China, and it is associated with the dysbiosis of the lung microbiome. This study attempted to screen for specific microorganisms as potential biomarkers for distinguishing benign lung disease from lung cancer. Methods Bronchoalveolar lavage fluid (BALF) sample was selected in the study instead of saliva to avoid contamination with oral microorganisms, and microbial taxonomic and functional differences in BALF samples from patients with lung cancer and those with those from patients with benign lung diseases were performed based on metagenomic next‐generation sequencing, for the first time, so that microorganisms other than bacteria could be included. Results The results showed that the intrasample diversity of malignant samples was different from benign samples, and the microbial differences among malignant samples were smaller, with lower microbial diversity, significantly changed microbial abundance and metabolic functions. Metabolic function analysis revealed amino acid‐related metabolism was more prevalent in benign samples, whereas carbohydrate‐related metabolism was more prevalent in malignant samples. By LEfSe, Metastat and Random Forest analysis, we identified a series of important differential microorganisms. Importantly, the model combining five key genera plus one tumor marker (neuron‐specific enolase) as indicators presented the optimal disease typing performance. Conclusion Thus results suggest the value of these differential microorganisms enriched in tumors in mechanism research and may be potential new targets for lung cancer therapy. More importantly, the biomarkers identified in this study can be conducive to improve the clinical diagnosis of lung cancer and have good application prospects. Keywords: bronchoalveolar lavage fluid, disease typing biomarkers, lung cancer, lung microbiome, metagenomic next‐generation sequencing

CITATION EXPORT

Qiang Chen, Kai Hou, Mingze Tang et al.. Screening of potential microbial markers for lung cancer using metagenomic sequencing. Cancer Medicine. (2022). https://doi.org/10.1002/cam4.5513

AUTHORS

12 · 3 CENTRES

Qiang Chen, Kai Hou, Mingze Tang, Shuo Ying, Xiaoyun Zhao, Guanhua Li, Jianhui Pan, Xiaomin He, Han Xia, Yuechuan Li, Zheng Lou, Li Zhang

1 Tianjin Chest Hospital, Tianjin, China
2 Tianjin Medical University, Tianjin, China
3 Hugobiotech Co., Ltd. Beijing, China