Screening of potential microbial markers for lung cancer using metagenomic sequencing

Cancer Medicine · 12 authors, 3 centres

AI SUMMARY

FIDELITY 100%

This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.

AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

“

TL;DR

A model combining five key bacterial genera with the tumor marker NSE showed high diagnostic accuracy for distinguishing lung cancer from benign disease.

Full summary

543 CHARS

Key findings included lower microbial diversity and distinct metabolic profiles in malignant samples, with carbohydrate metabolism more prevalent than amino acid metabolism. Differential microorganisms were identified, and a random forest model combining five key genera (Klebsiella, Mycobacterium, Pedobacter, Prevotella, Xanthomonas) with the tumor marker NSE achieved an area under the curve (AUC) of 0.959 for distinguishing lung cancer from benign disease. The study suggests these microbes could serve as potential diagnostic biomarkers.

STUDY TYPE

other

SPECIALTY

oncology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10067086

Screening of potential microbial markers for lung cancer using metagenomic sequencing

Cancer Medicine

READ THE FULL PAPER

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