Evidence map›Paper›PMID 39754314›Full record

ArticleClinical and translational medicine2025

Unravelling the prognostic and operative role of intratumoural microbiota in non-small cell lung cancer: Insights from 16S rRNA and RNA sequencing.

Fuling Mao, Zixuan Hu, Ruifeng Shi, Hongbing Zhang, Zihe Zhang, Yongwen Li, Xuanguang Li, Penghu Gao, Jinhui Li, Minghui Liu and 2 more

Abstract read
In one paragraph

Article in Clinical and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. [Research Progress on the Relationship between Intratumor Microbiota and Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025
    Review
  6. Review
  7. Article
4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Fuling MaoDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.ORCID 0009-0006-7103-8577
Zixuan HuDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Ruifeng ShiDepartment of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Hongbing ZhangDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Zihe ZhangDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Yongwen LiTianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.
Xuanguang LiDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Penghu GaoDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Jinhui LiDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Minghui LiuDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Hongyu LiuTianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, China.
Jun ChenDepartment of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, China.

Funding

Beijing Science and Technology Innovation Medical Development Fund KC2023-JX-0288-PZ78National Natural Science Foundation of China 82072595National Natural Science Foundation of China 82473191Natural Science Foundation of Tianjin Municipality 23JCZDJC00710Tianjin Health Science and Technology Project TJWJ2022XK005Tianjin Key Medical Discipline (Specialty) Construction Project TJYXZDXK-061B
6 · The paper itself

Abstract

backgroundComplex interrelationships between the microbiota and cancer have been identified by several studies. However, despite delineating microbial composition in non-small cell lung cancer (NSCLC), key pathogenic microbiota and their underlying mechanisms remain unclear.

methodsWe performed 16S rRNA V3-V4 amplicon and transcriptome sequencing on cancerous and adjacent normal tissue samples from 30 patients with NSCLC, from which clinical characteristics and prognosis outcomes were collected. We used 16S rRNA sequencing to dissect microbial composition and perform prognosis correlations, and in conjunction with transcriptome sequencing, we determined potential mechanisms underpinning significant microbiota actions.

resultsIn comparing different sample types, we identified more pronounced beta diversity disparity between NSCLC, lung squamous cell carcinoma (LUSC) and corresponding paired normal tissues. Concurrently, LUSC and lung adenocarcinoma exhibited distinct microbial composition traits at genus levels. Subsequently, four phyla, five classes, nine orders, 17 families and 36 genera were filtered out and were related to prognosis outcomes. Intriguingly, a protective microbial cluster was identified encompassing nine genera associated with delayed disease recurrence, with functional analyses suggested that these microbiota predominantly exerted metabolism-related functions. Additionally, a harmful microbial cluster (HMC) was identified, including three genera. In this HMC and subsequent prognosis model analyses, harmful intratumoural microbiota were potentially implicated in infection, inflammation and immune regulation. Crucially, we identified a microbial genus, Peptococcus, which was as an independent, detrimental NSCLC prognostic factor and potentially impacted prognosis outcomes via tumour necrosis factor (TNF) signalling.

conclusionsWe identified a substantial connection between intratumoural microbiota and NSCLC prognosis outcomes. Protective microbiota primarily exerted metabolic functions, whereas harmful microbiota were mainly implicated in infection, inflammation and immune modulation. Furthermore, Peptococcus may be significant in adverse NSCLC prognoses and serve as a potential biomarker for patient management and cancer screening. KEY POINTS: Four phyla, five classes, nine orders, 17 families and 36 genera have been found associated with NSCLC prognosis. We identified a protective microbial cluster associated with delayed recurrence and a harmful microbial cluster related to shorter survival and earlier recurrence. We identified Peptococcus as an independent, detrimental prognostic factor for NSCLC, potentially impacting prognosis via TNF signalling.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsMicrobiotaRNA, Ribosomal, 16SSequence Analysis, RNAAgedFemaleHumansMaleMiddle AgedPrognosisRNA, Ribosomal, 16Smicrobiotanon‐small cell lung cancer (NSCLC)Peptococcusprognosisprotective and harmful microbial clusters

Identifiers

PMID39754314
PMCPMC11702424

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.