Evidence map›Paper›PMID 35261323›Full record

ArticleAnnals of medicine2022

Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study.

Yingchang Zou, Yanjie Hu, Zaile Jiang, Ying Chen, Yuan Zhou, Zhiyou Wang, Yu Wang, Guobao Jiang, Zhiguang Tan, Fangrong Hu

Abstract read
In one paragraph

Article in Annals of medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. [Applications and Advances of Metabolomics in Lung Cancer Research].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025
    Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Review
  11. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yingchang ZouSchool of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.ORCID 0000-0001-5253-8273
Yanjie HuDepartment of Medicine, Zhejiang Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China.
Zaile JiangTianhe Culture Chain Technologies Co Ltd, Changsha, China.
Ying ChenSchool of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.
Yuan ZhouSchool of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.
Zhiyou WangSchool of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.
Yu WangZhijiang Lab, Research Center for Healthcare Data Science, Hangzhou, China.
Guobao JiangSchool of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.
Zhiguang TanSchool of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.
Fangrong HuSchool of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe clinical application of lung cancer detection based on breath test is still challenging due to lack of predictive molecular markers in exhaled breath. This study explored potential lung cancer biomarkers and their related pathways using a typical process for metabolomics investigation. MATERIAL AND

methodsBreath samples from 60 lung cancer patients and 176 healthy people were analyzed by GC-MS. The original data were GC-MS peak intensity removing background signal. Differential metabolites were selected after univariate statistical analysis and multivariate statistical analysis based on OPLS-DA and Spearman rank correlation analysis. A multivariate PLS-DA model was established based on differential metabolites for pattern recognition. Subsequently, pathway enrichment analysis was performed on differential metabolites.

resultsThe discriminant capability was assessed by ROC curve of whom the average AUC and average accuracy in 100-fold cross validations were 0.871 and 0.787, respectively. Eight potential biomarkers were involved in a total of 18 metabolic pathways. Among them, 11 metabolic pathways have DISCUSSION: Some pathways among them are related to risk factors or therapies of lung cancer. However, more of them are dysregulated pathways of lung cancer reported in studies based on genome or transcriptome data.

conclusionWe believe that it opens the possibility of using metabolomics methods to analyze data of exhaled breath and promotes involvement of knowledge dataset to cover more volatile metabolites. CLINICAL SIGNIFICANCE: Although a series of related research reported diagnostic models with highly sensitive and specific prediction, the clinical application of lung cancer detection based on breath test is still challenging due to disease heterogeneity and lack of predictive molecular markers in exhaled breath. This study may promote the clinical application of this technique which is suitable for large-scale screening thanks to its low-cost and non-invasiveness. As a result, the mortality of lung cancer may be decreased in future.Key messagesIn the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer.We found that it is possible to apply metabolomics methods in analysis of data from breath test, which is meaningful to discover convinced volatile markers with definite pathological and histological significance.

Indexed as

Lung NeoplasmsBiomarkers, TumorBreath TestsGas Chromatography-Mass SpectrometryHumansPilot ProjectsBiomarkers, TumorExhaled metabolic markerslung cancermetabolomicspathway enrichmentvolatile organic compounds

Identifiers

PMID35261323
PMCPMC8920387

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.