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

INTRODUCTION: The 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 investigat...

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Autores principales: Zou, Yingchang, Hu, Yanjie, Jiang, Zaile, Chen, Ying, Zhou, Yuan, Wang, Zhiyou, Wang, Yu, Jiang, Guobao, Tan, Zhiguang, Hu, Fangrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920387/
https://www.ncbi.nlm.nih.gov/pubmed/35261323
http://dx.doi.org/10.1080/07853890.2022.2048064
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author Zou, Yingchang
Hu, Yanjie
Jiang, Zaile
Chen, Ying
Zhou, Yuan
Wang, Zhiyou
Wang, Yu
Jiang, Guobao
Tan, Zhiguang
Hu, Fangrong
author_facet Zou, Yingchang
Hu, Yanjie
Jiang, Zaile
Chen, Ying
Zhou, Yuan
Wang, Zhiyou
Wang, Yu
Jiang, Guobao
Tan, Zhiguang
Hu, Fangrong
author_sort Zou, Yingchang
collection PubMed
description INTRODUCTION: The 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 METHODS: Breath 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. RESULTS: The 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 p-value smaller than .1. 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. CONCLUSION: We 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 MESSAGES: 1. In the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer. 2. 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.
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spelling pubmed-89203872022-03-15 Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study Zou, Yingchang Hu, Yanjie Jiang, Zaile Chen, Ying Zhou, Yuan Wang, Zhiyou Wang, Yu Jiang, Guobao Tan, Zhiguang Hu, Fangrong Ann Med Pulmonary Medicine INTRODUCTION: The 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 METHODS: Breath 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. RESULTS: The 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 p-value smaller than .1. 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. CONCLUSION: We 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 MESSAGES: 1. In the present study, 11 pathways involving 8 potential biomarkers were discovered to be dysregulated pathways of lung cancer. 2. 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. Taylor & Francis 2022-03-09 /pmc/articles/PMC8920387/ /pubmed/35261323 http://dx.doi.org/10.1080/07853890.2022.2048064 Text en © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Pulmonary Medicine
Zou, Yingchang
Hu, Yanjie
Jiang, Zaile
Chen, Ying
Zhou, Yuan
Wang, Zhiyou
Wang, Yu
Jiang, Guobao
Tan, Zhiguang
Hu, Fangrong
Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study
title Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study
title_full Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study
title_fullStr Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study
title_full_unstemmed Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study
title_short Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study
title_sort exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study
topic Pulmonary Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920387/
https://www.ncbi.nlm.nih.gov/pubmed/35261323
http://dx.doi.org/10.1080/07853890.2022.2048064
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