High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis

Abstract Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general...

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Autores principales:	Shi-ang Qi, Qian Wu, Zhenpu Chen, Wei Zhang, Yongchun Zhou, Kaining Mao, Jia Li, Yuanyuan Li, Jie Chen, Youguang Huang, Yunchao Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/522ec738648e412cbed5ee40157a1bd0
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spelling	oai:doaj.org-article:522ec738648e412cbed5ee40157a1bd02021-12-02T15:02:23ZHigh-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis10.1038/s41598-021-91276-22045-2322https://doaj.org/article/522ec738648e412cbed5ee40157a1bd02021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91276-2https://doaj.org/toc/2045-2322Abstract Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.Shi-ang QiQian WuZhenpu ChenWei ZhangYongchun ZhouKaining MaoJia LiYuanyuan LiJie ChenYouguang HuangYunchao HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shi-ang Qi Qian Wu Zhenpu Chen Wei Zhang Yongchun Zhou Kaining Mao Jia Li Yuanyuan Li Jie Chen Youguang Huang Yunchao Huang High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis
description	Abstract Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.
format	article
author	Shi-ang Qi Qian Wu Zhenpu Chen Wei Zhang Yongchun Zhou Kaining Mao Jia Li Yuanyuan Li Jie Chen Youguang Huang Yunchao Huang
author_facet	Shi-ang Qi Qian Wu Zhenpu Chen Wei Zhang Yongchun Zhou Kaining Mao Jia Li Yuanyuan Li Jie Chen Youguang Huang Yunchao Huang
author_sort	Shi-ang Qi
title	High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis
title_short	High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis
title_full	High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis
title_fullStr	High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis
title_full_unstemmed	High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis
title_sort	high-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/522ec738648e412cbed5ee40157a1bd0
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_version_	1718389137616142336

High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis

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