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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-91276-2 |
| _version_ | 1818343316603797504 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T16:28:39Z |
| format | Article |
| id | doaj.art-522ec738648e412cbed5ee40157a1bd0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-13T16:28:39Z |
| publishDate | 2021-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-522ec738648e412cbed5ee40157a1bd02022-12-21T23:38:33ZengNature PortfolioScientific Reports2045-23222021-06-0111111010.1038/s41598-021-91276-2High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosisShi-ang Qi0Qian Wu1Zhenpu Chen2Wei Zhang3Yongchun Zhou4Kaining Mao5Jia Li6Yuanyuan Li7Jie Chen8Youguang Huang9Yunchao Huang10Electrical and Computer Engineering, University of AlbertaShanghai Center for Bioinformation Technology and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology InstituteThird Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital)Electrical and Computer Engineering, University of AlbertaThird Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital)Electrical and Computer Engineering, University of AlbertaThird Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital)Shanghai Center for Bioinformation Technology and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology InstituteElectrical and Computer Engineering, University of AlbertaThird Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital)Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital)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.https://doi.org/10.1038/s41598-021-91276-2 |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis |
| title_sort | high resolution metabolomic biomarkers for lung cancer diagnosis and prognosis |
| url | https://doi.org/10.1038/s41598-021-91276-2 |
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