Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera
Abstract Differential diagnosis of pulmonary nodules detected by computed tomography (CT) remains a challenge in clinical practice. Here, we characterize the global metabolomes of 480 serum samples including healthy controls, benign pulmonary nodules, and stage I lung adenocarcinoma. The adenocarcin...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-37875-1 |
| _version_ | 1797836427111694336 |
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| author | Yao Yao Xueping Wang Jian Guan Chuanbo Xie Hui Zhang Jing Yang Yao Luo Lili Chen Mingyue Zhao Bitao Huo Tiantian Yu Wenhua Lu Qiao Liu Hongli Du Yuying Liu Peng Huang Tiangang Luan Wanli Liu Yumin Hu |
| author_facet | Yao Yao Xueping Wang Jian Guan Chuanbo Xie Hui Zhang Jing Yang Yao Luo Lili Chen Mingyue Zhao Bitao Huo Tiantian Yu Wenhua Lu Qiao Liu Hongli Du Yuying Liu Peng Huang Tiangang Luan Wanli Liu Yumin Hu |
| author_sort | Yao Yao |
| collection | DOAJ |
| description | Abstract Differential diagnosis of pulmonary nodules detected by computed tomography (CT) remains a challenge in clinical practice. Here, we characterize the global metabolomes of 480 serum samples including healthy controls, benign pulmonary nodules, and stage I lung adenocarcinoma. The adenocarcinoma demonstrates a distinct metabolomic signature, whereas benign nodules and healthy controls share major similarities in metabolomic profiles. A panel of 27 metabolites is identified in the discovery cohort (n = 306) to distinguish between benign and malignant nodules. The discriminant model achieves an AUC of 0.915 and 0.945 in the internal validation (n = 104) and external validation cohort (n = 111), respectively. Pathway analysis reveals elevation in glycolytic metabolites associated with decreased tryptophan in serum of lung adenocarcinoma vs benign nodules and healthy controls, and demonstrates that uptake of tryptophan promotes glycolysis in lung cancer cells. Our study highlights the value of the serum metabolite biomarkers in risk assessment of pulmonary nodules detected by CT screening. |
| first_indexed | 2024-04-09T15:08:45Z |
| format | Article |
| id | doaj.art-981ab3f998bb4fd982489ee53a30a6c1 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-09T15:08:45Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-981ab3f998bb4fd982489ee53a30a6c12023-04-30T11:19:56ZengNature PortfolioNature Communications2041-17232023-04-0114111210.1038/s41467-023-37875-1Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient seraYao Yao0Xueping Wang1Jian Guan2Chuanbo Xie3Hui Zhang4Jing Yang5Yao Luo6Lili Chen7Mingyue Zhao8Bitao Huo9Tiantian Yu10Wenhua Lu11Qiao Liu12Hongli Du13Yuying Liu14Peng Huang15Tiangang Luan16Wanli Liu17Yumin Hu18Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen UniversityDepartment of Clinical Laboratory, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer CenterDepartment of Radiology, The First Affiliated Hospital of Sun Yat-sen UniversityState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterDepartment of Pathology, The First Affiliated Hospital of Sun Yat-sen UniversityState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterMetabolomics Research Center, Zhongshan School of Medicine, Sun Yat-sen UniversityState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterSchool of Biology and Biological Engineering, South China University of TechnologyState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen UniversityDepartment of Clinical Laboratory, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterAbstract Differential diagnosis of pulmonary nodules detected by computed tomography (CT) remains a challenge in clinical practice. Here, we characterize the global metabolomes of 480 serum samples including healthy controls, benign pulmonary nodules, and stage I lung adenocarcinoma. The adenocarcinoma demonstrates a distinct metabolomic signature, whereas benign nodules and healthy controls share major similarities in metabolomic profiles. A panel of 27 metabolites is identified in the discovery cohort (n = 306) to distinguish between benign and malignant nodules. The discriminant model achieves an AUC of 0.915 and 0.945 in the internal validation (n = 104) and external validation cohort (n = 111), respectively. Pathway analysis reveals elevation in glycolytic metabolites associated with decreased tryptophan in serum of lung adenocarcinoma vs benign nodules and healthy controls, and demonstrates that uptake of tryptophan promotes glycolysis in lung cancer cells. Our study highlights the value of the serum metabolite biomarkers in risk assessment of pulmonary nodules detected by CT screening.https://doi.org/10.1038/s41467-023-37875-1 |
| spellingShingle | Yao Yao Xueping Wang Jian Guan Chuanbo Xie Hui Zhang Jing Yang Yao Luo Lili Chen Mingyue Zhao Bitao Huo Tiantian Yu Wenhua Lu Qiao Liu Hongli Du Yuying Liu Peng Huang Tiangang Luan Wanli Liu Yumin Hu Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera Nature Communications |
| title | Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera |
| title_full | Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera |
| title_fullStr | Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera |
| title_full_unstemmed | Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera |
| title_short | Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera |
| title_sort | metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high resolution mass spectrometry analysis of patient sera |
| url | https://doi.org/10.1038/s41467-023-37875-1 |
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