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- Title
Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis.
- Authors
Wang, Guangxi; Qiu, Mantang; Xing, Xudong; Zhou, Juntuo; Yao, Hantao; Li, Mingru; Yin, Rong; Hou, Yan; Li, Yang; Pan, Shuli; Huang, Yuqing; Yang, Fan; Bai, Fan; Nie, Honggang; Di, Shuangshuang; Guo, Limei; Meng, Zhu; Wang, Jun; Yin, Yuxin
- Abstract
Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism–related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum–based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0–18:1, 16:0–18:2, 18:0–18:1, 18:0–18:2, and 16:0–22:6; and triglycerides 16:0–18:1–18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography–mass spectrometry (MS)–based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention. Lipidomics for early lung cancer detection: Improving early detection of lung cancer is key to improving outcomes. On the basis of their exploratory single-cell RNA sequencing suggesting dysregulated lipid metabolism in early lung cancer tissue, Wang et al. performed lung plasma lipidomic profiling and found that nine lipids were commonly dysregulated in patients with early-stage lung tumors. The authors validated the ability of these nine lipids to detect early-stage cancer across multiple independent cohorts largely composed of nonsmokers with stage I adenocarcinomas, as compared with healthy individuals or individuals with benign tumors. This study shows promise for the clinical detection of early-stage lung cancer in patients.
- Subjects
LIPID metabolism; LUNG cancer; EARLY detection of cancer; LIQUID chromatography-mass spectrometry; OXYGENATORS; BENIGN tumors; RNA metabolism
- Publication
Science Translational Medicine, 2022, Vol 14, Issue 630, p1
- ISSN
1946-6234
- Publication type
Article
- DOI
10.1126/scitranslmed.abk2756