There are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. We try to establish a highly accurate method for detecting early-stage lung cancer by combining machine learning with untargeted and targeted metabolomics .
All plasma lipids were first detected by untargeted metabolomics methods and 9 feature lipids of early-stage lung cancer were selected by support vector machine algorithm. Then, a targeted metabolomics method was developed to detect the 9 lipids quantitatively based on multiple reaction monitoring mode. Finally, a detection model was established based on the 9 lipids.
Study Type
OBSERVATIONAL
Enrollment
558
Plasma lipids were detected by an Ultimate 3000 ultra-high-performance liquid chromatography (UHPLC) system coupled with Q-Exactive MS (Thermo Scientific) . Then a detection model was built based on plasma lipids using machine learning algorithm.
Peking University People's Hospital
Beijing, Beijing Municipality, China
Plasma Lipids
A detection model based on 9 lipids were developed, including 3 lysophosphatidylcholines, 5 phosphatidylcholines, and a triglyceride. The 9 lipids were detected by targeted metabolomics by mass spectrometry.
Time frame: All samples were detected together after participants recruitment and sample collection. All samples were detected within 18 months from sample collection.
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