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Method Article
Kun Qian
State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University
Corresponding Author
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Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 second using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.
Keywords
diagnostics, machine learning, cancer, metabolites, laser desorption/ionization mass spectrometry
The authors declare the following competing interests. The authors have filed patents for both the technology and the use of the technology to detect bio-samples.
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View the published article at Nature Communications.
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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
by Lin Huang, Lin Wang, Xiaomeng Hu, +12
Nature Communications (16 July, 2020)
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Method Article
Kun Qian
State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Published
View the published article at Nature Communications.
Version 1
Posted 26 May, 2021
No community comments so far
Metrics
Comments: 0
HTML Views: ...
Subject Areas
Diseases
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Nature Communications.
Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 second using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.
The authors declare the following competing interests. The authors have filed patents for both the technology and the use of the technology to detect bio-samples.
Loading...
Associated Publications
Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
by Lin Huang, Lin Wang, Xiaomeng Hu, +12
Nature Communications (16 July, 2020)