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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.


ABSTRACT: 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?s 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?

SUBMITTER: Huang L 

PROVIDER: S-EPMC7366718 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Huang Lin L   Wang Lin L   Hu Xiaomeng X   Chen Sen S   Tao Yunwen Y   Su Haiyang H   Yang Jing J   Xu Wei W   Vedarethinam Vadanasundari V   Wu Shu S   Liu Bin B   Wan Xinze X   Lou Jiatao J   Wang Qian Q   Qian Kun K  

Nature communications 20200716 1


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/ionizati  ...[more]

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