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

Nature Communications
Lin HuangKun Qian

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 < 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.

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Citations

Oct 28, 2020·Small·Md Sohel RanaKun Qian
Jan 20, 2021·ACS Applied Materials & Interfaces·Yuning WangBaohong Liu
Mar 28, 2021·Biosensors & Bioelectronics·Dujuan LiGaofeng Wang
Mar 16, 2021·Angewandte Chemie·Rongxin LiJingjing Wan
Aug 8, 2021·Journal of Genetics and Genomics = Yi Chuan Xue Bao·Xuezhu WangYang Chen
Aug 20, 2021·Cellular and Molecular Life Sciences : CMLS·Andrey S MarchevMilen I Georgiev
Oct 14, 2021·Chemical Communications : Chem Comm·Yonglei WuHao Wu
Oct 19, 2021·Frontiers in Chemistry·Qiongqiong WanSuming Chen

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Methods Mentioned

BETA
biopsy
nuclear magnetic resonance
transmission electron microscopy
electron diffraction
scanning electron microscopy
dynamic light scattering
blood drawn
PCA

Software Mentioned

SIMCA
ORCA
MetaboAnalyst
MATLAB
SPSS
MasSpec Pen

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