HPLC-HRMS and interpretable machine learning decipher serum lipidomic signatures in NSCLC.

Authors

Chengxi Tang, Jiahua Lyu, Jianming Huang, Xue Zhang, Chunxiao Mou, Yuxin Liu, Shuang Ni, Yudi Liu, Linjie Li, Ling Xiao, Shichuan Zhang, Xiumei Zheng

Published in

PeerJ. Volume 14. Pages e21504. Epub Jul 08, 2026.

Abstract

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer mortality, largely due to the lack of reliable non-invasive tools for detection and risk stratification. Lipid metabolic reprogramming is a hallmark of cancer and may serve as a promising source of diagnostic biomarkers.
Serum from 40 NSCLC patients and 30 controls was profiled by high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS), quantifying 331 annotated lipids. Differential and pathway analyses were performed. Least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM) models were evaluated using stratified 10-fold cross-validation; feature prioritization used recursive feature elimination and Shapley additive explanations (SHAP). A combined clinical-lipid model incorporating selected lipids and clinical covariates was assessed with discrimination, calibration, and decision-curve analysis.
NSCLC exhibited broad decreases in glycerophospholipids, sphingolipids, and triacylglycerols, consistent with membrane-lipid remodeling. LightGBM showed the best discrimination in internal validation. Key discriminant lipids included lysophosphatidylcholine (LPC(O-18:1)), decanoylcarnitine, and sulfatide (SL) (SL 38:5). The integrated lipid-clinical model achieved good discrimination (area under the receiver operating characteristic curve (AUC) = 0.946) and acceptable calibration. A nomogram was constructed for individualized risk estimation.
This study nominates candidate serum lipid markers and an interpretable modeling workflow for NSCLC classification in an exploratory case-control cohort. External validation and targeted quantification in larger, multicenter and screening-relevant populations are required before clinical implementation.

PMID:
42437038
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.

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