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Related Experiment Video

Updated: Jul 13, 2026

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Published on: May 20, 2013

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

Chengxi Tang¹, Jiahua Lyu², Jianming Huang³

¹Department of Radiation, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

|July 12, 2026

Summary

This study identifies novel serum lipid biomarkers for non-small cell lung cancer (NSCLC) detection. An integrated model combining lipids and clinical data achieved high accuracy, offering a potential non-invasive tool for risk stratification.

Keywords:

Liquid chromatography-mass spectrometry Machine learning Non-small cell lung cancer Serum lipidomics

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Last Updated: Jul 13, 2026

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

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Published on: May 20, 2013

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MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier (MSC) for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier (MSC) for Lung Cancer Screening

Published on: October 26, 2017

Area of Science:

Oncology
Biochemistry
Computational Biology

Background:

Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality.
Lack of non-invasive tools for NSCLC detection and risk stratification hinders effective management.
Lipid metabolic reprogramming in cancer presents opportunities for biomarker discovery.

Purpose of the Study:

To identify and validate serum lipid biomarkers for non-small cell lung cancer (NSCLC) detection.
To develop and evaluate a computational model for NSCLC risk stratification using lipid profiles.
To explore the potential of lipidomics in non-invasive cancer diagnostics.

Main Methods:

Serum lipid profiling of 40 NSCLC patients and 30 controls using HPLC-HRMS.
Application of machine learning models (LASSO, SVM, XGBoost, LightGBM) for classification.
Development of an integrated lipid-clinical model with nomogram for risk estimation.

Main Results:

NSCLC samples showed decreased glycerophospholipids, sphingolipids, and triacylglycerols.
The LightGBM model demonstrated superior discrimination in internal validation.
An integrated lipid-clinical model achieved an AUC of 0.946 with acceptable calibration.

Conclusions:

Candidate serum lipid markers and an interpretable modeling workflow for NSCLC classification were identified.
The findings suggest potential for non-invasive NSCLC detection and risk stratification.
Further external validation in larger, multicenter cohorts is necessary for clinical implementation.