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Updated: Sep 11, 2026
Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
Untargeted metabolomics and lipidomics identified four subtypes of small cell lung cancer
Chenyue Zhang1,2, Xiaoling Shang3, Haiyong Wang4
1Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Introduction:
Small cell lung cancer (SCLC) is a heterogeneous malignancy with dismal prognosis. However, few studies have conducted on the metabolic heterogeneity in SCLC.
Objective:
We therefore identify SCLC classifications using untargeted metabolomics and lipidomics. We also compared their survival and the immunotherapy responses.
Methods:
Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) analysis was performed in 191 SCLC serum samples. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted to identify metabolic pathways. The Kaplan-Meier and log-rank test were used to analyze the survival curves. The univariate and multivariate Cox proportional hazards regression models were used to evaluate prognostic factors for OS in patients with SCLC.
Results:
Distinct subtypes of SCLC were identified by consensus clustering algorithm using partioning around medoids (pam) based on untargeted metabolomics and lipidomics. Four distinct subtypes of SCLC were identified, with distinct metabolic pathways. Subgroup 2 had the longest survival whereas Subgroup 1 had the shortest. Subtype 2 benefited most from immunotherapy in OS, as in contrast to Subtype 3 with shortest survival.
Conclusion:
Our study revealed the metabolic heterogeneity in SCLC and identified four subtypes with distinct metabolic features. It indicates promising therapeutic and prognostic value that may guide treatment for SCLC. The subtype-specific clinical trials may be designed and would be instructive for drug development.
