ArticleThe oncologist2024
Construction of a Diagnostic Model for Small Cell Lung Cancer Combining Metabolomics and Integrated Machine Learning.
Article in The oncologist, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- Machine Learning for Classification in Lung Cancer Using Routine Clinical and Laboratory Data.Annals of surgical oncology · 2026Article
- Lipidomic signatures as predictive biomarkers for early-onset lung cancer: Identification and development of a risk prediction model.Journal of advanced research · 2026Article
- Liquid biopsy in diagnosis and monitoring of treatment efficacy in patients with small cell lung cancer.Molecular biology reports · 2025Review
- Metabolic reprogramming signature predicts prognosis and immune landscape in small cell lung cancer: MOCS2 validation and implications for personalized therapy.Frontiers in molecular biosciences · 2025Article
- Machine learning-guided analysis of metabolomic alterations in Parkinson's disease with comorbid symptoms.Frontiers in aging neuroscience · 2025Article
- Translational impact of machine learning-driven predictive modeling with pathway-based plasma metabolomic biomarkers for lung cancer detection.Frontiers in oncology · 2025Article
- Applications and challenges of multi-omics approaches in lung cancer research and precision treatment.Frontiers in genetics · 2025Review
- Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.Frontiers in oncology · 2025Article
- Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma.Frontiers in immunology · 2025Article
- Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling.Cancers · 2024Article
- From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment.Medicina (Kaunas, Lithuania) · 2024Review
- Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer.Cell biology and toxicology · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundTo date, no study has systematically explored the potential role of serum metabolites and lipids in the diagnosis of small cell lung cancer (SCLC). Therefore, we aimed to conduct a case-cohort study that included 191 cases of SCLC, 91 patients with lung adenocarcinoma, 82 patients with squamous cell carcinoma, and 97 healthy controls.
methodsMetabolomics and lipidomics were applied to analyze different metabolites and lipids in the serum of these patients. The SCLC diagnosis model (d-model) was constructed using an integrated machine learning technology and a training cohort (n = 323) and was validated in a testing cohort (n=138).
resultsEight metabolites, including 1-mristoyl-sn-glycero-3-phosphocholine, 16b-hydroxyestradiol, 3-phosphoserine, cholesteryl sulfate, D-lyxose, dioctyl phthalate, DL-lactate and Leu-Phe, were successfully selected to distinguish SCLC from controls. The d-model was constructed based on these 8 metabolites and showed improved diagnostic performance for SCLC, with the area under curve (AUC) of 0.933 in the training cohort and 0.922 in the testing cohort. Importantly, the d-model still had an excellent diagnostic performance after adjusting the stage and related clinical variables and, combined with the progastrin-releasing peptide (ProGRP), showed the best diagnostic performance with 0.975 of AUC for limited-stage patients.
conclusionThis study is the first to analyze the difference between metabolomics and lipidomics and to construct a d-model to detect SCLC using integrated machine learning. This study may be of great significance for the screening and early diagnosis of SCLC patients.
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