ArticleJournal of advanced research2026
Lipidomic signatures as predictive biomarkers for early-onset lung cancer: Identification and development of a risk prediction model.
Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction for lung cancer screening: a systematic review and meta-regression.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Lipid Metabolic Reprogramming in Lung Cancer: Mechanisms and Therapeutic Targets in the Tumor Microenvironment.International journal of cancer · 2026Review
- Preliminary revelation of potential therapeutic targets related to ribosome biogenesis in lung adenocarcinoma based on bioinformatics analysis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Identification of Circulating Lipidomic Biomarkers of Malnutrition Risk among Oncology Patients in the Total Cancer Care (TCC) Study: A Cross-Sectional Analysis.The Journal of nutrition · 2026Article
- Lipidomic signatures in lung adenocarcinoma and spontaneous pneumothorax tissues associated with heated tobacco use: a pilot study.BMC research notes · 2026Article
- The Growing Burden of Early-Onset Lung Cancer in Young Women in China: Analysis for the Global Burden of Disease Study 2021.International journal of women's health · 2026Article
- Inhalable extracellular vesicles as cell-free therapeutics for chronic respiratory disease.Frontiers in bioengineering and biotechnology · 2026Review
- HPLC-HRMS and interpretable machine learning decipher serum lipidomic signatures in NSCLC.PeerJ · 2026Article
- Applications and challenges of multi-omics approaches in lung cancer research and precision treatment.Frontiers in genetics · 2025Review
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Authors and funding
18 authors.
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Abstract
introductionLung cancer is the leading cause of cancer-related mortality worldwide. While traditionally associated with older adults, early-onset lung cancer (EOLC) is rising, particularly in Asia, which accounts for 75.9% of global cases. Existing lung cancer screening guidelines primarily focus on older populations, which may result in missed opportunities for early detection in younger individuals. Given its distinct clinical characteristics, EOLC warrants dedicated research and targeted interventions.
objectivesThis study aims to characterize the lipidomic profiles specific to EOLC patients (aged 18-49 years) and develop a biomarker-based predictive model to improve risk assessment and early detection.
methodsThe discovery and validation sets included 117 EOLC cases and 121 non-EOLC controls, all aged 18-49 years. Targeted lipidomics analysis, combined with logistic regression, was performed on plasma samples to identify differentially expressed lipids species. Clustering and pathway analyses were conducted to uncover and visualize the internal signatures of the identified lipids. Key lipids were refined using the LASSO-bootstrap regression method combined with the Boruta algorithm. A random forest model was subsequently employed to develop a robust prediction model for EOLC.
resultsA total of 843 lipids were identified, with 60 differentially expressed lipids detected, of which 33 were validated in the validation set. Cluster analysis revealed that passive smoking (OR: 2.75, 95% CI: 1.08-7.29) and current smoking (OR: 15.65, 95% CI: 2.55-142.10) were associated with elevated lipid metabolite profiles in EOLC patients. The validated lipids were further refined using LASSO and Boruta methods, which ultimately selected 6 lipids for inclusion in a prediction model constructed with random forest. This model achieved an area under the curve (AUC) of 0.874 in the validation set.
conclusionOur study identified lipidomic signatures associated with the risk of EOLC, offering potential translational implications for lung cancer prevention strategies.
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