ArticleJournal of translational medicine2025
Lipidomics reveals biomarkers of the efficacy of first-line ICI therapy combined with chemotherapy in NSCLC.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine Learning-Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation.Metabolites · 2026Review
- A comprehensive plasma-based approach to thromboinflammation in bladder cancer: integrating lipidomics, thrombin generation, and NETosis biomarkers.Journal of translational medicine · 2026Article
- Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.Journal of translational medicine · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundImmune checkpoint inhibitors (ICIs) plus chemotherapy have become the first-line standard therapy for non-oncogene addicted advanced non-small cell lung cancer (NSCLC) patients. There is a lack of reliable biomarkers to predict treatment outcomes. This study aimed to identify relevant lipids that can predict treatment outcomes in NSCLC patients receiving first-line ICIs plus chemotherapy via lipidomics.
methodWe recruited non-oncogene addicted advanced NSCLC patients receiving first-line ICI plus chemotherapy to participate in this study. According to the progression free survival (PFS) criteria, these patients were categorized into a response (R) group (PFS ≥ 12 months) and a non-response (NR) group (PFS < 12 months). Plasma samples were collected from patients before the start of treatment for untargeted lipidomics and semi-targeted lipidomics analysis.
resultsA total of 49 patients were included in this study. We screened 13 differential lipids according to P < 0.05 and AUC > 0.7. Multivariate logistic regression further identified FA-18:2 (linoleic acid, LA), PE-P-34:2, and PI-40:4 as independent predictors. The triple-lipid biomarker panel demonstrated showed good predictive performance with an AUC of 0.878. We further investigated the role of LA, a pivotal lipid involved in immune regulation, in animal and cellular models and explored its potential in enhancing NSCLC immunotherapy. Our results showed that LA synergistically enhanced PD-1 inhibitor efficacy in the Lewis lung cancer mouse model, significantly suppressing tumor growth, and downregulating PD-L1 protein expression in both tumor cells and tumor tissues.
conclusionWe established the lipidomics-based predictive model for the efficacy of non-oncogene addicted advanced NSCLC patients treated with first-line chemotherapy plus ICI and elucidated a novel mechanism whereby LA potentiates immunotherapy by regulating the PD-L1 pathway. These findings provide dual theoretical foundations for personalized treatment strategies and the development of immunoadjuvants.
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