ArticleFrontiers in oncology2024
Plasma lipidomics profiling in predicting the chemo-immunotherapy response in advanced non-small cell lung cancer.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- HPLC-HRMS and interpretable machine learning decipher serum lipidomic signatures in NSCLC.PeerJ · 2026Article
- Identification of predictive biomarkers for nivolumab efficacy in non-small cell lung cancer through integrated serum lipidomics and proteomics analysis.Frontiers in immunology · 2026Article
- Targeting Lipid Metabolic Reprogramming to Overcome Immunotherapy Resistance: Systemic Nutritional Modulation and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Plasma proteomics to explore benefit from anti-PD-(L)1 in advanced solid tumors: a prospective pilot study.Scientific reports · 2025Article
- Applications and challenges of multi-omics approaches in lung cancer research and precision treatment.Frontiers in genetics · 2025Review
- Preoperative lipid levels and prognosis in non-small cell lung cancer: A retrospective age-stratified study.Science progressArticle
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4 authors.
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Abstract
Background: Advanced non-small cell lung cancer (NSCLC) presents significant treatment challenges, with chemo-immunotherapy emerging as a promising approach. This study explores the potential of lipidomic biomarkers to predict responses to chemo-immunotherapy in advanced non-small cell lung cancer (NSCLC) patients. Methods: A prospective analysis was conducted on 68 NSCLC patients undergoing chemo-immunotherapy, divided into disease control (DC) and progressive disease (PD) groups based on treatment response. Pre-treatment serum samples were subjected to lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Key predictive lipids (biomarkers) were identified through projection to latent structures discriminant analysis. A biomarker combined model and a clinical combined model were developed to enhance the prediction accuracy. The predictive performances of the clinical combined model in different histological subtypes were also performed. Results: Six lipids were identified as the key lipids. The expression levels of PC(16:0/18:2), PC(16:0/18:1), PC(16:0/18:0), CE(20:1), and PC(14:0/18:1) were significantly up-regulated. While the expression level of TAG56:7-FA18:2 was significantly down-regulated. The biomarker combined model demonstrated a receiver operating characteristic (ROC) curve of 0.85 (95% CI: 0.75-0.95) in differentiating the PD from the DC. The clinical combined model exhibited an AUC of 0.87 (95% CI: 0.79-0.96) in differentiating the PD from the DC. The clinical combined model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.78 (95% CI: 0.62-0.96), 0.79 (95% CI: 0.64-0.94), and 0.86 (95% CI: 0.52-1.00) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively. Pathway analysis revealed the metabolisms of linoleic acid, alpha-linolenic acid, glycerolipid, arachidonic acid, glycerophospholipid, and steroid were implicated in the chemo-immunotherapy response in advanced NSCLC. Conclusion: Lipidomic profiling presents a highly accurate method for predicting responses to chemo-immunotherapy in patients with advanced NSCLC, offering a potential avenue for personalized treatment strategies.
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