Cancer Cell Int. 2025 Apr 24;25(1):161. doi: 10.1186/s12935-025-03792-0.

ABSTRACT

BACKGROUND: For patients with nodules detected in imaging that are indeterminate for malignancy, achieving accurate, early, and non-invasive diagnosis of Lung Squamous Cell Carcinoma (LUSC) remains a significant challenge. Therefore, we aimed to establish diagnostic and prognostic models by identifying plasma extracellular vesicles (EVs) associated protein biomarkers specific to LUSC.

METHODS: This study employed a novel nanomaterial, NaY, for the enrichment of EVs from plasma. Validation was conducted through transmission electron microscopy, nanoparticle tracking analyses, and Western blotting. Machine learning algorithms were utilized to compute protein biomarkers associated with LUSC and establish a diagnostic model. Additionally, a prognostic prediction model for LUSC was developed using a combination of 101 machine learning algorithms. Risk scoring of patients was performed to explore the underlying reasons for prognostic differences between high and low-risk groups.

RESULTS: The results of three experiments demonstrate that the new nanomaterial NaY effectively enriches EVs from plasma. Analysis of the enriched profile reveals pathways related to glycolysis/gluconeogenesis and carbon metabolism enriched in plasma EVs of LUSC patients. Thirty-eight LSCC-related EV biomarkers were identified, from which five proteins (TUBB3, RPS7, RPLP1, KRT2, and VTN) were selected to establish a diagnostic model distinguishing between benign and LUSC nodules. The diagnostic efficacy of RPS7 and VTN was further validated in independent samples using ELISA experiments. Furthermore, DPYD, GALK1, CDC23, UBE2L3, RHEB, and PSME1 were determined as potential prognostic biomarkers. Subsequently, risk scores were computed for each sample, classifying all patients into high and low-risk groups. Enrichment analysis revealed that EVs from the high-risk group contained proteins promoting cell proliferation and invasion, while those from the low-risk group were enriched in immune-related protein biomarkers.

CONCLUSIONS: The novel nanomaterial NaY effectively enriches EVs from plasma. Utilizing plasma EV biomarkers, the diagnostic model demonstrates strong discriminative ability between benign and malignant pulmonary nodules in patients.

PMID:40275246 | PMC:PMC12023671 | DOI:10.1186/s12935-025-03792-0

Front Immunol. 2025 Feb 7;16:1479550. doi: 10.3389/fimmu.2025.1479550. eCollection 2025.

ABSTRACT

BACKGROUND: To construct a prediction model consisting of metabolites and proteins in peripheral blood plasma to predict whether patients with unresectable stage III and IV non-small cell lung cancer can benefit from immunotherapy before it is administered.

METHODS: Peripheral blood plasma was collected from unresectable stage III and IV non-small cell lung cancer patients who were negative for driver mutations before receiving immunotherapy. Then we classified samples according to the follow-up results after two courses of immunotherapy and non-targeted metabolomics and proteomics analyses were performed to select different metabolites and proteins. Finally, potential biomarkers were picked out by applying machine learning methods including random forest and stepwise regression and prediction models were constructed by logistic regression.

RESULTS: The presence of metabolites and proteins in peripheral blood plasma was causally associated with both non-small cell lung cancer and PD-L1/PD-1 expression levels. A total of 2 differential metabolites including 5-sulfooxymethylfurfural and Anthranilic acid and 2 differential proteins including Immunoglobulin heavy variable 1-45 and Microfibril-associated glycoprotein 4 were selected as reliable biomarkers. The area under the curve (AUC) of the prediction model built on clinical risks was merely 0.659. The AUC of metabolomics prediction model was 0.977 and the AUC of proteomics was 0.875 while the AUC of the integrative-omics prediction model was 0.955.

CONCLUSIONS: Metabolic and protein biomarkers in peripheral blood both have high efficacy and reliability in the prediction of immunotherapy sensitivity in unresectable stage III and IV non-small cell lung cancer, but validation in larger population-based cohorts is still needed.

PMID:39991162 | PMC:PMC11842339 | DOI:10.3389/fimmu.2025.1479550