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

Clin Transl Med. 2025 Feb;15(2):e70225. doi: 10.1002/ctm2.70225.

ABSTRACT

BACKGROUND: Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

METHODS: To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

RESULTS: Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

CONCLUSIONS: With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

KEYPOINTS: Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

PMID:39909829 | PMC:PMC11798665 | DOI:10.1002/ctm2.70225

Clin Transl Med. 2025 Jan;15(1):e70160. doi: 10.1002/ctm2.70160.

ABSTRACT

BACKGROUND: Plasma protein has gained prominence in the non-invasive predicting of lung cancer. We utilised Zeolite Zotero NaY-based plasma proteomics to investigate its potential for multiple event predicting, including lung cancer diagnosis (task #1), lymph node metastasis detection (task #2) and tumour‒node‒metastasis (TNM) staging (task #3).

METHODS: A total of 4703 plasma proteins were quantified from 241 participants based on a prospective cohort of 2757 participants. An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. Random forest was used for multitask model construction based on the key proteins. Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm.

RESULTS: For task #1, 10 proteins panel showed an AUC of .87 (.77‒.97) in the external validation. After integrating clinical factors, a significant increase diagnostic accuracy was observed with AUC of .91 (.85‒.98). For task #2, nine proteins panel achieved an AUC of .88 (.80‒.96), integration model showed an increase diagnostic accuracy with AUC of .90 (.85‒.97). For task #3, 10 proteins panel showed an AUC of .88 (.74‒.96) for stage I, .92 (.84‒.97) for stage II, .88 (.76‒.96) for stage III and .99 (.98‒.99) for stage IV in the integration model.

CONCLUSIONS: This study comprehensively profiled the NaY-based plasma proteome biomarker, laying the foundation for a high-performance blood test for predicting multiple events in lung cancer.

KEY POINTS: Our study developed an innovative nanomaterial, Zeolite NaY, which addressed the masking effect and improved the depth of the proteome. The performance of NaY-based plasma proteomics as a preclinical diagnostic tool was validated through both internal and external cohort. Furthermore, we explored the different patterns of plasma protein changes during the progression of lung cancer and used the explanations method to elucidate the roles of proteins in the multitask predictive model.

PMID:39783847 | PMC:PMC11714244 | DOI:10.1002/ctm2.70160