ArticleClinical and translational medicine2025
LcProt: Proteomics-based identification of plasma biomarkers for lung cancer multievent, a multicentre study.
Article in Clinical and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- In vivo CAR-cell therapy: current challenges and emerging therapeutic advances.Molecular biomedicine · 2026Review
- Lipidomic signatures as predictive biomarkers for early-onset lung cancer: Identification and development of a risk prediction model.Journal of advanced research · 2026Article
- Liquid biopsy biomarkers for cancer detection, treatment monitoring, and clinical outcome prediction.Frontiers in cell and developmental biology · 2026Review
- Dynamic screening initiation using 16 plasma protein biomarkers with polygenic risk and PLCOm2012: a precision prevention framework for lung cancer.Journal of translational medicine · 2025Article
- Peripheral blood tumor marker levels can indicate the location of lung cancer metastasis.Oncology letters · 2025Article
- Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC.JHEP reports : innovation in hepatology · 2025Article
- LcProt: Proteomics-based identification of plasma biomarkers for lung cancer multievent, a multicentre study.Clinical and translational medicine · 2025Article
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20 authors.
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Abstract
backgroundPlasma 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).
methodsA 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.
resultsFor 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.
conclusionsThis 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.
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