LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study
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...
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2025-01-01
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| Series: | Clinical and Translational Medicine |
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| Online Access: | https://doi.org/10.1002/ctm2.70160 |
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| author | Hengrui Liang Runchen Wang Ran Cheng Zhiming Ye Na Zhao Xiaohong Zhao Ying Huang Zhanpeng Jiang Wangzhong Li Jianqi Zheng Hongsheng Deng Yu Jiang Yuechun Lin Yun Yan Lei Song Jie Li Xin Xu Wenhua Liang Jun Liu Jianxing He |
| author_facet | Hengrui Liang Runchen Wang Ran Cheng Zhiming Ye Na Zhao Xiaohong Zhao Ying Huang Zhanpeng Jiang Wangzhong Li Jianqi Zheng Hongsheng Deng Yu Jiang Yuechun Lin Yun Yan Lei Song Jie Li Xin Xu Wenhua Liang Jun Liu Jianxing He |
| author_sort | Hengrui Liang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-67c0c169961047029f2f7f09006af738 |
| institution | Kabale University |
| issn | 2001-1326 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Medicine |
| spelling | doaj-art-67c0c169961047029f2f7f09006af7382025-01-25T04:00:38ZengWileyClinical and Translational Medicine2001-13262025-01-01151n/an/a10.1002/ctm2.70160LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre studyHengrui Liang0Runchen Wang1Ran Cheng2Zhiming Ye3Na Zhao4Xiaohong Zhao5Ying Huang6Zhanpeng Jiang7Wangzhong Li8Jianqi Zheng9Hongsheng Deng10Yu Jiang11Yuechun Lin12Yun Yan13Lei Song14Jie Li15Xin Xu16Wenhua Liang17Jun Liu18Jianxing He19Department of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Proteomics Tianjin Key Laboratory of Clinical Multi‐Omics Tianjin ChinaDepartment of Proteomics Tianjin Key Laboratory of Clinical Multi‐Omics Tianjin ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Proteomics Tianjin Key Laboratory of Clinical Multi‐Omics Tianjin ChinaDepartment of Proteomics Tianjin Key Laboratory of Clinical Multi‐Omics Tianjin ChinaDepartment of Proteomics Tianjin Key Laboratory of Clinical Multi‐Omics Tianjin ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaDepartment of Thoracic Surgery and Oncology the First Affiliated Hospital of Guangzhou Medical University State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease Guangzhou ChinaABSTRACT 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.https://doi.org/10.1002/ctm2.70160lung cancermultitaskplasma proteomicszeolite NaY |
| spellingShingle | Hengrui Liang Runchen Wang Ran Cheng Zhiming Ye Na Zhao Xiaohong Zhao Ying Huang Zhanpeng Jiang Wangzhong Li Jianqi Zheng Hongsheng Deng Yu Jiang Yuechun Lin Yun Yan Lei Song Jie Li Xin Xu Wenhua Liang Jun Liu Jianxing He LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study Clinical and Translational Medicine lung cancer multitask plasma proteomics zeolite NaY |
| title | LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study |
| title_full | LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study |
| title_fullStr | LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study |
| title_full_unstemmed | LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study |
| title_short | LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study |
| title_sort | lcprot proteomics based identification of plasma biomarkers for lung cancer multievent a multicentre study |
| topic | lung cancer multitask plasma proteomics zeolite NaY |
| url | https://doi.org/10.1002/ctm2.70160 |
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