Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer
Abstract Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched region...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55594-z |
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| author | Mengmeng Zhao Gang Xue Bingxi He Jiajun Deng Tingting Wang Yifan Zhong Shenghui Li Yang Wang Yiming He Tao Chen Jun Zhang Ziyue Yan Xinlei Hu Liuning Guo Wendong Qu Yongxiang Song Minglei Yang Guofang Zhao Bentong Yu Minjie Ma Lunxu Liu Xiwen Sun Yunlang She Dan Xie Deping Zhao Chang Chen |
| author_facet | Mengmeng Zhao Gang Xue Bingxi He Jiajun Deng Tingting Wang Yifan Zhong Shenghui Li Yang Wang Yiming He Tao Chen Jun Zhang Ziyue Yan Xinlei Hu Liuning Guo Wendong Qu Yongxiang Song Minglei Yang Guofang Zhao Bentong Yu Minjie Ma Lunxu Liu Xiwen Sun Yunlang She Dan Xie Deping Zhao Chang Chen |
| author_sort | Mengmeng Zhao |
| collection | DOAJ |
| description | Abstract Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. The clinic-RadmC yields an area-under-the-curve (AUC) of 0.923 on the external test set, outperforming the single-omics models, and models that only combine clinical features with radiomic, or fragmentomic features in 5mC-enriched regions (p < 0.050 for all). The superiority of the clinic-RadmC maintains well even after adjusting for clinic-radiological variables. Furthermore, the clinic-RadmC-guided strategy could reduce the unnecessary invasive procedures for benign IPLs by 10.9% ~ 35%, and avoid the delayed treatment for lung cancer by 3.1% ~ 38.8%. In summary, our study indicates that the clinic-RadmC provides a more effective and noninvasive tool for optimizing lung cancer diagnoses, thus facilitating the precision interventions. |
| format | Article |
| id | doaj-art-d64a9048c128445ab8737092ff591650 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d64a9048c128445ab8737092ff5916502025-01-05T12:39:10ZengNature PortfolioNature Communications2041-17232025-01-0116111610.1038/s41467-024-55594-zIntegrated multiomics signatures to optimize the accurate diagnosis of lung cancerMengmeng Zhao0Gang Xue1Bingxi He2Jiajun Deng3Tingting Wang4Yifan Zhong5Shenghui Li6Yang Wang7Yiming He8Tao Chen9Jun Zhang10Ziyue Yan11Xinlei Hu12Liuning Guo13Wendong Qu14Yongxiang Song15Minglei Yang16Guofang Zhao17Bentong Yu18Minjie Ma19Lunxu Liu20Xiwen Sun21Yunlang She22Dan Xie23Deping Zhao24Chang Chen25Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineLaboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan UniversityBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineDepartment of Radiology, Zhongshan Hospital, Fudan UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineTailai IncTailai IncLaboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan UniversityDepartment of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical CollegeDepartment of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical CollegeDepartment of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical CollegeDepartment of Thoracic Surgery, Hwa Mei Hospital, Chinese Academy of SciencesDepartment of Thoracic Surgery, Hwa Mei Hospital, Chinese Academy of SciencesDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanchang UniversityDepartment of Thoracic Surgery, The First Hospital of Lanzhou UniversityInstitute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan UniversityDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of MedicineDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineLaboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan UniversityDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of MedicineAbstract Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. The clinic-RadmC yields an area-under-the-curve (AUC) of 0.923 on the external test set, outperforming the single-omics models, and models that only combine clinical features with radiomic, or fragmentomic features in 5mC-enriched regions (p < 0.050 for all). The superiority of the clinic-RadmC maintains well even after adjusting for clinic-radiological variables. Furthermore, the clinic-RadmC-guided strategy could reduce the unnecessary invasive procedures for benign IPLs by 10.9% ~ 35%, and avoid the delayed treatment for lung cancer by 3.1% ~ 38.8%. In summary, our study indicates that the clinic-RadmC provides a more effective and noninvasive tool for optimizing lung cancer diagnoses, thus facilitating the precision interventions.https://doi.org/10.1038/s41467-024-55594-z |
| spellingShingle | Mengmeng Zhao Gang Xue Bingxi He Jiajun Deng Tingting Wang Yifan Zhong Shenghui Li Yang Wang Yiming He Tao Chen Jun Zhang Ziyue Yan Xinlei Hu Liuning Guo Wendong Qu Yongxiang Song Minglei Yang Guofang Zhao Bentong Yu Minjie Ma Lunxu Liu Xiwen Sun Yunlang She Dan Xie Deping Zhao Chang Chen Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer Nature Communications |
| title | Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer |
| title_full | Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer |
| title_fullStr | Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer |
| title_full_unstemmed | Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer |
| title_short | Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer |
| title_sort | integrated multiomics signatures to optimize the accurate diagnosis of lung cancer |
| url | https://doi.org/10.1038/s41467-024-55594-z |
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