Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer
Pancreatic cancer (PC) is a highly aggressive malignancy in humans, where early diagnosis significantly improves patient outcomes. However, effective methods for accurate and early detection remain limited. In this multiethnic study involving human subjects, we developed a liquid biopsy signature ba...
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eLife Sciences Publications Ltd
2025-08-01
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| Online Access: | https://elifesciences.org/articles/103737 |
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| author | Jianying Xu Wenjie Shi Yi Zhu Chao Zhang Julia Nagelschmitz Maximilian Doelling Sara Al-Madhi Ujjwal Mukund Mahajan Maciej Pech Georg Rose Roland Siegfried Croner Guoliang Zheng Christoph Kahlert Ulf Dietrich Kahlert |
| author_facet | Jianying Xu Wenjie Shi Yi Zhu Chao Zhang Julia Nagelschmitz Maximilian Doelling Sara Al-Madhi Ujjwal Mukund Mahajan Maciej Pech Georg Rose Roland Siegfried Croner Guoliang Zheng Christoph Kahlert Ulf Dietrich Kahlert |
| author_sort | Jianying Xu |
| collection | DOAJ |
| description | Pancreatic cancer (PC) is a highly aggressive malignancy in humans, where early diagnosis significantly improves patient outcomes. However, effective methods for accurate and early detection remain limited. In this multiethnic study involving human subjects, we developed a liquid biopsy signature based on extracellular vesicle (EV)-derived microRNAs (miRNAs) linked to radiomics features extracted from patients’ tumor imaging. We integrated eight datasets containing clinical records, imaging data of benign and malignant pancreatic lesions, and small RNA sequencing data from plasma-derived EVs of PC patients. Radiomics features were extracted and analyzed using the limma package, with feature selection conducted via the Boruta algorithm and model construction through Least Absolute Shrinkage and Selection Operator regression. Radiomics-related low-abundance EV miRNAs were identified via weighted gene co-expression network analysis and validated for diagnostic accuracy using 10 machine-learning algorithms. Three key EV miRNAs were found to robustly distinguish malignant from benign lesions. Subsequent molecular clustering of these miRNAs and their predicted targets identified two PC subtypes, with distinct survival profiles and therapeutic responses. Specifically, one cluster was associated with prolonged overall survival and higher predicted sensitivity to immunotherapy, while the other indicated high-risk tumors potentially amenable to targeted drug interventions. This radiogenomic EV miRNA signature in human plasma represents a promising non-invasive biomarker for early diagnosis and molecular subtyping of PC, with potential implications for precision treatment strategies. |
| format | Article |
| id | doaj-art-1c4dc52e22e84e4295e6d7009b4554a1 |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-1c4dc52e22e84e4295e6d7009b4554a12025-08-20T03:59:36ZengeLife Sciences Publications LtdeLife2050-084X2025-08-011410.7554/eLife.103737Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancerJianying Xu0https://orcid.org/0009-0007-1924-8400Wenjie Shi1https://orcid.org/0009-0001-7345-579XYi Zhu2Chao Zhang3Julia Nagelschmitz4Maximilian Doelling5Sara Al-Madhi6Ujjwal Mukund Mahajan7Maciej Pech8Georg Rose9Roland Siegfried Croner10Guoliang Zheng11Christoph Kahlert12https://orcid.org/0000-0003-4124-7918Ulf Dietrich Kahlert13https://orcid.org/0000-0002-6021-1841Department of Medicine II, Hospital of the LMU, Munich, GermanyMolecular and Experimental Surgery, Clinic for General-, Visceral -, Vascular- and Transplantation Surgery, Medical Faculty and University Hospital Magdeburg, Otto-von-Guericke University, Magdeburg, GermanyDepartment of Gastroenterological Surgery, The Affiliated Hospital of Jiaxing University, Jiaxing, ChinaDepartment of Radiology, The First Affiliated Hospital of Wannan Medical College, Wannan, ChinaMolecular and Experimental Surgery, Clinic for General-, Visceral -, Vascular- and Transplantation Surgery, Medical Faculty and University Hospital Magdeburg, Otto-von-Guericke University, Magdeburg, GermanyMolecular and Experimental Surgery, Clinic for General-, Visceral -, Vascular- and Transplantation Surgery, Medical Faculty and University Hospital Magdeburg, Otto-von-Guericke University, Magdeburg, GermanyMolecular and Experimental Surgery, Clinic for General-, Visceral -, Vascular- and Transplantation Surgery, Medical Faculty and University Hospital Magdeburg, Otto-von-Guericke University, Magdeburg, GermanyDepartment of Medicine II, Hospital of the LMU, Munich, GermanyClinic for Radiology and Nuclear Medicine, University Hospital Magdeburg, Magdeburg, GermanyResearch Campus Stimulate, Otto von Guericke University Magdeburg, Magdeburg, GermanyMolecular and Experimental Surgery, Clinic for General-, Visceral -, Vascular- and Transplantation Surgery, Medical Faculty and University Hospital Magdeburg, Otto-von-Guericke University, Magdeburg, GermanyDepartment of Gastric Surgery, Cancer Hospital of China Medical University (Liaoning Cancer Hospital and Institute), Shenyang, ChinaDepartment of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, GermanyMolecular and Experimental Surgery, Clinic for General-, Visceral -, Vascular- and Transplantation Surgery, Medical Faculty and University Hospital Magdeburg, Otto-von-Guericke University, Magdeburg, Germany; Research Campus Stimulate, Otto von Guericke University Magdeburg, Magdeburg, GermanyPancreatic cancer (PC) is a highly aggressive malignancy in humans, where early diagnosis significantly improves patient outcomes. However, effective methods for accurate and early detection remain limited. In this multiethnic study involving human subjects, we developed a liquid biopsy signature based on extracellular vesicle (EV)-derived microRNAs (miRNAs) linked to radiomics features extracted from patients’ tumor imaging. We integrated eight datasets containing clinical records, imaging data of benign and malignant pancreatic lesions, and small RNA sequencing data from plasma-derived EVs of PC patients. Radiomics features were extracted and analyzed using the limma package, with feature selection conducted via the Boruta algorithm and model construction through Least Absolute Shrinkage and Selection Operator regression. Radiomics-related low-abundance EV miRNAs were identified via weighted gene co-expression network analysis and validated for diagnostic accuracy using 10 machine-learning algorithms. Three key EV miRNAs were found to robustly distinguish malignant from benign lesions. Subsequent molecular clustering of these miRNAs and their predicted targets identified two PC subtypes, with distinct survival profiles and therapeutic responses. Specifically, one cluster was associated with prolonged overall survival and higher predicted sensitivity to immunotherapy, while the other indicated high-risk tumors potentially amenable to targeted drug interventions. This radiogenomic EV miRNA signature in human plasma represents a promising non-invasive biomarker for early diagnosis and molecular subtyping of PC, with potential implications for precision treatment strategies.https://elifesciences.org/articles/103737liquid biopsypancreatic cancerradiomicsmachine learningextracellular vesiclesmicroRNA |
| spellingShingle | Jianying Xu Wenjie Shi Yi Zhu Chao Zhang Julia Nagelschmitz Maximilian Doelling Sara Al-Madhi Ujjwal Mukund Mahajan Maciej Pech Georg Rose Roland Siegfried Croner Guoliang Zheng Christoph Kahlert Ulf Dietrich Kahlert Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer eLife liquid biopsy pancreatic cancer radiomics machine learning extracellular vesicles microRNA |
| title | Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer |
| title_full | Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer |
| title_fullStr | Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer |
| title_full_unstemmed | Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer |
| title_short | Human multiethnic radiogenomics reveals low-abundancy microRNA signature in plasma-derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer |
| title_sort | human multiethnic radiogenomics reveals low abundancy microrna signature in plasma derived extracellular vesicles for early diagnosis and molecular subtyping of pancreatic cancer |
| topic | liquid biopsy pancreatic cancer radiomics machine learning extracellular vesicles microRNA |
| url | https://elifesciences.org/articles/103737 |
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