Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development
Abstract Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83742-4 |
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| author | Laura E. Kane Gregory S. Mellotte Eimear Mylod Paul Dowling Simone Marcone Caitriona Scaife Elaine M. Kenny Michael Henry Paula Meleady Paul F. Ridgway Finbar MacCarthy Kevin C. Conlon Barbara M. Ryan Stephen G. Maher |
| author_facet | Laura E. Kane Gregory S. Mellotte Eimear Mylod Paul Dowling Simone Marcone Caitriona Scaife Elaine M. Kenny Michael Henry Paula Meleady Paul F. Ridgway Finbar MacCarthy Kevin C. Conlon Barbara M. Ryan Stephen G. Maher |
| author_sort | Laura E. Kane |
| collection | DOAJ |
| description | Abstract Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC = 0.806. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC = 0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC = 0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of patients with pancreatic cystic lesions. |
| format | Article |
| id | doaj-art-e5da0fa21ee146faab413210368b6a72 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-e5da0fa21ee146faab413210368b6a722025-01-05T12:14:16ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-83742-4Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer developmentLaura E. Kane0Gregory S. Mellotte1Eimear Mylod2Paul Dowling3Simone Marcone4Caitriona Scaife5Elaine M. Kenny6Michael Henry7Paula Meleady8Paul F. Ridgway9Finbar MacCarthy10Kevin C. Conlon11Barbara M. Ryan12Stephen G. Maher13Department of Surgery, Trinity St. James’s Cancer Institute, Trinity Translational Medicine Institute, Trinity College Dublin, St. James’s HospitalDepartment of Gastroenterology, Tallaght University HospitalDepartment of Surgery, Trinity St. James’s Cancer Institute, Trinity Translational Medicine Institute, Trinity College Dublin, St. James’s HospitalDepartment of Biology, Maynooth UniversityDepartment of Surgery, Trinity St. James’s Cancer Institute, Trinity Translational Medicine Institute, Trinity College Dublin, St. James’s HospitalMass Spectrometry Facility, Conway Institute of Biomolecular and Biomedical Research, University College DublinELDA BiotechNational Institute for Cellular Biotechnology, Dublin City UniversityNational Institute for Cellular Biotechnology, Dublin City UniversityDepartment of Surgery, Centre for Pancreatico-Biliary Diseases, Trinity College Dublin, St. James’s HospitalDepartment of Clinical Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, St. James’s HospitalDepartment of Surgery, School of Medicine, Trinity College DublinDepartment of Gastroenterology, Tallaght University HospitalDepartment of Surgery, Trinity St. James’s Cancer Institute, Trinity Translational Medicine Institute, Trinity College Dublin, St. James’s HospitalAbstract Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC = 0.806. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC = 0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC = 0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of patients with pancreatic cystic lesions.https://doi.org/10.1038/s41598-024-83742-4Pancreatic cancerPancreatic cystic lesionRisk stratificationBiomarkerMulti-omics |
| spellingShingle | Laura E. Kane Gregory S. Mellotte Eimear Mylod Paul Dowling Simone Marcone Caitriona Scaife Elaine M. Kenny Michael Henry Paula Meleady Paul F. Ridgway Finbar MacCarthy Kevin C. Conlon Barbara M. Ryan Stephen G. Maher Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development Scientific Reports Pancreatic cancer Pancreatic cystic lesion Risk stratification Biomarker Multi-omics |
| title | Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development |
| title_full | Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development |
| title_fullStr | Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development |
| title_full_unstemmed | Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development |
| title_short | Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development |
| title_sort | multi omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development |
| topic | Pancreatic cancer Pancreatic cystic lesion Risk stratification Biomarker Multi-omics |
| url | https://doi.org/10.1038/s41598-024-83742-4 |
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