Henry, Michael, Meleady, Paula ORCID: 0000-0001-5306-310X, Kane, Laura E., Mellotte, Gregory S., Mylod, Eimear, Dowling, Paul, Marcone, Simone, Scaife, Caitriona, Kenny, Elaine M., Ridgway, Paul F., MacCarthy, Finbar, Conlon, Kevin C., Ryan, Barbara M. and Maher, Stephen G.
(2025)
Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development.
Scientific Reports, 15
(129).
ISSN 2045-2322
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 crossbiofluid 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.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Subjects: | Biological Sciences > Biotechnology Humanities > Biological Sciences > Biotechnology |
DCU Faculties and Centres: | Research Institutes and Centres > National Institute for Cellular Biotechnology (NICB) |
Publisher: | Nature Publishing Group |
Official URL: | https://www.nature.com/articles/s41598-024-83742-4 |
Copyright Information: | Authors |
ID Code: | 30662 |
Deposited On: | 16 Jan 2025 14:55 by Gordon Kennedy . Last Modified 16 Jan 2025 14:55 |
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