Predicting progression from pancreatic cyst to pancreatic cancer

Reference: September 2025 | Issue 9 | Vol 11 | Page 29

Patients with pancreatic cancer continue to face a poor prognosis, with just 13 per cent of patients diagnosed with pancreatic cancer surviving for five years or more after initial diagnosis. In Ireland, there are approximately 900 new cases of pancreatic cancer per year and 820 related deaths. Survival rates remain poor due to the vague nature of symptoms associated with early-stage pancreatic cancer, and subsequently the late-stage of the disease at diagnosis.

Researchers from Trinity College Dublin are using fluid from pancreatic cystic lesions to tackle the crucial issue of identifying patients at high risk of developing pancreatic cancer. The team integrated data from different omic panels and across biofluids to produce a biomarker panel that performs with high accuracy, showing promise for the risk stratification of patients with pancreatic cystic lesions.

The team extensively profiled the proteome and transcriptome of patient pancreatic cyst fluid (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.

An 11-feature multi-omic panel in pancreatic cyst fluid produced an area under the ROC curve (AUC) of 0.806. The panel included PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, and miR-216b-5p. A 13-feature multi-omic serum panel that included SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, and miR-6782-5p, produced an AUC of 0.824.

Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel, with an AUC of 0.970. Included in this panel were S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, and miR-3180-3p.

While these data remain to be validated in a larger, independent cohort, the biomarker panel could have a profound impact on identifying patients at risk of developing pancreatic cancer at an earlier stage.

The group’s work generated four large datasets that are now publicly available for download and use. Together, these datasets can be combined into a larger and unique dataset that has been unavailable online until now and can be used for a myriad of research purposes, such as the development of new treatments for pancreatic cancer patients or the identification of key biological pathways involved in pancreatic cystic lesion development or progression to pancreatic cancer.

The research has been spearheaded by Dr Laura Kane, Research Ireland Postdoctoral Research Fellow; Prof Barbara Ryan, Consultant Gastroenterologist; and Prof Stephen Maher, Professor in Translational Oncology, TCD.

Presenting the findings, the authors said: “The results reported here not only describe the dysregulation of proteins and miRNAs in pancreatic disease that have not previously been seen, but also demonstrate their potential utility as biomarkers of patient pancreatic cancer risk in this pancreatic cystic lesion cohort. Promising multi-omic panels have been identified in both the pancreatic cyst fluid and the serum that have the potential to classify patients based on their risk of pancreatic cancer with high accuracy.

“Using novel CombiROC software, these two multi-omic panels were reduced and integrated to create a cross-biofluid multi-omic panel that could stratify patients with improved accuracy compared to either multi-omic panel alone. This research not only highlights promising novel biomarkers of patient pancreatic cancer risk stratification, but provides a unique methodology for the generation of biomarker panels across biological samples.”

Author Bios

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