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 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, R

SUBMITTER: Kane LE 

PROVIDER: S-EPMC11696309 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature