Transcriptomic analysis of serum in patients with pancreatic cysts reveals novel biomarkers of pancreatic cancer risk

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=0.80. 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 these patients. Neat serum samples from patients with pancreatic cystic lesions (n=30) were processed for HTG Molecular miRNA Whole Transcriptome Sequencing by HTG Molecular. Patients were separated into low-risk (n=15) and high-risk (n=15) of pancreatic cancer categories using the 2018 European evidence-based guidlines of cyctic neoplasms. Differential expression analysis, downstream bioinformatic interrogation and multi-omic integration was used to identify novel biomarkers of pancreatic cancer risk. *************************************************************** These data were generated with HTG Molecular Diagnostics Inc. using their proprietary quantitative nuclease protection assay (qNPA) chemistry with a next generation sequencing platform. The data raw datafiles generated cannot be analysed with standard RNA-seq analysis tools. ***************************************************************