Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples.

Background: Non-endoscopic cell collection devices combined with biomarkers can detect Barrett's intestinal metaplasia and early oesophageal cancer. However, assays performed on multi-cellular samples lose information about the cell source of the biomarker signal. This cross-sectional study examines whether a bespoke artificial intelligence-based computational pathology tool could ascertain the cellular origin of microRNA biomarkers, to inform interpretation of the disease pathology, and confirm biomarker validity.

Methodological considerations for measuring biofluid-based microRNA biomarkers.

MicroRNAs (miRNAs) are small non-coding RNA that regulate the expression of messenger RNA and are implicated in almost all cellular processes. Importantly, miRNAs can be released extracellularly and are stable in these matrices where they may serve as indicators of organ or cell-specific toxicity, disease, and biological status. There has thus been great enthusiasm for developing miRNAs as biomarkers of adverse outcomes for scientific, regulatory, and clinical purposes.