Circulating extracellular vesicle long RNA profiling combined with machine learning unveils novel diagnostic...

No clinically useful non-invasive biomarkers have been developed for diagnosis of chronic pancreatitis (CP), and molecular features of CP have not been characterised. Extracellular vesicles (EVs) consisted of abundant RNA species with specialised functions and clinical applications.

Our study aimed to construct a diagnostic model for CP and depict molecular landscape of CP based on EV long RNA (ExLR).

Candidate ExLRs were defined using prespecified expression-quality criteria and complementary discovery-stage screens, and a resampling-based consensus feature selection in the training cohort yielded a five-ExLRs panel for model construction. The ExLRs-based CP diagnostic model (ExLRCPdscore) was further confirmed in another two independent validation cohorts with different controls. To elucidate the biological architecture of CP through ExLR profiling, we integrated ExLR-seq, single-cell data and clinical information.

ExLRCPdscore constructed by random forest demonstrated excellent performance for detecting CP. Importantly, ExLRCPdscore could effectively detect early-stage CP, CP without alarm symptoms, CP without significant imaging findings and CP without risk factors. Using ExLR profiling and phenotypic data, we pinpointed MUC5B⁺ ductal cells exhibiting the strongest correlation with CP and derived an ExLR-based acinar-to-ductal metaplasia (ADM) score as a blood-based transcriptomic proxy of ADM-related programme. Integration of ExLR-seq and clinical information revealed significant associations between ADMscore and clinical characteristics, imaging findings and metabolic sequelae.

Our study is the first to report an ExLRs-based diagnostic model that demonstrates exceptional robustness in differentiating CP from healthy controls and non-pancreatic disease controls. ExLRs offer a promising tool for CP molecular characterisation and pathophysiological quantification.