Back

Salivary Extracellular Vesicle RNA Profiling Reveals Biomarkers for Sjogrens

Chakrabortty, S. K.; Xing, S.; George, A.; Sawicki, B.; Lang, S.; Nguyen, S.; Cole, T. J.; Mitsock, E.; Ray, C. J.; Zoukhri, D.; Singh, M. L.; Chatzis, L.; Goules, A.; Saridaki, M.-I.; Gowrisankar, S.; Tzioufas, A. G.; Papas, A.; Skog, J. K.

2025-08-07 genomics
10.1101/2025.08.05.668729 bioRxiv
Show abstract

Sjogrens is a chronic autoimmune disease affecting exocrine glands and is subclassified into SSA-positive (SSA+) and SSA-negative (SSA-) subtypes, with a complex diagnostic journey and an average diagnostic delay of almost 4 years. While SSA+ cases can be detected via serological testing, current assays lack specificity. For SSA-patients, no non-invasive diagnostic tools exist, and diagnosis often requires invasive lip biopsy. A saliva-based liquid biopsy capable of diagnosing both subtypes is therefore of high clinical interest. However, saliva poses challenges due to its abundant oral microbiome, which complicates unbiased biomarker discovery. In this study, we present a novel RNA sequencing workflow that efficiently depletes microbial content, enabling deep profiling of long RNAs within salivary extracellular vesicles (EVs). This approach identified both known and novel RNA biomarkers capable of diagnosing SSA+ and SSA-subtypes with high sensitivity and specificity. Moreover, we uncovered distinct RNA signatures that allow molecular stratification of Sjogrens subtypes. Pathway analysis in SSA+ cases revealed enrichment of immune and glandular pathways consistent with prior tissue-based studies, supporting the utility of salivary EVs as a non-invasive surrogate for tissue biopsy. Importantly, our data provides new molecular insights into the under-characterized SSA-subtype, laying the foundation for future mechanistic studies and facilitating their broader inclusion in clinical trials.

Matching journals

The top 16 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.