Background: Sjögren’s Disease (SjD) is a chronic autoimmune condition that remains difficult to diagnose due to its heterogeneous clinical presentation, nonspecific symptoms, and the absence of reliable biomarkers. Its substantial clinical and molecular overlap with related autoimmune disorders, particularly Systemic Lupus Erythematosus (SLE), can further contribute to delayed or incorrect diagnosis. Another challenge in SjD management is the lack of effective targeted systemic treatments, with many drugs failing clinical trials. In this work, we followed a comprehensive multi-omics approach, integrating transcriptomic, proteomic, and single cell RNA sequencing to dissect the complex molecular mechanisms driving SjD. This approach identifies robust molecular signatures that can improve diagnosis and guide precision medicine.

Objectives: We aimed to establish a comprehensive multi-omics database for SjD, SLE, and healthy donors to characterize disease-specific molecular signatures, disentangle shared and distinct mechanisms between SjD and SLE, enable molecular stratification of patients, and identify candidate biomarkers for improved diagnosis.

Methods: A total of 236 patients with Sjögren’s Disease (SjD) and 140 patients with Systemic Lupus Erythematosus (SLE), fulfilling the corresponding EULAR/ACR classification criteria, were enrolled in Ethics Committee–approved clinical studies at Hannover Medical School, Germany. In addition, 100 healthy donors were recruited through a separate Ethics Committee–approved study conducted in a dedicated Phase 1 clinical unit following a thorough medical examination to confirm healthy status. Biosample collection and processing were performed in a standardized manner. Serum samples were analyzed using mass spectrometry–based proteomics. Whole-blood transcriptomic profiling was conducted using bulk RNA sequencing with globin and mitochondrial transcript depletion. Single-cell RNA sequencing was performed on fresh whole blood samples with optimized handling to minimize cellular stress and preserve granulocytes. Differential expression analyses for transcriptomic and proteomic data were conducted using standard linear modeling approaches. Support vector machine–based classification with recursive feature elimination was used to distinguish SjD, SLE, and healthy samples and to identify key predictive molecular features.

Results: SjD and SLE exhibited highly similar blood transcriptomic and serum proteomic profiles, both clearly distinct from healthy controls. Genes and proteins associated with inflammatory pathways, type I interferon signaling, neutrophil activation, apoptosis, and known mediators of autoimmune responses were consistently regulated in both patient groups. Using blood-based multi-omics data, machine-learning classifiers were applied to distinguish SjD, SLE, and healthy donors. The model robustly separated patient groups from healthy controls, whereas differentiation between SjD and SLE remained limited, reflecting their shared immune-mediated molecular mechanisms. Recursive feature elimination identified 35 molecular features with the highest predictive value for SjD versus healthy donors. This feature set includes genes previously implicated in SjD pathogenesis, supporting the biological relevance of the approach and providing a basis for the development of novel diagnostic biomarkers.

Conclusions: Multi-omics profiling reveals substantial molecular overlap between SjD and SLE while clearly distinguishing both from healthy individuals. The difficulty in separating SjD from SLE at the molecular level highlights current diagnostic challenges and the need for novel biomarker-driven approaches. The identified SjD-associated molecular features provide a promising foundation for the development of more accurate, molecularly informed diagnostic tools.

REFERENCES: NIL.

Acknowledgments: NIL.

Disclosure of Interests: Hanna Schebet Employee of Evotec International, Ilayda Kokundu Employee of Evotec International, Rene Rex Employee of Evotec International, Melinda Barkhuizen Employee of Evotec International, Kay Schreiter Employee of Evotec International, Kulwadee Thanamit Employee of Evotec International, Helge Stark Employee of Evotec International, Frank Rolfs Employee of Evotec International, Carleen Kluger Employee of Evotec International, Ilya Komarov Employee of Evotec International, Fiona Engelke reports that Hannover Medical School received institutional research funding and cost reimbursement from Evotec for the conduct of this study; no personal financial compensation was received., Alaa Elsaghir reports that Hannover Medical School received institutional research funding and cost reimbursement from Evotec for the conduct of this study; no personal financial compensation was received., Katja Kniesch reports that Hannover Medical School received institutional research funding and cost reimbursement from Evotec for the conduct of this study; no personal financial compensation was received., Torsten Witte reports that Hannover Medical School received institutional research funding and cost reimbursement from Evotec for the conduct of this study; no personal financial compensation was received., Philipp Skroblin Employee of Evotec International, Uwe Andag Employee of Evotec International.