A Multi-omics and Machine Learning Framework Identifies Plasma SBDS as a Causal Biomarker and Therapeutic Target in Primary Sclerosing Cholangitis
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background and Aims: Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease. Its molecular etiology remains poorly defined, hindering the development of mechanism-based diagnostics and therapies. Therefore, this study aimed to identify key molecular drivers and causal biomarkers of PSC by integrating transcriptomics, machine learning, and genetic causal inference. Methods: We deployed a computational framework combining transcriptomics, network biology, machine learning, and genetic causal inference. Peripheral blood transcriptomes from PSC patients and controls were analyzed to identify disease-associated modules. Candidate genes were refined via protein-protein interaction networks and a multi-algorithm machine learning screen. Causal inference was performed using two-sample Mendelian randomization, integrating plasma protein quantitative trait loci with PSC genome-wide association study summary statistics. Results: = 0.001). Sensitivity analyses supported the validity of the Mendelian randomization assumptions. Conclusions: Our study establishes disrupted ribosome homeostasis as a causal pathway in PSC and nominates plasma SBDS as a high-confidence diagnostic biomarker and therapeutic target. The integrative framework provides a generalizable strategy for discovering causal biomarkers in complex diseases.
Institutions: Capital Medical University, Beijing YouAn Hospital
Language: English
Topic: Liver Diseases and Immunity
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Award: G395045825
OpenAlex ID: W7140028293
FWCI:
Cites:
Cited by:
Related to: