Analysis and validation of serum biomarkers in brucellosis patients through proteomics and bioinformatics.

Introduction: This study aims to utilize proteomics, bioinformatics, and machine learning algorithms to identify diagnostic biomarkers in the serum of patients with acute and chronic brucellosis. Methods: Proteomic analysis was conducted on serum samples from patients with acute and chronic brucellosis, as well as from healthy controls. Differential expression analysis was performed to identify proteins with altered expression, while Weighted Gene Co-expression Network Analysis (WGCNA) was applied to detect co-expression modules associated with clinical features of brucellosis. Machine learning algorithms were subsequently used to identify the optimal combination of diagnostic biomarkers. Finally, ELISA was employed to validate the identified proteins. Results: A total of 1,494 differentially expressed proteins were identified, revealing two co-expression modules significantly associated with the clinical characteristics of brucellosis. The Gaussian Mixture Model (GMM) algorithm identified six proteins that were concurrently present in both the differentially expressed and co-expression modules, demonstrating promising diagnostic potential. After ELISA validation, five proteins were ultimately selected. Discussion: These five proteins are implicated in the innate immune processes of brucellosis, potentially associated with its pathogenic mechanisms and chronicity. Furthermore, we highlighted their potential as diagnostic biomarkers for brucellosis. This study further enhances our understanding of brucellosis at the protein level, paving the way for future research endeavors.