# Comprehensive analysis of molecular immunological characteristics and potential biomarkers in brucellosis

## TL;DR

This study identifies potential biomarkers for brucellosis by analyzing gene expression and immune responses, offering new insights for diagnosis and treatment.

## Contribution

The study introduces six novel hub genes as potential diagnostic biomarkers for brucellosis using machine learning and co-expression network analysis.

## Key findings

- 264 differentially expressed genes were identified, enriched in pathways like cell cycle and immune response.
- Two gene co-expression modules were significantly linked to brucellosis clinical traits.
- Six hub genes showed strong diagnostic performance and are involved in key immune and inflammatory processes.

## Abstract

This study aims to explore potential biological biomarkers for brucellosis by integrating transcriptomic profiling and bioinformatics-driven approaches.

Differentially expressed genes (DEGs) associated with acute and chronic brucellosis were identified using transcriptomic data from the Gene Expression Omnibus (GEO). Functional annotation and pathway enrichment analysis of DEGs were performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted Gene Co-expression Network Analysis (WGCNA) was applied to construct gene co-expression modules, followed by screening of modules significantly correlated with disease phenotypes. Notably, a multi-model machine learning framework was employed for systematic screening, cross-validation, and validation of diagnostically relevant biomarkers—ensuring robustness and generalizability of the findings.

A total of 103 brucellosis patients and 46 healthy controls with whole blood transcriptomic profiles were included. Comparative analysis identified 264 DEGs, which were predominantly enriched in mitotic nuclear division, chromosome segregation, nucleocytoplasmic transport, cell cycle regulation, and cytokine-cytokine receptor interaction pathways—providing novel insights into the molecular pathogenesis of brucellosis. Immune infiltration profiling revealed that brucellosis progression was positively correlated with CD8+ T cells, follicular helper T cells, and resting NK cells—highlighting previously underappreciated immune regulatory mechanisms. Two co-expression modules were significantly associated with brucellosis clinical traits through WGCNA. Cross-validation using machine learning algorithms (LASSO, SVM, random forest) prioritized six overlapping hub genes: RTP5, KIF19, CDKN2A, RCAN2, GLB1L3, and IL12RB2. Receiver Operating Characteristic (ROC) curve analysis demonstrated robust diagnostic performance, supporting their potential as combinatorial biomarkers for brucellosis detection.

These novel hub genes are closely implicated in inflammatory responses, neutrophil regulation, and B cell receptor signaling pathways—key processes underlying brucellosis pathogenesis that have not been previously targeted for diagnostic biomarker development. This work not only enhances our understanding of brucellosis biology but also lays a critical foundation for the development of non-invasive, accurate diagnostic tools and targeted therapeutic strategies—filling a significant gap in current brucellosis management.