مقالات پذیرفته شده در نهمین کنگره بین المللی زیست پزشکی
Transcriptomic Profiling Identifies Candidate Biomarkers of Disease Severity in Systemic Lupus Erythematosus
Transcriptomic Profiling Identifies Candidate Biomarkers of Disease Severity in Systemic Lupus Erythematosus
Maryam Heydari,1,*Shirin Farivar,2
1. Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, 1983969411, Evin, Tehran, Iran 2. Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, 1983969411, Evin, Tehran, Iran
Introduction: Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder with diverse clinical manifestations and variable severity. Patients may experience mild disease with cutaneous or musculoskeletal involvement, while others develop severe complications affecting organs such as the kidneys, central nervous system, or hematological systems. Current diagnostic tools and serological markers do not reliably predict progression from mild to severe disease, underscoring the need for molecular biomarkers that can guide early intervention and tailored therapy.
Transcriptomic profiling using RNA sequencing (RNA-seq) provides a powerful strategy to identify genome-wide expression changes that underlie disease heterogeneity. By distinguishing gene expression signatures between mild and severe forms, it is possible to uncover pathways that drive progression and identify candidate biomarkers with translational potential.
Methods: RNA-seq data from the public dataset GSE228066 were analyzed, including blood samples from patients with mild and severe SLE (n=5 each). Raw reads were assessed with FastQC and processed with Trimmomatic to remove adapters and low-quality sequences. Clean reads were aligned to the human reference genome (GRCh38) using HISAT2, with quality metrics summarized in MultiQC. Gene expression was quantified using FeatureCounts, and differential expression was assessed with DESeq2. Genes were considered significant based on adjusted p-value < 0.05 and |log2 fold change| > 4.
To explore functional relevance, significantly altered transcripts were subjected to enrichment analysis using enrichR (KEGG pathways and Gene Ontology categories). Protein–protein interaction (PPI) networks were generated with STRING, and clusters of related genes were identified using MCODE in Cytoscape. Hub genes were prioritized with CytoHubba according to topological parameters such as degree, closeness, betweenness, and MCC scores.
Results: Transcriptomic profiling revealed clear differences in expression patterns between mild and severe SLE. A distinct set of differentially expressed genes was identified, many of which clustered into tightly connected sub-networks, suggesting coordinated molecular programs underlying severity. Network topology analysis further highlighted central nodes that may act as critical regulators of disease progression.
Functional enrichment analysis revealed consistent dysregulation of biological processes linked to immune activation and inflammatory signaling. Pathways associated with cytokine signaling, antigen processing, and immune cell communication were particularly enriched in severe SLE, reflecting intensified immune dysregulation. Beyond these immune-related signatures, broader transcriptomic differences were detected that distinguish severe from mild disease.
Conclusion: This study demonstrates that transcriptomic profiling of peripheral blood can effectively differentiate mild from severe SLE, identifying molecular signatures that provide new insights into disease biology. The integration of differential expression, network-based clustering, and enrichment analyses uncovered candidate biomarkers that may contribute to improved disease stratification.
The implications are twofold: first, expression-based biomarkers have potential as tools for early identification of patients at risk of severe disease, allowing clinicians to adapt management strategies proactively. Second, the implicated pathways highlight possible therapeutic targets at the intersection of immune dysregulation and cellular regulatory mechanisms.
Although this study analyzed a limited number of samples, the consistency of the transcriptomic distinctions underscores the promise of such approaches. Validation in larger cohorts, and integration with proteomic and clinical data, will be essential to establish robust biomarker panels and advance precision medicine strategies in SLE.