Screening and identification of biomarkers for systemic sclerosis via microarray technology

Area of Science:

• Genomics
• Immunology
• Bioinformatics

Background:

• Systemic sclerosis (SSc) is a complex autoimmune disease with unclear pathogenesis.
• Traditional research methods face challenges in understanding SSc's intricate mechanisms.

Purpose of the Study:

• To identify novel biomarkers for Systemic Sclerosis (SSc) using bioinformatics analysis of gene expression data.
• To explore the functional roles of differentially expressed genes (DEGs) in SSc pathogenesis.

Main Methods:

• Downloaded and analyzed Gene Expression Omnibus (GEO) data for SSc and healthy skin samples.
• Identified differentially expressed genes (DEGs) using GEO2R and performed functional enrichment analysis (GO, KEGG).
• Constructed and analyzed protein-protein interaction (PPI) networks to identify hub genes.

Main Results:

• Identified 106 DEGs between SSc and healthy skin samples.
• Discovered 10 hub genes, including Toll-like receptor 4 (TLR4) and calumenin (CALU), with potential as SSc biomarkers.
• Enrichment analysis revealed involvement in nitric oxide biosynthesis, apoptosis regulation, and cytokine activity.

Conclusions:

• Bioinformatics approaches are effective for identifying SSc biomarkers.
• The identified hub genes, particularly TLR4 and CALU, show promise for SSc diagnosis and understanding disease mechanisms.