Identification of biomarkers and immune infiltration associated with sexes in systemic sclerosis: a bioinformatics and machine learning

This study aimed to identify key candidate genes associated with the sexes of patients with systemic sclerosis (SSc). Skin gene expression datasets from patients with SSc and healthy controls (GSE181549 and GSE130955) were retrieved from the GEO database. GSE181549 served as the testing set, while the GSE130955 was used for validation. Differentially expressed genes (DEGs) between SSc and normal skin samples were identified using Limma, stratified by sex in the GSE181549. Bioinformatics analyses were performed to evaluate the DEGs, and machine learning techniques were applied to identify sex-specific. In male samples from the testing set, 80 DEGs were upregulated and 20 were downregulated, while in female samples, 94 DEGs were upregulated and 12 were downregulated. Functional enrichment analysis indicated that these DEGs are potentially implicated in sex-specific SSc pathogenesis. Machine learning identified 10 marker genes in males samples and 12 in females. Immune infiltration analysis revealed a significant increase in M0 and M1 macrophages and a decrease in M2 macrophages and resting dendritic cells in male SSc samples. In female SSc samples, memory B cells, plasma cells and M1 macrophages were significantly elevated, whereas resting CD4 memory T cells were notably reduced. Patients with SSc exhibit distinct sex-specific differences in DEGs, marker genes, and immune infiltration profiles.