A transcriptomic dataset comparing systemic sclerosis and systemic lupus erythematosus: differential gene expression, hub genes, and pathway enrichment

This dataset provides whole-transcriptome RNA sequencing (RNA-seq) data from patients with systemic sclerosis (SSc) and systemic lupus erythematosus (SLE), two autoimmune diseases that share overlapping immune features but also display distinct molecular signatures. Peripheral blood mononuclear cells (PBMCs) were collected from 10 SSc and 24 SLE patients who met the ACR/EULAR classification criteria, and total RNA was sequenced on the Illumina NovaSeq 6000 platform (150 bp paired-end).

The dataset includes:
Raw FASTQ files: quality-checked reads with adapters removed.
Processed expression matrices: normalized gene counts for all quantified genes.
Differential expression results: complete tables of significantly up- and down-regulated genes between SSc and SLE, generated using DESeq2.
Functional enrichment analysis: KEGG, Gene Ontology (GO), and Disease Ontology (DO) enrichment results highlighting pathways associated with immune dysregulation, fibrosis, and interferon signaling.
Hub gene and network analysis: co-expression networks and protein–protein interaction (PPI) data identifying potential key regulators, such as AP3D1, USP47, CUX1, and SETD1B.
Clinical metadata: anonymized patient demographic and clinical characteristics, including disease duration, organ involvement, and autoantibody profiles.
The value of this dataset lies in enabling comparative analyses between SSc and SLE at the transcriptomic level. Researchers can reuse these data to explore common and disease-specific molecular mechanisms, discover candidate biomarkers, develop predictive machine learning models, and identify potential therapeutic targets for autoimmune diseases.

All data are provided in both raw and processed forms, facilitating reproducibility and integration with other multi-omics resources.