Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning

TL;DR

This study uses bioinformatics and machine learning to identify glucocorticoid-related genes and immune patterns in systemic lupus erythematosus, revealing potential biomarkers and therapeutic targets.

Contribution

The study introduces novel insights into SLE by identifying PTX3, DYSF, and F2R as potential biomarkers and F2R as a possible therapeutic target.

Findings

SLE patients were divided into two subclusters with 2,681 differentially expressed genes.

PTX3, DYSF, and F2R were identified as hub genes with diagnostic potential.

Monocyte infiltration was found to play a significant role in SLE progression.

Abstract

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant impacts on patients’ quality of life and poses a substantial economic burden on society.

This study aimed to elucidate the molecular mechanisms underlying SLE by analyzing glucocorticoid-related genes (GRGs) expression profiles.

We examined the expression profiles of GRGs in SLE and performed consensus clustering analysis to identify stable patient clusters. We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to investigate biological functional differences, and we also conducted CIBERSORTx to estimate the number of immune cells. Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF) algorithms to screen for hub genes. We then validated the expression of these hub genes and constructed nomograms for further validation. Moreover, we employed single-sample Gene Set Enrichment Analysis (ssGSEA) to analyze immune infiltration. We also constructed an RNA-binding protein (RBP)-mRNA network and conducted drug sensitivity analysis along with molecular docking studies.

Patients with SLE were divided into two subclusters, revealing a total of 2,681 DEGs. Among these, 1,458 genes were upregulated, while 1,223 were downregulated in cluster_1. GSVA showed significant changes in the pathways associated with cluster_1. Immune infiltration analysis revealed high levels of monocyte in all samples, with greater infiltration of various immune cells in cluster_1. A comparison of SLE patients to control subjects identified 269 DEGs, which were enriched in several pathways. Hub genes, including PTX3, DYSF and F2R, were selected through LASSO and RF methods, resulting in a well-performing diagnostic model. Drug sensitivity and docking studies suggested F2R as a potential new therapeutic target.

PTX3, DYSF and F2R are potentially linked to SLE and are proposed as new molecular markers for its onset and progression. Additionally, monocyte infiltration plays a crucial role in advancing SLE.

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