Integrated bioinformatics analysis and experimental validation of exosome-related gene signature in steroid-induced osteonecrosis of the femoral head

Abstract

Background: Steroid-induced osteonecrosis of the femoral head (SIONFH) is a universal hip articular disease and is very hard to perceive at an early stage. The understanding of the pathogenesis of SIONFH is still limited, and the identification of efficient diagnostic biomarkers is insufficient. This research aims to recognize and validate the latent exosome-related molecular signature in SIONFH diagnosis by employing bioinformatics to investigate exosome-related mechanisms in SIONFH.

Method: The GSE123568 and GSE74089 datasets were employed to conduct differentially expressed genes (DEGs) analysis, and the GSE123568 dataset was subjected to perform weighted genes co-expression network analysis (WGCNA). The exosome-related genes (ERGs) were retrieved from the GeneCards database. We identified differentially expressed exosome-related genes (DEERGs) between healthy controls (HC) and SIONFH patients, and a consensus clustering analysis was then implemented to group the SIONFH patients. The CIBERSORT was implemented to calculate the immune cell infiltration. Gene Set Variation Analysis (GSVA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to investigate latent enriched pathways. In addition, machine-learning algorithms were applied to refine the DEERGs. Ultimately, we verified the diagnostic significance and expression of the hub genes using the SIONFH datasets and performing quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis.

Results: This study identified twenty DEERGs from the peripheral serum and hip articular cartilage samples of SIONFH patients and HC. Two SIONFH subtypes related to ERGs were identified, and distinctions in pathways and immune cell infiltration patterns were compared. SIONFH's high-risk subpopulation exhibited enriched immune-related pathways and high immune cell infiltration, such as M0 macrophages, resting mast cells, and neutrophils. Three machine-learning algorithms then determined LCP1, PNP, UBE2V1, and ZFP36 as four exosome-related hub genes (ERHGs). Compared to HC samples, these ERHGs showed excellent diagnostic efficiency (overall AUC for ERHGs is in the range of 0.923 to 0.970 in GSE123568) in SIONFH samples. LCP1, PNP, UBE2V1, and ZFP36 expressions were validated in the GSE123568 and GSE74089 datasets and finally detected in peripheral serum samples with accordant expression by RT-qPCR.

Conclusion: Twenty potential exosome-related genes involved in SIONFH were identified through bioinformatics analysis. LCP1, PNP, UBE2V1, and ZFP36 might become candidate biomarkers and therapeutic targets because they have an intimate relationship with exosomes. These findings shed light on the exosome-related acquaintance of SIONFH and might contribute to the diagnosis and prognosis of SIONFH.

Keywords: Exosome-related genes; Infiltrating immune cells; Machine learning; Peripheral blood; Steroid-induced osteonecrosis of the femoral head.