Identification and validation of ferroptosis-related biomarkers in steroid-induced osteonecrosis of the femoral head

Knockdown of lncRNA XR_877193.1 suppresses ferroptosis and promotes osteogenic differentiation via the PI3K/AKT signaling pathway in SONFH

Ferroptosis is a novel form of regulated cell death characterized by the iron-dependent accumulation of lipid peroxides. Recent research has suggested that ferroptosis in osteoblasts contributes to steroid-induced osteonecrosis of the femoral head (SONFH). However, the relationship between ferroptosis and SONFH remains unclear. In this study, in vitro experiments show that dexamethasone (Dex) treatment reduces the expressions of key ferroptosis regulators, SLC7A11 and GPX4, in MC3T3-E1 cells. This reduction leads to a decrease in intracellular glutathione (GSH) levels, accompanied by elevated levels of total iron, malondialdehyde (MDA), and reactive oxygen species (ROS). Importantly, the ferroptosis inhibitor ferrostatin-1 (Fer-1) effectively reverses Dex-induced ferroptosis in MC3T3-E1 cells. Furthermore, RNA-seq analysis reveals that the long noncoding RNA (lncRNA) XR_877193.1is significantly upregulated in Dex-treated MC3T3-E1 cells. Functional studies demonstrate that the knockdown of lncRNA XR_877193.1 promotes osteogenic differentiation by inhibiting Dex-induced ferroptosis in MC3T3-E1 cells, whereas its overexpression exacerbates cell death via ferroptosis. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis reveals that the differentially expressed lncRNA XR_877193.1 is enriched in ferroptosis-related pathways, including the PI3K/AKT signaling pathway. Moreover, PI3K/AKT inhibitors reverse ferroptosis in MC3T3-E1 cells inhibited by lncRNA XR_877193.1 knockdown. Collectively, our findings indicate that lncRNA XR_877193.1 knockdown exerts anti-ferroptosis effects by stimulating the PI3K/AKT signaling pathway, suggesting a promising therapeutic strategy for attenuating SONFH.

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

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.

Pathological mechanisms and related markers of steroid-induced osteonecrosis of the femoral head

BACKGROUND

Osteonecrosis of the femoral head (ONFH) is a refractory orthopedic disease with a high disability rate. Long-term administration of steroids is the most common pathogenic factor for non-traumatic ONFH. Early diagnosis of steroid-induced osteonecrosis of the femoral head (SONFH) is difficult and mainly depends on imaging.

OBJECTIVES

The objectives of this review were to examine the pathological mechanisms of SONFH, summarize related markers of SONFH, and identify areas for future studies.

METHODS

We reviewed studies on pathological mechanisms and related markers of SONFH and discussed the relationship between them, as well as clinical applications and the outlook of potential markers.

RESULTS

The pathological mechanisms of SONFH included decreased osteogenesis, lipid accumulation, increased intraosseous pressure, and microcirculation disruption. Differential proteomics and genomics play crucial roles in the occurrence, progression, and outcome of SONFH, providing novel insights into SONFH. Additionally, the biological functions of mesenchymal stem cells (MSCs) and exosomes (Exos) in SONFH have attracted increasing attention.

CONCLUSIONS

The pathological mechanisms of SONFH are complex. The related markers mentioned in the current review can predict the occurrence and progression of SONFH, which will help provide effective early clinical prevention and treatment strategies for SONFH.

CD4 T cell-secreted IFN-γ in Sjögren's syndrome induces salivary gland epithelial cell ferroptosis

BACKGROUND

Sjögren's syndrome (SS) is a chronic autoimmune disease that predominantly affects exocrine glands. Previous studies have demonstrated that upregulated interferon-gamma (IFN-γ) in SS triggers ferroptosis in salivary gland epithelial cells (SGECs), resulting in impaired salivary gland secretion. However, the immune cells responsible for secreting IFN-γ remain unclear. Therefore, this study conducted bioinformatics analysis and molecular validation to identify the origin of IFN-γ in SS salivary gland.

METHODS

The 'limma' package in R software was utilized to identify differentially expressed genes (DEGs) in the human SS dataset. Subsequently, the identified DEGs were compared with the ferroptosis database and screened through Cytoscape to determine candidate genes. The cellular localization and expression patterns of candidate genes were further confirmed in the salivary gland single-cell RNA sequence (scRNA-seq) data set from healthy control and SS mice. Furthermore, in vitro and in vivo studies were performed to analyze the effect of CD4 T-secreted IFN-γ on SGECs' ferroptosis and functions.

RESULTS

Upregulated TLR4, IFNG, and IL33 were screened as candidates ferroptosis ferroptosis-inducing genes in SS salivary glands. The association of IFNG and IL33 with CD4 T cells was established through immune infiltration analysis. The expression of IFN-γ on CD4 T cells was robustly higher compared with that of IL33 as evidenced by scRNA-seq and immunofluorescence co-localization. Subsequent experiments conducted on candidate genes consistently demonstrated the potent ability of IFN-γ to induce SGECs' ferroptosis and inhibit AQP5 expression.

CONCLUSIONS

Our findings indicate that CD4 T cell-secreted IFN-γ in SS induces SGECs' ferroptosis and inhibits AQP5 expression.

Identification and validation of ferroptosis-related gene SLC2A1 as a novel prognostic biomarker in AKI

BACKGROUND

Emerging evidence reveals the key role of ferroptosis in the pathophysiological process of acute kidney injury (AKI). Our study aimed to investigate the potential ferroptosis-related gene in AKI through bioinformatics and experimental validation.

METHODS

The AKI single-cell sequencing dataset was retrieved from the GEO database and ferroptosis-related genes were extracted from the GENECARD website. The potential differentially expressed ferroptosis-related genes of AKI were selected. Functional enrichment analysis was performed. Machine learning algorithms were used to identify key ferroptosis-related genes associated with AKI. A multi-factor Cox regression analysis was used to construct a risk score model. The accuracy of the risk score model was validated using receiver operating characteristic (ROC) curve analysis. We extensively explored the immune landscape of AKI using CIBERSORT tool. Finally, expressions of ferroptosis DEGs were validated in vivo and in vitro by Western blot, ICH and transfection experiments.

RESULTS

Three hub genes (BAP1, MDM4, SLC2A1) were identified and validated by constructing drug regulatory network and subsequent screening using experimentally determined interactions. The risk mode showed the low-risk group had significantly better prognosis compared to high-risk group. The risk score was independently associated with overall survival. The ROC curve analysis showed that the prognosis model had good predictive ability. Additionally, CIBERSORT immune infiltration analysis suggest that the hub gene may influence cell recruitment and infiltration in AKI. Validation experiments revealed that SLC2A1 functions by regulating ferroptosis.

CONCLUSIONS

In summary, our study not only identifies SLC2A1 as diagnostic biomarker for AKI, but also sheds light on the role of it in AKI progression, providing novel insights for the clinical diagnosis and treatment of AKI.