Current Genomics - Online First

Osteonecrosis of the Femoral Head (ONFH) is one of the common refractory diseases. However, the role of cuproptosis in ONFH pathogenesis remains unexplored. This study aimed to investigate the potential relationship between cuproptosis and ONFH.

ONFH-related datasets were obtained from the Gene Expression Omnibus (GEO) database, and cuproptosis-related genes in the GSE123568 dataset were identified through differential expression analysis. To further discover potential cuproptosis-related biomarkers, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine (SVM) analysis were conducted. The Receiver Operating Characteristic (ROC) curve analysis was used to explore the diagnostic value of cuproptosis-related biomarkers. The summary Statistics-based Mendelian Randomization (SMR) algorithm was used to investigate the causal relationship between the related genes and ONFH. The immune infiltration analysis was conducted to assess the effect of immune cells on ONFH. Subsequently, the GSE74089 and GSE89587 datasets were used to validate gene expression levels and predict the lncRNA-miRNA-mRNA network. Finally, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was employed to validate the expression of these genes.

The study showed that the upregulation of PDHB, a cuproptosis-related biomarker, may contribute to the development of ONFH. Additionally, immune cells were found to play a crucial role in ONFH, and PDHB showed a significant association with various immune cells. Furthermore, the study identified the existence of the MIR22HG/let-7c-5p/PDHB regulatory pathway, which may play a critical role in ONFH through cuproptosis.

This study discovered a cuproptosis-related regulating pathway, MIR22HG/let-7c-5p/PDHB. This can provide new insights into the treatment of ONFH. However, further experimental validation is needed.

PDHB, identified as a cuproptosis-related biomarker, can induce ONFH through cuproptosis. PDHB also contributes to the pathogenesis and progression of ONFH by influencing immune cell function. This is most likely mediated through the regulatory interaction between MIR22HG, let-7c-5p, and PDHB.

The appearance of single-cell RNA sequencing (scRNA-seq) data has brought a distinctive perspective to studying gene expression at the cell level. However, it faces challenges such as large data volume, sparsity, heterogeneity, and the curse of dimensionality. Current clustering methods still face many challenges in studying cell type distribution and have not utilized the structural relationship information between cells.

To avoid the insufficiency of the single characteristic space of scRNA-seq data in characterizing cell function, this paper constructs multiple view characteristic spaces and utilizes multi-view learning to characterize scRNA-seq information from distinctive perspectives comprehensively. In multi-view learning, the similarity graph is divided into weighted learning and structural learning stages. Through weighting the multi-view similarity graphs, the significance of diverse views and features is underscored. During the structural stage, the emphasis is placed on uncovering potential relationships among different views by preserving common edges in the multi-view similarity graphs. The optimization of the attribute and structure graphs was conducted separately by the alternating direction multiplier method.

The performance of the MVGSC was validated using eight different scales of real scRNA-seq datasets, and the experimental results showed that the proposed multi-view clustering method significantly surpasses other single-view clustering methods and multi-view clustering methods.

When the features of scRNA-seq data are complex and there are significant differences between views, the two-stage multi-view graph method can better capture the complex relationships in the data, demonstrating superior performance compared to a single framework.

Two-stage multi-view learning can more accurately capture complex relationships in the data, thereby improving the accuracy of the model. It can also better capture consistency and complementary information in multi-view data, thereby enhancing the generalization ability of the model.

Thyroid Cancer (TC) is a prevalent endocrine malignancy with an increasing incidence worldwide, often associated with Hashimoto's Thyroiditis (HT), an autoimmune thyroid disorder. This study aimed to identify and validate key hub genes common to TC and HT and explore their diagnostic, prognostic, and therapeutic roles.

Gene expression datasets for TC and HT were analyzed using bioinformatics tools to identify hub genes. In SW579 cells, Gefitinib treatment and siRNA-mediated knockdown of ALDH3A1 and DDX52 were performed, followed by RT-qPCR, Western blot, cell proliferation, colony formation, and wound healing assays.

After analyzing TC and HT datasets, we identified four common dysregulated hub genes: ALDH3A1, DDX52, RASA1, and SPATS2. RT-qPCR confirmed their significant upregulation in TC cell lines compared to normal controls (p < 0.001). ROC analysis demonstrated high diagnostic accuracy, with RASA1 and SPATS2 achieving AUC = 1. Gene expression validation using GSCA and HPA datasets corroborated these findings, and promoter hypomethylation analysis revealed regulatory mechanisms underlying their upregulation. Survival analyses associated elevated ALDH3A1 expression with poor overall survival. Functional assays in TC cells highlighted their oncogenic roles, with knockdown experiments showing reduced proliferation, migration, and colony formation. Immune correlation analyses revealed interactions with immune inhibitors and infiltrates, while miRNA profiling identified tumor-suppressive miRNAs targeting these genes. Drug prediction and molecular docking identified Gefitinib as a promising therapeutic, which effectively suppressed ALDH3A1 and DDX52 expression and oncogenic phenotypes in TC cells.

This study offers comprehensive insights into the molecular underpinnings of TC progression, highlighting the diagnostic and therapeutic potential of these hub genes and their associated regulatory networks. These findings lay a foundation for developing novel therapeutic strategies targeting these genes in TC management.