Utilizing bioinformatics to identify biomarkers and analyze their expression in relation to immune cell ratios in femoral head necrosis.

Background: Necrosis of the Femoral Head (NFH) represents a challenging orthopedic condition, characterized by elusive early detection and rapid progression, predominantly in the middle-aged demographic. Current research on the pathophysiological and immunoregulatory mechanisms underpinning immune cell infiltration in NFH is sparse. This study employs bioinformatics analysis of publicly available RNA sequencing databases to elucidate the pivotal molecules and pathways implicated in NFH progression. Methods: The NFH-related dataset GSE123568 was obtained from the Gene Expression Omnibus (GEO). Subsequently, CIBERSORT was utilized to assess the proportion and distribution of immune cell types, followed by the identification of critical Hub immune cells using LASSO and RFE algorithms. The dataset GSE123568 was then explored to identify significantly differentially expressed genes (DEGs). These genes were further refined by intersecting with death-associated genes reported in existing literature. GO and KEGG pathway enrichment analyses were conducted to elucidate their underlying molecular mechanism. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized via Cytoscape. Hub genes were identified using the CytoHubba plugin, followed by enrichment analysis, and their expression levels were evaluated using the ROC curve. In addition, we performed expression data visualization and ROC curve analysis on the external dataset GSE74089 to further evaluate the discriminative power of the hub genes. Moreover, the study analyzed the correlation between the identified hub genes and Hub immune cells. Finally, we verified the hub genes utilizing real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry. Results: Four types of immune cells (Neutrophil, Mast cell resting, Myeloid dendritic cell activated, Macrophage M0) were identified. Fourteen pivotal genes (BCL2L1, BIRC2, NFKBIA, XIAP, CFLAR, AKT1, BIRC3, IKBKB, RIPK1, CASP8, TNFRSF1A, IL1B, CASP1, STAT3) were identified, and the findings were validated using the external dataset GSE74089. Among these, STAT3 exhibited the most pronounced positive correlation with neutrophils (r = 0.6804, p = 3.525e-05). Conversely, XIAP displayed the most significant negative correlation with Myeloid dendritic cell activated (r = -0.3610, p = 0.04003). In experiments, the experimental outcomes for five hub genes (CASP8, TNFRSF1A, AKT1, XIAP and STAT3) were congruent with the results obtained from bioinformatics analysis. Conclusion: Our study identified CASP8, TNFRSF1A, AKT1, XIAP, STAT3 and BCL2L1 as potential biomarkers for NFH patients and elucidated the immune cell types with the strongest association to these markers. These insights may be crucial for the early diagnosis, understanding of the pathophysiological mechanisms, and the development of treatment strategies for NFH.