Frontiers in immunology · 2026
Development and experimental validation of an osteoporosis diagnosis model based on disulfidoptosis-related genes and immune infiltration analysis.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Yang Ping, Chen Minzhi, Zhang Junxiang, Liu Zhonghua
Аннотация
BACKGROUND: The role of disulfidoptosis, a novel form of programmed cell death, in osteoporosis remains unclear. This study aimed to identify disulfidoptosis-related molecular signatures for diagnosis, explore their link to immune infiltration, and validate their expression experimentally.
METHODS: We conducted an integrative bioinformatics analysis utilizing microarray datasets from the Gene Expression Omnibus (GEO). Analyses included the identification of disulfidoptosis-related differentially expressed genes (DRDEGs), weighted gene co-expression network analysis (WGCNA), and functional enrichment analyses. Three machine learning algorithms were employed to identify hub genes for diagnostic model construction. Immune infiltration patterns were assessed using CIBERSORT, and osteoporosis subtypes were identified via consensus clustering. Experimental validation was conducted in a RANKL-induced osteoclast model using qPCR and Western blot.
RESULTS: We identified 299 DRDEGs and constructed a diagnostic model based on three hub genes (SOAT2, FOLR3, TUBA8). The model showed moderate diagnostic accuracy in the internal validation set (AUC: 0.7-0.9) and high accuracy upon external validation (AUC > 0.9). Immune analysis revealed distinct infiltration patterns and significant correlations between key genes and immune cells. Consensus clustering defined two osteoporosis subtypes with distinct molecular and immune characteristics. Crucially, experimental validation confirmed significant dysregulation of these genes during osteoclast differentiation, with TUBA8 showing a striking discordance between mRNA upregulation and protein downregulation.
CONCLUSION: This study establishes a diagnostic model for osteoporosis based on disulfidoptosis-related genes and provides multi-level experimental evidence for their dysregulation in vitro. Our findings may reveal immune heterogeneity and uncover potential post-transcriptional regulatory mechanisms, offering new insights for precision medicine in osteoporosis.
METHODS: We conducted an integrative bioinformatics analysis utilizing microarray datasets from the Gene Expression Omnibus (GEO). Analyses included the identification of disulfidoptosis-related differentially expressed genes (DRDEGs), weighted gene co-expression network analysis (WGCNA), and functional enrichment analyses. Three machine learning algorithms were employed to identify hub genes for diagnostic model construction. Immune infiltration patterns were assessed using CIBERSORT, and osteoporosis subtypes were identified via consensus clustering. Experimental validation was conducted in a RANKL-induced osteoclast model using qPCR and Western blot.
RESULTS: We identified 299 DRDEGs and constructed a diagnostic model based on three hub genes (SOAT2, FOLR3, TUBA8). The model showed moderate diagnostic accuracy in the internal validation set (AUC: 0.7-0.9) and high accuracy upon external validation (AUC > 0.9). Immune analysis revealed distinct infiltration patterns and significant correlations between key genes and immune cells. Consensus clustering defined two osteoporosis subtypes with distinct molecular and immune characteristics. Crucially, experimental validation confirmed significant dysregulation of these genes during osteoclast differentiation, with TUBA8 showing a striking discordance between mRNA upregulation and protein downregulation.
CONCLUSION: This study establishes a diagnostic model for osteoporosis based on disulfidoptosis-related genes and provides multi-level experimental evidence for their dysregulation in vitro. Our findings may reveal immune heterogeneity and uncover potential post-transcriptional regulatory mechanisms, offering new insights for precision medicine in osteoporosis.