ArticleFrontiers in immunology2026
Development and experimental validation of an osteoporosis diagnosis model based on disulfidoptosis-related genes and immune infiltration analysis.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.