Objective: The aim of this study was to examine the relationship between active osteoclasts, as defined by positive nuclear NFATc1 signals, and the clinical behaviors of oral giant cell granulomas.
Materials and methods: NFATc1 immunohistochemical and TRAP-Cbfa1 double immunofluorescence stainings were performed on 9 cases of peripheral giant cell granulomas (PGCGs), 9 cases of central giant cell granulomas (CGCGs) with a recurrent history, and 10 cases of CGCGs without a recurrent history. The results were photographed and quantified by ImageJ. Nine osteoclast- and osteoblast-related parameters were analyzed with conventional statistics and with Rapidminer, an open data analysis platform for computer predictive modeling.
Results: Peripheral giant cell granulomas had a significantly lower percentage of active osteoclasts than CGCGs. The recurrent CGCG subgroup had the highest active osteoclast density in comparison with non-recurrent CGCG subgroup and PCCGs.
Conclusions: The study strongly indicates that the status of osteoclasts, as defined by the subcellular NFATc1 signal, has an association with the clinical behavior of oral giant cell granulomas. NFATc1 staining may be useful as a biomarker to predict recurrence of CGCGs. The study also illustrates the potential application of data science tools in studying pathology to facilitate the discovery of disease-associated biomarkers.
Keywords: giant cell granuloma; giant cell lesions; immunohistochemistry; machine learning; osteoblasts; osteoclasts.
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