Identification of m6A-associated diagnostic biomarkers and subtypes for osteoporosis's disease diagnosis and risk prediction based on machine learning

Abstract Background Osteoporosis(OP) is a systemic skeletal dysfunction disorder that occurs in large numbers worldwide. The aim of our study was to screen potential m6A-related diagnostic markers and establish diagnostic predictive models to provide a reference framework for the diagnosis and treatment of OP. Methods GSE56815 and GSE35956 were retrieved from GEO. The m6A-related diagnostic markers for OP were identified through random forest algorithm screening of GSE56815, and subsequently validated for diagnostic efficacy in GSE35956. The mechanism exploration of diagnostic markers was conducted via GO and KEGG analyses based on GSEA. Immune cell infiltration estimation was performed using ssGSEA, with correlation analysis between immune cells and diagnostic markers. Consensus clustering produced two OP patient subtypes, which were compared using m6A scores. Results 4 m6A-related diagnostic markers for OP have been selected by random forest. Utilizing these 4 diagnostic markers, an OP diagnostic model was successfully established, and its diagnostic value was validated using independent external data. The immune infiltration analysis of ssGSEA revealed that CD56 dim natural killer cell significantly infiltrated the OP samples, while the diagnostic markers were found to possess regulatory effects on diverse immune cells. OP samples were classified into two m6A subtypes through the concordance clustering, with type A having a higher m6A score than type B. The classification can provide more instructive assistance for the diagnosis of OP. Conclusions A diagnostic model for OP was constructed based on four m6A-related genes in the study, which provides significant references for diagnosis of OP and holds practical significance.