Immune Mechanism Of Low Bone Mineral Density Caused By Ankylosing Spondylitis Based On Bioinformatics And Machine Learning

Ankylosing spondylitis(AS)is inflammatory rheumatism,mainly affecting the axial skeleton,causing characteristic inflammatory back pain,leading to structural and functional disorders and declining quality of life.The enthesitis of AS increases new bone formation and spinal ankylosis.Meanwhile,the loss of bone trabecula in the vertebral body of AS patients lowered local bone mineral density and caused osteoporosis.Interestingly,the opposite trend of bone metabolism reduced the spine’s ability to resist impact and strain,increasing the risk of spinal fracture,then increasing the probability of spinal cord and nerve root injury and mortality in patients with AS.As an essential risk factor for vertebral fracture and a common complication of AS,low bone mineral density(LBMD)has attracted the attention of many scholars.Recent studies on the mechanism of LBMD caused by AS have focused on immunity and inflammation.Unfortunately,the mechanism by which AS induces LBMD is not fully understood.Therefore,exploring the role of immune-related genes in AS-induced LBMD is helpful in accurately judging the fracture risk of AS patients,guiding drug development and clinical management.Objective:The purpose of our study is to comprehensively use bioinformatics methods to explore the specific mechanism of immune-related genes in AS-induced LBMD and its relationship with immune cells.At the same time,machine learning methods were used to establish diagnostic models to evaluate the risk of LBMD in AS patients and guide clinical treatment.Methods:AS and LBMD datasets were downloaded from the GEO database,and differential expression gene analysis was performed to obtain DEGs(differential expressed gene).Immune-related genes(IRGs)were obtained from ImmPort database.Overlapping DEGs and IRGs got I-DEGs.Pearson coefficients were used to calculate DEGs and IRGs correlations in the AS and LBMD datasets,and construct the co-expression network.Louvain community discovery was used to cluster the co-expression network to get gene modules.The module most related to the immune module was defined as the key module.Metascape was used for the enrichment analysis of key modules.Further,I-DEGs with the same trend in AS and LBMD were considered key I-DEGs.Multiple machine learning methods were used to construct diagnostic models based on key I-DEGs.The IID database was used to find the context of I-DEGs,especially in the skeletal system.Gene-biological process and gene-pathway networks were constructed based on key I-DEGs.In addition,immune infiltration was analyzed on the AS dataset using the CIBERSORT algorithm.Results:1.A total of 19 genes were identified I-DEGs,of which IFNAR1,PIK3CG,PTGER2,TNF,and CCL3 were considered the key I-DEGs.These key I-DEGs had a good relationship with the hub genes of key modules.2.Multiple machine learning showed that key I-DEGs,as a signature,had an excellent diagnostic performance in both AS and LBMD,and the SVM model had the highest AUC value.3.Key I-DEGs were closely linked through bridge genes,especially in the skeletal system.4.Pathway analysis showed that PIK3CG,IFNAR1,CCL3,and TNF participated in NETs formation through pathways such as the MAPK signaling pathway.5.Immune infiltration analysis showed neutrophils had the most significant differences between case and control groups and a good correlation with key I-DEG.Conclusion:The key I-DEGs,TNF,CCL3,PIK3CG,PTGER2,and IFNAR1,can be utilized as biomarkers to determine the risk of LBMD in AS patients.They may affect neutrophil infiltration and NETs formation to influence the bone remodeling process in AS.