Title identification of shared diagnostic genes between osteoporosis and Crohn’s disease through integrated transcriptomic analysis and machine learning

Introduction Crohn’s disease (CD) is a chronic inflammatory bowel disease. CD-related inflammation can lead to enhanced bone resorption and destruction, thereby increasing the risk of osteoporosis (OP). This study aimed to screen the hub co-diagnostic gene of CD and OP. Methods The gene expression profiles of CD and OP were obtained from the GEO database to select differentially expressed genes (DEGs). Module genes were identified by weighted gene co-expression network analysis. Two machine learning algorithms were employed to screen potential shared genes, and nomograms were constructed to assess their clinical predictive value. Receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the diagnostic performance of the hub genes. Gene set enrichment analysis (GSEA) and immune infiltration analysis were performed to explore the underlying mechanisms of the hub genes in CD and OP. In vitro experiments were conducted to validate the bioinformatics results. Results The result showed that a total of 8 DEGs and 15 key module genes were found to be related to both CD and OP, from which machine learning screened out 5 potential shared genes. Subsequently, ABO was identified as the hub co-diagnostic gene with good diagnostic value. GSEA results showed that ABO was involved in the mitochondrial matrix, chromosomal region, and ribosome in both CD and OP. Immune infiltration analysis found that activated CD8 T cell, effector memory CD4 T cell, and immature B cell were all significantly negatively correlated with ABO in both diseases. In vitro experiments confirmed the downregulation of ABO in CD and OP cell models. Discussion Overall, ABO was identified as a hub co-diagnostic gene for CD and OP, providing new insights into their co-management.