Abstract:

Objective To construct and validate a diagnostic model for osteonecrosis of the femoral head (ONFH) based on alcohol exposure-related genes using machine learning methods. Methods The transcriptomic data related to alcohol exposure and ONFH were obtained from the GEO database for construction of a diagnostic model for ONFH. The differentially expressed genes (DEGs) of alcohol exposure and ONFH were identified, and functional enrichment analysis of the intersecting genes was performed. Single sample gene set enrichment analysis (ssGSEA) was used to quantify immune infiltration. A total of 113 combinations of 12 machine learning algorithms were tested on the training set, and 10-fold cross-validation was used to construct the diagnostic model of ONFH, which was validated on the test set. The expressions of the DEGs in the model were detected by qRT-PCR in alcohol-treated MC3T3-E1 cells to verify the reliability of the constructed model. The Enrichr platform was used to identify potential drugs for ONFH. Results Twenty-one intersecting DEGs closely related to alcohol exposure and ONFH were identified, which were also involved in immune processes. Immune infiltration analysis showed that the patients with alcohol exposure and ONFH had significant differences in immune cell infiltration compared with the healthy controls. The diagnostic model was constructed based on 8 genes (SOAT1, GMCL1, GMPR, CISD2, ST3GAL6, AHSP, UBL3, and PTPN12), which showed significant differential expressions in alcohol-treated MC3T3-E1 cells. Ten potential drugs for ONFH treatment were predicted. Conclusion By integrating bioinformatics analysis and machine learning methods, a reliable model for diagnosing ONFH has been successfully constructed based on the DEGs shared by alcohol exposure and ONFH.

Key words: alcohol exposure, osteonecrosis of the femoral head, bioinformatics analysis, machine learning, diagnostic model