Role of arachidonic acid metabolism in intervertebral disc degeneration: identification of potential biomarkers and therapeutic targets via multi-omics analysis and artificial intelligence strategies

Abstract

Abstract Background Intervertebral disc degeneration (IVDD) is widely recognized as the primary etiological factor underlying low back pain, often necessitating surgical intervention as the sole recourse in severe cases. The metabolic pathway of arachidonic acid (AA), a pivotal regulator of inflammatory responses, influences the development and progression of IVDD. Methods Initially, a comparative analysis was conducted to investigate the relationship between AA expression patterns and different stages of IVDD using single-cell sequencing (scRNA-seq) data. Additionally, three machine learning methods (LASSO, random forest, and support vector machine recursive feature elimination) were employed to identify hub genes associated with IVDD. Subsequently, a novel artificial intelligence prediction model was developed for IVDD based on an artificial neural network algorithm and validated using an independent dataset. The identified hub genes were further subjected to functional enrichment, immune infiltration, and Connectivity Map analysis. Moreover, external validation was performed using flow cytometry and real-time reverse transcription polymerase chain reaction analysis. Results Both scRNA-seq and bulk RNA-seq data revealed a positive correlation between the severity of IVDD and the AA metabolic pathway. They also revealed increased AA metabolic activity in macrophages and neutrophils, as well as enhanced intercellular communication with nucleus pulposus cells. Utilizing advanced machine learning algorithms, five hub genes (AKR1C3, ALOX5, CYP2B6, EPHX2, and PLB1) were identified, and an incipient diagnostic model was developed with an AUC of 0.961 in the training cohort and 0.72 in the validation cohort. An in-depth exploration of the functionality of these hub genes revealed their notable association with inflammatory responses and immune cell infiltration. Lastly, AH6809 was found to delay IVDD by inhibiting AKR1C3. Conclusions This study offers comprehensive insights into potential biomarkers and small molecules associated with the early pathogenesis of IVDD. The identified biomarkers and the developed integrated diagnostic model hold great promise in predicting the onset of early IVDD. AH6809 was established as a therapeutic target for AKR1C3 in the treatment of IVDD, as evidenced by computer simulations and biological experiments.