Utilising bioinformatics and molecular docking technology to explore the underlying mechanisms of intervertebral disc degeneration with potential therapeutic drugs and formulas.

BACKGROUND

Intervertebral disc degeneration (IDD) is prevalent in orthopaedics, yet lacks effective treatments. This study seeks to discover potential therapeutic targets for IDD to inform clinical therapies and traditional medicine approaches.

METHODS

In this study, IDD-related data sets were sourced from the Gene Expression Omnibus, and differential expression analysis was performed to identify differentially expressed genes. Subsequently, candidate genes associated with IDD were recognised using databases such as GeneCards, OMIM, DrugBank, and DisGeNET, with further validation of these genes' biological functions and involvement in signalling pathways through enrichment analyses. Additionally, machine learning algorithms were applied to select candidate targets. The diagnostic value of these targets for IDD was assessed by constructing a nomogram model, and their functional networks and biological processes were revealed using GeneMANIA and Gene Set Enrichment Analysis. Eventually, the research also encompassed immune infiltration analysis and the construction of competing endogenous RNA (ceRNA) networks, as well as predictions for potential drugs and traditional Chinese medicine (TCM) prescriptions.

RESULTS

A total of 89 differentially expressed genes were identified through bioinformatics analysis, and further analysis led to the determination of 16 candidate genes associated with IDD. Seven candidate targets were found from the candidate genes using machine learning methods. Two of these targets, cytochrome P450 family 1 subfamily B member 1 (CYP1B1) and tumour necrosis factor alpha-induced protein 6 (TNFAIP6), were chosen as key targets because they demonstrated a significant difference in expression in IDD. Following, it was also found that CYP1B1 and TNFAIP6, as well as the nomogram, indicated good predictive performance for IDD. Furthermore, gamma-delta T cells were more prevalent in IDD. CYP1B1 and TNFAIP6 showed strong correlations with gamma delta T cells, indicating a tight link between these key targets and the pathology of IDD. Eventually, 11 natural small molecules corresponding to key targets were discovered. Three of these compounds (Quercetin, Genistein, Apigenin) were found in six TCM. This could offer new theoretical references for the clinical treatment of IDD.

CONCLUSIONS

This study identified CYP1B1 and TNFAIP6 as important targets for IDD, developed a predictive nomogram, and explored the application of TCM herbal formulae, providing new insights into the clinical treatment and prescription development of IDD.