Background Lumbar disc degeneration (LDD) is a major pathological process implicated in low back pain. At present, the research in the fields of spinal surgery has highlighted the complex mechanisms underlying LDD, with autophagy being considered as one of the important processes involved. Objectives We aimed to identify the genes and molecular pathways associated with LDD and autophagy using computational tools and publicly available data, and to identify drugs targeting the relevant genes associated with LDD and autophagy. Materials and Methods We used text mining to detect the LDD and autophagy-associated genes, and the intersection of the two gene sets was selected for gene ontology analysis using the DAVID program. We then constructed protein–protein interaction networks, followed by a functional enrichment analysis, from which we obtained three significant gene modules. Finally, the final list of genes was queried against the Drug Gene Interaction database to find drug candidates targeting relevant genes associated with LDD and autophagy. Results Our analysis identified 72 genes common to both the “LDD” and “Autophagy” text mining concepts. Gene enrichment analysis yielded three significant gene modules (22 genes), which represent four significant pathways and could be targeted by 32 Food and Drug Administration (FDA)-approved drug molecules, and identified the drug–gene interactions. Conclusion Using text mining, pathway analysis tools, and drug–gene interaction analysis for gene screening, signal pathway analysis and drug discovery can provide new ideas for the scientific research and clinical treatment.

Background: Lumbar disc degeneration (LDD) is a major pathological process implicated in low back pain. At present, the research in the fields of spinal surgery has highlighted the complex mechanisms underlying LDD, with autophagy being considered as one of the important processes involved.Objectives: This study was designed to identify the potential key genes and molecular pathways associated with LDD and autophagy using computational tools and publicly available data, and to explore drugs targeting the relevant genes associated with LDD and autophagy.Materials and Methods: We used text mining to detect the LDD and autophagy-associated genes, and the intersection of the two gene sets was selected for gene ontology analysis using the DAVID program. We then constructed protein–protein interaction networks, followed by a functional enrichment analysis, from which we obtained three significant gene modules. Finally, the final list of genes was queried against the Drug Gene Interaction database to find drug candidates targeting relevant genes associated with LDD and autophagy.Results: Our analysis identified 72 genes common to both the “LDD” and “Autophagy” text mining concepts. Gene enrichment analysis yielded three significant gene modules (22 genes), which represent four significant pathways and could be targeted by 32 Food and Drug Administration (FDA)-approved drug molecules, and identified the drug–gene interactions. Conclusion: In summary, we presented a method to explore the potential key genes, molecular pathways and candidate drugs associated with LDD and autophagy. As a result, in this method, we identified a total of 22 potential genes, four significant pathways and 32 candidate drugs, which could provides a basis for new trials and the development of novel targeted therapies as potential treatments for LDD.