Identification of KCNQ1 as a diagnostic biomarker related to endoplasmic reticulum stress for intervertebral disc degeneration based on machine learning and experimental evidence

Abstract

Intervertebral disc degeneration (IDD) is a primary cause of low back pain and disability. Cellular senescence and apoptosis due to endoplasmic reticulum stress (ERS) are key in IDD pathology. Identifying biomarkers linked to ERS in IDD is crucial for diagnosis and treatment. We utilized machine learning on gene expression profiles from the Gene Expression Omnibus database to discover biomarkers associated with ERS in IDD. Gene set enrichment analysis (GSEA) and single-sample GSEA were applied to evaluate the immunological features and biological functions of these biomarkers. The expression of KCNQ1 was experimentally validated. Machine learning identified KCNQ1 as a diagnostic biomarker for ERS in IDD, confirmed by Western blotting. GSEA indicated that KCNQ1 influences IDD primarily through the Notch signaling pathway and by regulating macrophage and monocyte infiltration. KCNQ1, identified as an ERS-associated biomarker in IDD, impacts the Notch signaling pathway and immune cell infiltration, suggesting its potential as a therapeutic target for IDD. Further validation through prospective studies and additional experimental methods is necessary to elucidate the role of KCNQ1 in IDD comprehensively.