Lei Sun, Shuihua Xie, Tao Tang
Abstract Low back pain is commonly driven by intervertebral disc degeneration (IDD), a condition marked by the deterioration of the extracellular matrix (ECM), the buildup of senescent cells, and ongoing chronic inflammation. The molecular basis of IDD is still not fully elucidated, especially regarding how transcription factors contribute to the advancement of the disease. We applied weighted gene co-expression network analysis (WGCNA) to microarray data derived from human nucleus pulposus (NP) tissues to uncover gene modules associated with IDD. Hub genes within the most significantly associated module were prioritized using differential expression analysis and 3 machine learning algorithms (LASSO, SVM-RFE, and Random Forest). ZEB2 was selected for further validation. Its expression was assessed in human IDD samples, TBHP-induced aged NP cells, and rat IDD models via RT-qPCR, Western blot, and immunohistochemistry. WGCNA revealed a set of co-expressed genes strongly linked to IDD, enriched for pathways involved in ECM remodeling, cell cycle regulation, and inflammatory signaling. Machine learning models consistently prioritized ZEB2 as a hub transcription factor. Experimental validation confirmed its upregulation in human, cellular, and animal models of disc degeneration at the transcriptional and translational levels. Co-expression and functional enrichment analyses further associated ZEB2 with senescence and matrix dysregulation. ZEB2 showed potential discriminatory ability between IDD and control samples in multiple cohorts (AUC > 0.88). ZEB2 is upregulated in IDD and associated with key pathological processes including ECM degradation, cellular senescence, and inflammation. Integrated bioinformatics analysis and multi-level experimental validation suggest that ZEB2 may serve as a potential biomarker and candidate regulator in IDD.