Identification and experimental validation of aging-related biomarkers in intervertebral disc degeneration

Abstract

Abstract Intervertebral disc degeneration (IDD) is a prevalent disease with an increasing incidence, and aging is a key risk factor for its progression. Therefore, this study aimed to explore the role of aging-related genes in IDD through bioinformatics analysis. Differentially expressed aging-related genes were obtained from the CellAge, GSE150408 and GSE124272 datasets, followed by biomarker screening via machine learning. Finally, the identified biomarkers were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR), Western Blot (WB), and immunohistochemistry (IHC) assays. A total of 33 aging-related differentially expressed genes (DEGs) were screened in IDD, and machine learning combined with ROC curve analysis identified PPM1D, PIK3C2A, and BTG3 as aging-related biomarkers for IDD; Gene Set Enrichment Analysis (GSEA) revealed that these three biomarkers were enriched in gene functions including cellular senescence, multicellular organismal aging, negative regulation of cellular senescence, and Ribosome. In addition, the multifactorial regulatory network showed that transcription factor E2F1 and hsa-miR-147 co-regulated both PPM1D and BTG3. In experiments validating biomarker expression levels, BTG3 and PIK3C2A exhibited consistent expression trends across IHC, RT-qPCR, WB assays and the two datasets. BTG3 and PIK3C2A may play more critical roles in the progression of IDD, thereby providing novel insights for the development of new therapeutic strategies for IDD patients.