Machine learning-based analysis of programmed cell death types and key genes in intervertebral disc degeneration.

Abstract

Intervertebral disc degeneration (IVDD) is intricately associated with various forms of programmed cell death (PCD). Identifying key PCD types and associated genes is essential for understanding the molecular mechanisms underlying IVDD and discovering potential therapeutic targets. This study aimed to elucidate core PCD types, related genes, and potential drug interactions in IVDD using comprehensive bioinformatic and experimental approaches. Using datasets GSE167199, GSE176205, GSE34095, GSE56081, and GSE70362, relevant gene expression and clinical data were analyzed. Differential expression gene (DEG) analysis identified upregulated genes linked to 15 PCD types. Gene Set Variation Analysis (GSVA) was employed to pinpoint key PCD types contributing to disc degeneration. Core genes were identified through machine learning techniques, while immune infiltration and single-cell analysis helped identify apoptosis-related cell types. Molecular docking, along with in vivo and in vitro experiments using a murine IVDD model, validated potential drug interactions. The results identified apoptosis, autophagy, ferroptosis, and necroptosis as key PCD types in IVDD. A gene module associated with apoptosis showed a strong correlation with the severity of disc degeneration, revealing 34 central genes in the gene network. Drug screening identified Glibenclamide as effectively interacting with PDCD6 and UBE2K. Subsequent in vitro and in vivo experiments demonstrated that Glibenclamide reduced apoptosis and delayed disc degeneration progression. This study provides a comprehensive bioinformatics analysis of PCD in IVDD, identifying four primary PCD types contributing to the disease's progression. The findings offer novel insights into the molecular pathology of disc degeneration and suggest promising therapeutic strategies for future treatment development.