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

Area of Science:

• Biochemistry and Molecular Biology
• Genetics and Bioinformatics
• Regenerative Medicine

Background:

• Intervertebral disc degeneration (IVDD) is linked to programmed cell death (PCD) pathways.
• Understanding these pathways is crucial for identifying therapeutic targets for IVDD.

Purpose of the Study:

• To identify core PCD types and associated genes in IVDD.
• To explore potential drug interactions for IVDD treatment.

Main Methods:

• Bioinformatic analysis of gene expression datasets (GSE167199, GSE176205, GSE34095, GSE56081, GSE70362).
• Differential gene expression, Gene Set Variation Analysis (GSVA), machine learning, immune infiltration, and single-cell analysis.
• Molecular docking, in vitro, and in vivo experiments using a murine IVDD model.

Main Results:

• Apoptosis, autophagy, ferroptosis, and necroptosis identified as key PCD types in IVDD.
• A gene module linked to apoptosis correlated with IVDD severity, revealing 34 central genes.
• Glibenclamide showed drug interactions with PDCD6 and UBE2K, reducing apoptosis and delaying IVDD progression in vivo and in vitro.

Conclusions:

• This study comprehensively analyzes PCD in IVDD, highlighting four major contributing PCD types.
• Identified key genes and validated Glibenclamide as a potential therapeutic agent for IVDD.
• Provides novel insights into disc degeneration pathology and suggests promising treatment strategies.