Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Intervertebral Disc Degeneration (IDD) is a primary cause of lower back pain.
• The precise molecular mechanisms underlying IDD remain largely unknown.
• IDD represents a significant global health challenge requiring further investigation.

Purpose of the Study:

• To identify key genes and molecular pathways implicated in IDD.
• To leverage bioinformatics and machine learning for IDD mechanism discovery.
• To uncover potential diagnostic markers and therapeutic targets for IDD.

Main Methods:

• Integrated analysis of gene expression profiles from patient and control cohorts.
• Differential gene expression analysis and Gene Ontology (GO) enrichment.
• Weighted Gene Co-expression Network Analysis (WGCNA) and LASSO regression.
• Protein-Protein Interaction (PPI) network construction and validation.

Main Results:

• Identified 244 differentially expressed genes (183 upregulated, 61 downregulated).
• WGCNA and DEG intersection revealed 9 candidate genes.
• LASSO regression identified 6 key genes, with IL1R1 and TCF7L2 highlighted as central in the PPI network.

Conclusions:

• IL1R1 and TCF7L2 are identified as core genes in Intervertebral Disc Degeneration.
• These findings provide novel insights into IDD pathogenesis.
• The study suggests potential avenues for future therapeutic development for IDD.