# Identifying druggable gene-related biomarkers in intervertebral disc degeneration through transcriptome sequencing and mendelian randomization analysis

## TL;DR

This study identifies potential biomarkers for intervertebral disc degeneration using gene analysis and confirms their role in disease mechanisms.

## Contribution

A novel diagnostic model and causal biomarkers (BPI and CTSG) are identified for intervertebral disc degeneration.

## Key findings

- Fourteen druggable genes were found to be differentially expressed in intervertebral disc degeneration.
- A five-gene diagnostic model achieved high accuracy in predicting IDD.
- BPI and CTSG were confirmed as causally linked to IDD through Mendelian Randomization and cellular experiments.

## Abstract

Intervertebral disc degeneration (IDD) is a major contributor to low back pain, yet its molecular mechanisms remain unclear. Identifying potential druggable genes (PDGs) associated with IDD could facilitate early diagnosis and targeted therapy. This study aimed to explore the diagnostic and mechanistic significance of PDGs in IDD.

Three GEO datasets were merged as a training set, with another blood-based dataset as testing. PDGs were obtained from the literature and intersected with differentially expressed genes (DEGs). Functional enrichment and immune infiltration analyses were performed. A Lasso regression model was developed for diagnosis, and Mendelian Randomization (MR) analysis inferred causality. Cellular experiments validated key gene expression. Fourteen differentially expressed PDGs were identified, primarily involved in immune responses and neutrophil activity. A five-gene diagnostic model (BPI, CD160, CTSG, CYP27A1, KIF11) was constructed and demonstrated high accuracy. MR analysis confirmed a causal relationship between BPI and CTSG with IDD. GSEA revealed that BPI was negatively associated with oxidative phosphorylation, while CTSG was linked to the G2M checkpoint. Cellular experiments confirmed BPI and CTSG upregulation in TNF-α-induced NPCs.

This study constructed a diagnostic model and identified BPI and CTSG as potential biomarkers for IDD, providing new insights into IDD treatment.