Machine learning algorithm predicts fibrosis-related blood diagnosis markers of intervertebral disc degeneration.

ABSTRACT:

Background

Intervertebral disc cell fibrosis has been established as a contributing factor to intervertebral disc degeneration (IDD). This study aimed to identify fibrosis-related diagnostic genes for patients with IDD.

Methods

RNA-sequencing data was downloaded from Gene Expression Omnibus (GEO) database. The diagnostic genes was identified using Random forest based on the differentially expressed fibrosis-related genes (DE-FIGs) between IDD and control samples. The immune infiltration states in IDD and the regulatory network as well as potential drugs targeted diagnostic genes were investigated. Quantitative Real-Time PCR was conducted for gene expression valifation.

Results

CEP120 and SPDL1 merged as diagnostic genes. Substantial variations were observed in the pro

SUBMITTER: Zhao W 

PROVIDER: S-EPMC10619283 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature