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

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 proportions of natural killer cells, neutrophils, and myeloid-derived suppressor cells between IDD and control samples. Further experiments indicated that AC144548.1 could regulate the expressions of SPDL1 and CEP120 by combininghsa-miR-5195-3p and hsa-miR-455-3p, respectively. Additionally, transcription factors FOXM1, PPARG, and ATF3 were identified as regulators of SPDL1 and CEP120 transcription. Notably, 56 drugs were predicted to target these genes. The down-regulation of SPDL1 and CEP120 was also validated.

Conclusion: This study identified two diagnostic genes associated with fibrosis in patients with IDD. Additionally, we elucidated their potential regulatory networks and identified target drugs, which offer a theoretical basis and reference for further study into fibrosis-related genes involved in IDD.