Identification of a potential novel biomarker in intervertebral disk degeneration by bioinformatics analysis and experimental validation

Abstract

Background: Intervertebral disk degeneration (IVDD) is a major cause of low back pain and one of the most common health problems all over the world. However, the early diagnosis of IVDD is still restricted. The purpose of this study is to identify and validate the key characteristic gene of IVDD and analyze its correlation with immune cell infiltration.

Methods: 3 IVDD-related gene expression profiles were downloaded from the Gene Expression Omnibus database to screen for differentially expressed genes (DEGs). Gene Ontology (GO) and gene set enrichment analysis (GSEA) were conducted to explore the biological functions. Two machine learning algorithms were used to identify characteristic genes, which were tested to further find the key characteristic gene. The receiver operating characteristic curve was performed to estimate the clinical diagnostic value of the key characteristic gene. The excised human intervertebral disks were obtained, and the normal nucleus pulposus (NP) and degenerative NP were carefully separated and cultured in vitro. The expression of the key characteristic gene was validated by real-time quantitative PCR (qRT-PCR). The related protein expression in NP cells was detected by Western blot. Finally, the correlation was investigated between the key characteristic gene and immune cell infiltration.

Results: A total of 5 DEGs, including 3 upregulated genes and 2 downregulated genes, were screened between IVDD and control samples. GO enrichment analysis showed that DEGs were enriched to 4 items in BP, 6 items in CC, and 13 items in MF. They mainly included the regulation of ion transmembrane transport, transporter complex, and channel activity. GSEA suggested that the cell cycle, DNA replication, graft versus host disease, and nucleotide excision repair were enriched in control samples, while complement and coagulation cascades, Fc γ R-mediated phagocytosis, neuroactive ligand-receptor interaction, the NOD-like receptor signaling pathway, gap junctions, etc., were enriched in IVDD samples. Furthermore, ZNF542P was identified and tested as key characteristic gene in IVDD samples through machine learning algorithms and showed a good diagnostic value. The results of qRT-PCR showed that compared with normal NP cells, the expression of ZNF542P gene was decreased in degenerated NP cells. The results of Western blot suggested that compared with normal NP cells, the expression of NLRP3 and pro Caspase-1 was increased in degenerated NP cells. Finally, we found that the expression of ZNF542P was positively related to the proportions of T cells gamma delta (γδT cells).

Conclusion: ZNF542P is a potential biomarker in the early diagnosis of IVDD and may be associated with the NOD-like receptor signaling pathway and the infiltration of γδT cells.

Keywords: bioinformatics; biomarker; immune cell infiltration; intervertebral disc degeneration; machine learning algorithms; nucleus pulposus cell.