ArticleBMC medical genomics2023
Machine learning algorithm predicts fibrosis-related blood diagnosis markers of intervertebral disc degeneration.
Article in BMC medical genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 3 citations in OpenAlex.
- TEAD4 and RXRA Regulate the Function of Nucleus Pulposus Cells in Intervertebral Disc Degeneration Via the TNF-α/NF-κB Pathway: An Integrated Analysis of Single-Cell RNA-Seq, Bulk RNA-Seq, and In Vitro Validation.Applied biochemistry and biotechnology · 2026Article
- Messenger RNA therapy in bone and joint diseases: Rationale, delivery systems, and applications.Biomaterials translational · 2026Article
- Single-cell extracellular vesicle-program scoring maps immunometabolic rewiring and immune crosstalk of mesenchymal stromal cells in intervertebral disc degeneration, prioritizing AP2S1 and CSTB.Frontiers in immunology · 2026Article
- Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning.Frontiers in immunology · 2025Article
- Immune microenvironment in intervertebral disc degeneration: pathophysiology and therapeutic potential.Frontiers in immunology · 2025Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntervertebral 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.
methodsRNA-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.
resultsCEP120 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.
conclusionThis 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.
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