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July 10, 2024Frontiers in ImmunologyOpen Access

Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms

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Authors

HZHao ZhangSSShengbo ShiXHXingxing Huang

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Overview

Machine learning analysis identifies IL1R1 and TCF7L2 as core biomarkers in intervertebral disc degeneration, highlighting therapeutic targets for lower back pain.

Key Points

  • Intervertebral disc degeneration displays critical molecular dependency on IL1R1 and TCF7L2, identifying both candidates as central diagnostic and regulatory biomarkers.
  • Analysis revealed 244 differentially expressed genes and prioritized 6 prognostic targets through LASSO regression across samples from 35 patients and 43 healthy controls.
  • Bioinformatics and machine learning algorithms integrated with WGCNA network modeling isolate key pathways in disc tissue, highlighting potential targets for future therapies.

Cite This Study

Zhang et al. (2024) studied this question.

synapsesocial.com/papers/68e60be9b6db64358759ec82https://doi.org/10.3389/fimmu.2024.1401957
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Identification of Biomarkers in Intervertebral Disc Degeneration Using Bioinformatics and Machine Learning Algorithms2024
  2. 2Identifying druggable gene-related biomarkers in intervertebral disc degeneration through transcriptome sequencing and mendelian randomization analysis2026
  3. 3Identification and validation of novel risk genes for intervertebral disc disorder by integrating large-scale multi-omics analyses and experimental studies2025
  4. 4Unveiling key genes for intervertebral disc degeneration prediction and potential drug discovery2025
  5. 5Role of hypoxia-related genes and immune infiltration in intervertebral disc degeneration: molecular mechanisms and diagnostic potential2025 · 3 citations