Open Access

Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms

  • Authors:
    • Lei Yan
    • Chu Chen
    • Lingling Wang
    • Hongxiang Hong
    • Chunshuai Wu
    • Jiayi Huang
    • Jiawei Jiang
    • Jiajia Chen
    • Guanhua Xu
    • Zhiming Cui
  • View Affiliations

  • Published online on: May 22, 2024     https://doi.org/10.3892/etm.2024.12581
  • Article Number: 292
  • Copyright: © Yan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Spinal cord injury (SCI) is a severe neurological complication following spinal fracture, which has long posed a challenge for clinicians. Microglia play a dual role in the pathophysiological process after SCI, both beneficial and detrimental. The underlying mechanisms of microglial actions following SCI require further exploration. The present study combined three different machine learning algorithms, namely weighted gene co‑expression network analysis, random forest analysis and least absolute shrinkage and selection operator analysis, to screen for differentially expressed genes in the GSE96055 microglia dataset after SCI. It then used protein‑protein interaction networks and gene set enrichment analysis with single genes to investigate the key genes and signaling pathways involved in microglial function following SCI. The results indicated that microglia not only participate in neuroinflammation but also serve a significant role in the clearance mechanism of apoptotic cells following SCI. Notably, bioinformatics analysis and lipopolysaccharide + UNC569 (a MerTK‑specific inhibitor) stimulation of BV2 cell experiments showed that the expression levels of Anxa2, Myo1e and Spp1 in microglia were significantly upregulated following SCI, thus potentially involved in regulating the clearance mechanism of apoptotic cells. The present study suggested that Anxa2, Myo1e and Spp1 may serve as potential targets for the future treatment of SCI and provided a theoretical basis for the development of new methods and drugs for treating SCI.
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July-2024
Volume 28 Issue 1

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Spandidos Publications style
Yan L, Chen C, Wang L, Hong H, Wu C, Huang J, Jiang J, Chen J, Xu G, Cui Z, Cui Z, et al: Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms. Exp Ther Med 28: 292, 2024.
APA
Yan, L., Chen, C., Wang, L., Hong, H., Wu, C., Huang, J. ... Cui, Z. (2024). Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms. Experimental and Therapeutic Medicine, 28, 292. https://doi.org/10.3892/etm.2024.12581
MLA
Yan, L., Chen, C., Wang, L., Hong, H., Wu, C., Huang, J., Jiang, J., Chen, J., Xu, G., Cui, Z."Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms". Experimental and Therapeutic Medicine 28.1 (2024): 292.
Chicago
Yan, L., Chen, C., Wang, L., Hong, H., Wu, C., Huang, J., Jiang, J., Chen, J., Xu, G., Cui, Z."Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms". Experimental and Therapeutic Medicine 28, no. 1 (2024): 292. https://doi.org/10.3892/etm.2024.12581