ArticleAging2023
Comprehensive landscape of immune-based classifier related to early diagnosis and macrophage M1 in spinal cord injury.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Role of Microglial Dysfunction in Parkinson's Disease: From Multifactorial Causes to Neurodegeneration.Neuroscience bulletin · 2026Review
- Eupatilin ameliorates spinal cord injury by inhibiting damage-associated microglia and optimizing the regenerative microenvironment.iScience · 2025Article
- The NFATC2/Nrf2 cascade regulates spinal cord ischemia-reperfusion injury by controlling inflammation, apoptosis and oxidative stress.Regenerative therapy · 2025Article
- Article
- Deciphering the crosstalk of immune dysregulation between COVID-19 and idiopathic inflammatory myopathy.Frontiers in immunology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Numerous studies have documented that immune responses are crucial in the pathophysiology of spinal cord injury (SCI). Our study aimed to uncover the function of immune-related genes (IRGs) in SCI. Here, we comprehensively evaluated the transcriptome data of SCI and healthy controls (HC) obtained from the GEO Database integrating bioinformatics and experiments. First, a total of 2067 DEGs were identified between the SCI and HC groups. Functional enrichment analysis revealed substantial immune-related pathways and functions that were abnormally activated in the SCI group. Immune analysis revealed that myeloid immune cells were predominantly upregulated in SCI patients, while a large number of lymphoid immune cells were dramatically downregulated. Subsequently, 51 major IRGs were screened as key genes involved in SCI based on the intersection of the results of WGCNA analysis, DEGs, and IRGs. Based on the expression profiles of these genes, two distinct immune modulation patterns were recognized exhibiting opposite immune characteristics. Moreover, 2 core IRGs (FCER1G and NFATC2) were determined to accurately predict the occurrence of SCI via machine learning. qPCR analysis was used to validate the expression of core IRGs in an external independent cohort. Finally, the expression of these core IRGs was validated by sequencing, WB, and IF analysis
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.