Evidence map›Paper›PMID 35706450›Full record

ArticleFrontiers in genetics2022

Revealing Potential Spinal Cord Injury Biomarkers and Immune Cell Infiltration Characteristics in Mice.

Liang Cao, Qing Li

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Liang CaoDepartment of Traumatic Orthopedics, The Second Affiliated Hospital, University of South China, Hengyang, China.
Qing LiDepartment of Orthopedics Traumatic, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Guiyang Medical University · CNUniversity of South China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spinal cord injury (SCI) is a disabling condition with significant morbidity and mortality. Currently, no effective SCI treatment exists. This study aimed to identify potential biomarkers and characterize the properties of immune cell infiltration during this pathological event. To eliminate batch effects, we concurrently analyzed two mouse SCI datasets (GSE5296, GSE47681) from the GEO database. First, we identified differentially expressed genes (DEGs) using linear models for microarray data (LIMMA) and performed functional enrichment studies on those DEGs. Next, we employed bioinformatics and machine-learning methods to identify and define the characteristic genes of SCI. Finally, we validated them using immunofluorescence and qRT-PCR. Additionally, this study assessed the inflammatory status of SCI by identifying cell types using CIBERSORT. Furthermore, we investigated the link between key markers and infiltrating immune cells. In total, we identified 561 robust DEGs. We identified Rab20 and Klf6 as SCI-specific biomarkers and demonstrated their significance using qRT-PCR in the mouse model. According to the examination of immune cell infiltration, M0, M1, and M2 macrophages, along with naive CD8, dendritic cell-activated, and CD4 Follicular T cells may have a role in the progression of SCI. Therefore, Rab20 and Klf6 could be accessible targets for diagnosing and treating SCI. Moreover, as previously stated, immune cell infiltration may significantly impact the development and progression of SCI.

Indexed as

bioinformatics analysisimmune cellskey genesmachine learning strategiesspinal cord injury

Identifiers

PMID35706450
PMCPMC9189360
OpenAlexW4281756026

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.