Evidence map›Paper›PMID 34532385›Full record

ArticleAnnals of translational medicine2021

Weighted gene co-expression network analysis reveals that CXCL10, IRF7, MX1, RSAD2, and STAT1 are related to the chronic stage of spinal cord injury.

Qi Wang, Liang Liu, Jiangang Cao, Muhetidier Abula, Yasen Yimingjiang, Shiqing Feng

Open access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

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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

5 citing papers in PubMed, 8 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

6 authors at 1 institution in 1 country.

Qi WangDepartment of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China.
Liang LiuDepartment of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China.
Jiangang CaoDepartment of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China.
Muhetidier AbulaDepartment of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China.
Yasen YimingjiangDepartment of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China.
Shiqing FengDepartment of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China.
Tianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe process of spinal cord injury involves acute, subacute, and chronic stages; however, the specific pathological mechanism remains unclear. In this study, weighted gene co-expression network analysis (WGCNA) was used to clarify specific modules and hub genes that associated with SCI.

methodsThe gene expression profiles GEO Series (GSE)45006 and GEO Series (GSE)2599 were downloaded, and the co-expression network modules were identified by the WGCNA package. The protein-protein interaction (PPI) network and Venn diagram were constructed to identify hub genes. Quantitative real-time polymerase chain reaction (QRT-PCR) was used to quantify the degree of the top five candidate genes. Correlation analysis was also carried out between hub genes and immune infiltration.

resultsIn total, 14,402 genes and seven modules were identified. The brown module was considered to be the most critical module for the chronic stage of SCI, which contained 775 genes that were primarily associated with various biological processes, including extracellular structure organization, lysosome, isoprenoid biosynthesis, response to nutrients, response to wounding, sulfur compound metabolic process, cofactor metabolic process, and ossification. Furthermore, C-X-C motif chemokine ligand 10 (

conclusionsThere were significant differences among the five hub genes (

Indexed as

bioinformatics analysischronic stageSpinal cord injury (SCI)weighted gene co-expression network analysis (WGCNA)

Identifiers

PMID34532385
PMCPMC8421925
OpenAlexW3191063485

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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.