Evidence map›Paper›PMID 39281413›Full record

ArticleFrontiers in neurology2024

The shared molecular mechanism of spinal cord injury and sarcopenia: a comprehensive genomics analysis.

Binyang Wang, Xu Yang, Chuanxiong Li, Rongxing Yang, Tong Sun, Yong Yin

Abstract read
In one paragraph

Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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.

Binyang WangDepartment of Rehabilitation, The Affiliated Hospital of Yunnan University, Kunming, China.
Xu YangDepartment of Rehabilitation, The Affiliated Hospital of Yunnan University, Kunming, China.
Chuanxiong LiDepartment of Rehabilitation, The Affiliated Hospital of Yunnan University, Kunming, China.
Rongxing YangDepartment of Rehabilitation, The Affiliated Hospital of Yunnan University, Kunming, China.
Tong SunDepartment of Rehabilitation, The Affiliated Hospital of Yunnan University, Kunming, China.
Yong YinDepartment of Rehabilitation, The Affiliated Hospital of Yunnan University, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The occurrence of Spinal cord injury (SCI) brings economic burden and social burden to individuals, families and society, and the complications after SCI greatly affect the rehabilitation and treatment of patients in the later stage.This study focused on the potential biomarkers that co-exist in SCI and sarcopenia, with the expectation to diagnose and prognose patients in the acute phase and rehabilitation phase using comprehensive data analysis. Methods: The datasets used in this study were downloaded from Gene Expression Omnibus (GEO) database. Firstly, the datasets were analyzed with the "DEseq2" and "Limma" R package to identify differentially expressed genes (DEGs), which were then visualized using volcano plots. The SCI and sarcopenia DEGs that overlapped were used to construct a protein-protein interaction (PPI) network. Three algorithms were used to obtain a list of the top 10 hub genes. Next, validation of the hub genes was performed using three datasets. According to the results, the top hub genes were Results: A total of 144 overlapped genes were obtained from two datasets. Following PPI network analysis and validation, we finally identified three hub-genes ( Discussion: Our study provides comprehensive insights into how muscle changes after SCI are associated with sarcopenia by moving from RNA-seq to RNA-SEQ, including Immune infiltration landscape, pesudotime change and so on. The three hub genes identified in this study could be used to distinguish the sarcopenia state at the genomic level. Additionally, they may also play a prognostic role in evaluating the efficiency of rehabilitation training.

Indexed as

genomicsrehabilitation trainingsarcopeniaskeletal musclespinal cord injury

Identifiers

PMID39281413
PMCPMC11392746

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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.