Evidence map›Paper›PMID 40858849›Full record

ArticleScientific reports2025

Screening of hub genes and immunocytes related to tendon injury based on bioinformatics and machine learning models.

Shulong Sun, Hua Li, Yan Li, Liubing Yang, Juanjuan Zhang, Yujing Cao

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

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

2 citing papers in PubMed.

  1. Article
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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.

Shulong SunDepartment of Emergency Trauma Center, Henan University of Chinese Medicine Henan Province Hospital of Traditional Chinese Medicine, Zhengzhou, China.
Hua LiDepartment of Emergency Trauma Center, Henan University of Chinese Medicine Henan Province Hospital of Traditional Chinese Medicine, Zhengzhou, China.
Yan LiDepartment of Emergency Trauma Center, Henan University of Chinese Medicine Henan Province Hospital of Traditional Chinese Medicine, Zhengzhou, China.
Liubing YangDepartment of Emergency Trauma Center, Henan University of Chinese Medicine Henan Province Hospital of Traditional Chinese Medicine, Zhengzhou, China.
Juanjuan ZhangDepartment of Emergency Trauma Center, Henan University of Chinese Medicine Henan Province Hospital of Traditional Chinese Medicine, Zhengzhou, China.
Yujing CaoDepartment of Emergency Trauma Center, Henan University of Chinese Medicine Henan Province Hospital of Traditional Chinese Medicine, Zhengzhou, China. bravecao@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tendon injury is a common and challenging clinical problem, and its healing process involves complex cellular and biological factors. Therefore, this study aims to reveal the mechanism of tendon healing and provide theoretical basis for clinical treatment. We first selected GSE26051 dataset from the GEO database and used R language to obtain 721 DEGs (459 up-regulated and 262 down-regulated). Subsequently, the 7378 genes of tendon injury obtained from the GeneCards database were intersected with DEGs to obtain 228 common genes. We constructed a PPI network of common genes using the STRING database, visualized it using Cytoscape software, and selected the top 10 (MYH6, MYL3, MYH1, MYH8, MYL1, TTN, TCAP, PKP2, ACTN2, CSRP3) genes through the CytoHubba plugin. We further identified hub genes (MYH1, MYH6, PKP2, MYH8) via machine learning models. Afterwards, the cytoskeleton in muscle cells and IL-17 signaling pathways were obtained by GO and KEGG analysis of common genes. Finally, the macrophages M2 was screened through immune infiltration analysis. This study revealed that hub genes such as MYH1, MYH6, MYH8 and PKP2 were mainly enriched in the cytoskeleton in muscle cells signaling pathway, and macrophage M2 played an important role in the inflammatory phase of tendon healing.

Indexed as

Computational BiologyMachine LearningTendon InjuriesDatabases, GeneticGene Expression ProfilingGene Regulatory NetworksHumansMacrophagesProtein Interaction MapsSignal TransductionHub genes2immunocytes3Machine learning5Signaling pathways4Tendon injury1

Identifiers

PMID40858849
PMCPMC12381286

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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.