Evidence map›Paper›PMID 39430607›Full record

ArticleNon-coding RNA research2025

Study on molecular mechanism of intervertebral disc degeneration by single cell hdWGCNA combined with transcriptome sequencing.

Xuan Zhao, Qijun Wang, Wei Wang, Xiaolong Chen, Shibao Lu

Abstract read
In one paragraph

Article in Non-coding RNA research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

5 authors.

Xuan ZhaoDepartment of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing, China.
Qijun WangDepartment of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing, China.
Wei WangDepartment of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing, China.
Xiaolong ChenDepartment of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing, China.
Shibao LuDepartment of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Intervertebral disc degeneration (IVDD) is one of the important causes of lower back pain, seriously affecting people's health and quality of life. This research employs single-cell analysis to identify the specific cellular subtypes and key regulatory genes associated with IVDD. Methods: We analyzed the single-cell data and screened cells that closely associated with the development of IVDD. The differential expression of feature genes between IVDD and control groups was analyzed. Additionally, drugs and regulatory transcription factors that interact with feature genes were predicted and clinically validated by reverse transcription quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and enzyme-linked immunosorbent assay (ELISA). Results: Our study identified the Chond2 cell subtype associated with IVDD and selected four feature genes influencing the development of IVDD, namely IGFBP3, ACAN, VAPA and TMEM45A, through the high-dimensional weighted gene co-expression network analysis (hdWGCNA) analysis, least absolute shrinkage and selection operator (LASSO), and random forest (RF). Besides, compared to the MDD group, IGFBP3 and TMEM45A were significantly upregulated in the SDD group, while ACAN and VAPA showed no significant difference between the two groups. ELISA testing revealed a positive correlation between IGFBP3 concentration and the grading of IVDD. Furthermore, Celecoxib may be used to treat IVDD by inhibiting IGFBP3. Conclusion: Our study identified the Chond2 cell subtype associated with IVDD and selected four feature genes influencing the development of IVDD, namely IGFBP3, ACAN, VAPA and TMEM45A. Our findings establish a robust theoretical foundation for the clinical diagnosis and treatment of IVDD patients.

Indexed as

Diagnostic markershdWGCNAIntervertebral disc degenerationMachine learningSingle cell analysis

Identifiers

PMID39430607
PMCPMC11488424

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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.