Evidence map›Paper›PMID 40756519›Full record

ArticleFrontiers in endocrinology2025

Shared diagnostic genes and potential mechanisms between intervertebral disc degeneration and diabetes mellitus revealed by integrated transcriptomic analysis and machine learning.

Bao Song, Jianmin Wang, Hao Tang, Huadong Li, Wanli Zhang

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2025. 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. Review
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.

Bao SongDepartment of Tuina, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Jianmin WangDepartment of Tuina, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Hao TangDepartment of Tuina, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Huadong LiDepartment of Tuina, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Wanli ZhangDepartment of Tuina, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Intervertebral disc degeneration (IDD) and diabetes mellitus (DM) are clinically associated beyond traditional risk factors, yet the shared molecular mechanisms remain unclear. Methods: We integrated transcriptomic data from IDD and DM cohorts, performed differential expression and protein-protein interaction (PPI) network analyses, and applied machine learning to identify shared diagnostic genes. Pathway enrichment, immune infiltration analyses, and experimental qPCR validation were conducted to explore mechanisms and confirm findings. Results: We identified 138 shared differentially expressed genes enriched in immune-related pathways. Seven hub genes, including PRTN3, were identified by Random Forest models. PRTN3 showed consistent upregulation across discovery, validation, and internal cohorts. Pathway and immune analyses revealed strong associations between PRTN3 expression and neutrophil-related processes in both IDD and DM. Experimental validation confirmed PRTN3 upregulation in blood samples from patients with concurrent IDD and DM. Discussion: Our findings suggest that immune-inflammatory mechanisms, particularly involving neutrophils, underlie the comorbidity between IDD and DM. PRTN3 emerges as a promising shared diagnostic biomarker and potential therapeutic target for these conditions.

Indexed as

Diabetes MellitusIntervertebral Disc DegenerationMachine LearningTranscriptomeAdultBiomarkersFemaleGene Expression ProfilingHumansMaleMiddle AgedProtein Interaction MapsBiomarkersdiabetes mellitusdiagnosticimmune infiltrationintervertebral disc degenerationPRTN3

Identifiers

PMID40756519
PMCPMC12313482

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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.