Evidence map›Paper›PMID 40603950›Full record

ArticleScientific reports2025

Identification and verification of oxidative stress-related genes in the diagnosis of osteoporosis.

Zhenchuan Liu, Xu Yang, Zexin Wang, Qi Li, Hanwen Gu, Qunbo Meng, Yuanqiang Zhang

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, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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 synthesis or guideline pooled it.

  1. Pooled it
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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

7 authors.

Zhenchuan Liu *Department of Orthopaedic Surgery, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, 250012, Shandong, China.ORCID http://orcid.org/0000-0002-4592-4966
Xu Yang *Department of Spine Surgery, The Second Hospital of Shandong University, Jinan, 250033, China.
Zexin Wang *Department of Orthopaedic Surgery, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, 250012, Shandong, China.
Qi LiDepartment of Orthopaedic Surgery, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, 250012, Shandong, China.
Hanwen GuDepartment of Orthopaedic Surgery, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, 250012, Shandong, China.ORCID http://orcid.org/0000-0002-2716-9055
Qunbo MengDepartment of Orthopaedic Surgery, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, 250012, Shandong, China. mengqunbo@126.com.
Yuanqiang ZhangDepartment of Orthopaedic Surgery, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, 250012, Shandong, China. drzhyq@126.com.

Funding

National Key Research and Development Program of China 2020YFC2009004National Natural Science Foundation of China 81900804
6 · The paper itself

Abstract

The association between oxidative stress and osteoporosis (OP) has been substantiated by numerous studies; however, the precise underlying mechanism remains elusive. Hence, we employed bioinformatics methodologies to investigate this phenomenon. OP-related datasets (GSE56815 and GSE7158) were utilized in this study. Key module genes linked to oxidative stress-related genes (OS-RGs) were acquired through weighted gene coexpression network analysis (WGCNA). By crossing key module genes and differentially expressed genes (DEGs) from differential expression analysis, candidate genes were obtained. Subsequently, diagnostic genes were obtained through receiver operating characteristic (ROC) curve analysis and expression evaluation. Furthermore, a nomogram model was developed utilizing these genes to assess the collective predictive capacity of the diagnostic genes for OP comprehensively. Additionally, gene set variation analysis (GSVA), immune analysis, and construction of a molecular regulatory network were implemented to further understand the mechanism of the diagnostic genes in OP. We also preliminarily verified the effect of NAPG on osteogenic differentiation through experiments such as ALP, ARS and Western Blot. A total of 101 candidate genes were identified by crossing 395 DEGs and 1,730 key module genes. Importantly, NAPG, NCOA1, and TRIM44 were identified as diagnostic genes associated with oxidative stress in OP. The nomogram model showed the potential predictive ability of OP. Moreover, the GSVA results demonstrated that low expression of NAPG, NCOA1, and TRIM44 was enriched in the oestrogen response early signalling pathway. Moreover, these diagnostic genes were strongly correlated with multiple immune-related genes in the two datasets. Additionally, we identified several important factors that have regulatory relationships with diagnostic genes, such as MEF2A, STAT3, YY1, CREB1, hsa-mir-132-3p, and hsa-mir-148a-3p. Finally, it was verified at the protein and cellular levels that NAPG inhibits osteogenic differentiation and may play a crucial role in osteoporosis. NAPG, NCOA1, and TRIM44 were found to be associated with the diagnosis of OP, suggesting novel opportunities for diagnosing and treating OP.

Indexed as

OsteoporosisOxidative StressCell DifferentiationComputational BiologyDatabases, GeneticGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansNomogramsOsteogenesisDiagnostic genesNomogramOsteoporosisOxidative stress

Identifiers

PMID40603950
PMCPMC12222490

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