ArticleBMC oral health2025
Identification and analysis of neutrophil extracellular trap-related genes in periodontitis via bioinformatics and experimental verification.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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Who cites it
2 citing papers in PubMed.
- Functional Foods and Micro- and Nanoplastics: Advances in Precision Nutritional Medicine for Oral-Gut-Brain Axis Health.Antioxidants (Basel, Switzerland) · 2026Review
- Integrating bulk and single-cell RNA sequencing data to dissect genetic links between periodontitis and obstructive sleep apnea.Sleep & breathing = Schlaf & Atmung · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundEmerging evidence highlights the significant role of neutrophil extracellular traps (NETs) in periodontitis, though the precise mechanisms remain insufficiently understood. This study intends to investigate the comprehensive effects of NET-related genes (NRGs) on periodontitis by bioinformatic analysis.
methodsThe microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed NRGs (DE-NRGs) were identified and functionally annotated. Then, machine learning algorithms were exploited to screen hub NRGs, and a predictive model was constructed based on these hub NRGs. Moreover, the expression level of CXCR4, one of the hub NRGs, was experimentally validated.
resultsEighty-three DE-NRGs were identified and mainly correlated with multiple periodontitis-related pathways. Then, a diagnostic NRG signature based on 7-hub NRGs (LPAR3, CXCR4, F3, MAPK7, KCNN3, SYK, and HIF1A) was constructed using two different machine learning algorithms. The diagnostic NRG signature demonstrated favorable predictive efficacy, with an AUC of 0.929 in the training and 0.936 in the validation cohorts. The mouse periodontitis model verified that CXCR4 and HIF1A was markedly upregulated in periodontitis tissues.
conclusionThis study reveals that NRGs hold great potential as a robust and promising parameter for assessing periodontitis diagnosis. Targeting NRGs could represent a potential direction for future research into periodontitis treatment. CLINICAL TRIAL NUMBER: Not applicable.
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