ArticleFrontiers in immunology2022
Screening of crosstalk and pyroptosis-related genes linking periodontitis and osteoporosis based on bioinformatics and machine learning.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 46 citations in OpenAlex.
- A self-attention-based deep learning model for identifying key genes in insect pupal metamorphosis.BMC genomics · 2026Article
- Construction and evaluation of a bladder cancer prognosis model based on super-enhancer-associated genes.Discover oncology · 2026Article
- Identification of common genetic features and pathways for osteoporosis with frozen shoulder by integrated bioinformatics analysis and machine learning.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Revolutionizing osteoporosis management: the emerging role of immunotherapeutic and next-generation therapies.Inflammopharmacology · 2025Review
- Neutrophil extracellular traps drive osteoporosis via NCF2-dependent signaling: integrated transcriptomics with mechanistic validation.Redox report : communications in free radical research · 2025Article
- Senescence-related gene signatures in Crohn's disease: integrating bulk and single-cell RNA sequencing analysis.Scientific reports · 2025Article
- Identification and mechanistic insights of ubiquitin-proteasome system and pyroptosis-related biomarkers in type 2 diabetes mellitus.World journal of diabetes · 2025Article
- Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight.BMC pharmacology & toxicology · 2025Article
- Ferroptosis regulation by traditional chinese medicine for ischemic stroke intervention based on network pharmacology and data mining.PloS one · 2025Article
- Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.Frontiers in neurology · 2025Article
- From Inflammation to Bone Loss: The Multifaceted Role of Neutrophils in Osteoporosis.International journal of inflammation · 2025Review
- Exploring Pyroptosis-related Signature Genes and Potential Drugs in Ulcerative Colitis by Transcriptome Data and Animal Experimental Validation.Inflammation · 2024Article
- Trends and hotspots in research on osteoporosis and nutrition from 2004 to 2024: a bibliometric analysis.Journal of health, population, and nutrition · 2024Review
- ARMCX3 regulates ROS signaling, affects neural differentiation and inflammatory microenvironment in dental pulp stem cells.Heliyon · 2024Article
- Revealing the key role of cuproptosis in osteoporosis via the bioinformatic analysis and experimental validation of cuproptosis-related genes.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- Pyroptosis in Skeleton Diseases: A Potential Therapeutic Target Based on Inflammatory Cell Death.International journal of molecular sciences · 2024Review
- Article
- Identification and bioinformatics analysis of genes associated with pyroptosis in spinal cord injury of rat and mouse.Scientific reports · 2024Article
- Exploring the molecular mechanisms of ferroptosis-related genes in periodontitis: a multi-dataset analysis.BMC oral health · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and objective: This study aimed to identify crosstalk genes between periodontitis (PD) and osteoporosis (OP) and potential relationships between crosstalk and pyroptosis-related genes. Methods: PD and OP datasets were downloaded from the GEO database and were performed differential expression analysis to obtain DEGs. Overlapping DEGs got crosstalk genes linking PD and OP. Pyroptosis-related genes were obtained from literature reviews. Pearson coefficients were used to calculate crosstalk and pyroptosis-related gene correlations in the PD and OP datasets. Paired genes were obtained from the intersection of correlated genes in PD and OP. PINA and STRING databases were used to conduct the crosstalk-bridge-pyroptosis genes PPI network. The clusters in which crosstalk and pyroptosis-related genes were mainly concentrated were defined as key clusters. The key clusters' hub genes and the included paired genes were identified as key crosstalk-pyroptosis genes. Using ROC curve analysis and XGBoost screened key genes. PPI subnetwork, gene-biological process and gene-pathway networks were constructed based on key genes. In addition, immune infiltration was analyzed on the PD dataset using the CIBERSORT algorithm. Results: A total of 69 crosstalk genes were obtained. 13 paired genes and hub genes TNF and EGFR in the key clusters (cluster2, cluster8) were identified as key crosstalk-pyroptosis genes. ROC and XGBoost showed that PRKCB, GSDMD, ARMCX3, and CASP3 were more accurate in predicting disease than other key crosstalk-pyroptosis genes while better classifying properties as a whole. KEGG analysis showed that PRKCB, GSDMD, ARMCX3, and CASP3 were involved in neutrophil extracellular trap formation and MAPK signaling pathway pathways. Immune infiltration results showed that all four key genes positively correlated with plasma cells and negatively correlated with T cells follicular helper, macrophages M2, and DCs. Conclusion: This study shows a joint mechanism between PD and OP through crosstalk and pyroptosis-related genes. The key genes PRKCB, GSDMD, ARMCX3, and CASP3 are involved in the neutrophil extracellular trap formation and MAPK signaling pathway, affecting both diseases. These findings may point the way to future research.
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