Bioinformatics-based screening and validation of PANoptosis-related biomarkers in periodontitis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40612110", "kind": "science-record", "revision": 3, "content_hash": "sha256:678cf483171d3aaa9fcfb51b62e62b3bac4f8fa474fdadfd1ed16f930efa5e85", "created_at": "2026-11-01T20:30:42Z", "updated_at": "2026-11-01T22:14:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics-based screening and validation of PANoptosis-related biomarkers in periodontitis.", "doi": "10.3389/fcell.2025.1619002", "pmid": "40612110", "authors": ["Qing Sun", "JinYue Hu", "RuYue Wang", "ShuiXiang Guo", "GeGe Zhang", "Ao Lu", "Xue Yang", "LiNa Wang"], "journal": "Frontiers in cell and developmental biology", "year": 2025, "abstract": "Background: Periodontitis is the most prevalent chronic inflammatory disease affecting the periodontal tissues. PANoptosis, a recently characterized form of programmed cell death, has been implicated in various pathological processes; however, its mechanistic role in periodontitis remains unclear. This study integrates multi-omics data and machine learning approaches to systematically identify and validate key PANoptosis-related biomarkers in periodontitis.\nMethods: Periodontitis-related microarray datasets (GSE16134 and GSE10334) were obtained from the GEO database, and PANoptosis-related genes were retrieved from GeneCards. Differential gene expression analysis was performed using the GSE16134 dataset, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. The intersection of differentially expressed genes and WGCNA modules was used to define differentially expressed PANoptosis-related genes (PRGs). Protein-protein interaction (PPI) networks of these PRGs were constructed using the STRING database and visualized with Cytoscape. Subnetworks were identified using the MCODE plugin. Key genes were selected based on integration with rank-sum test results. Functional enrichment analysis was performed for these key genes. Machine learning algorithms were then applied to screen for potential biomarkers. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves and box plots. The relationship between selected biomarkers and immune cell infiltration was explored using the CIBERSORT algorithm. Finally, RT-qPCR was conducted to validate biomarker expression in clinical gingival tissue samples.\nResults: Through comprehensive bioinformatics analysis and literature review, ZBP1 was identified as a PANoptosis-related biomarker in periodontitis. RT-qPCR validation demonstrated that ZBP1 expression was significantly elevated in periodontitis tissues compared to healthy periodontal tissues (P < 0.05).\nConclusion: This study provides bioinformatic evidence linking PANoptosis to periodontitis. ZBP1 was identified as a key PANoptosis-related biomarker, suggesting that periodontitis may involve activation of the ZBP1-mediated PANoptosome complex.", "claims": [{"id": "claim-method", "statement": "The diagnostic potential of the identified potential biomarkers was further evaluated by constructing receiver operating characteristic (ROC) curves using the “pROC” package in R. The area under the curve (AUC) was calculated to quantify diagnostic accuracy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40612110", "locator": "sections[10] \"2.8 Validation of diagnostic ability of potential biomarkers\" paragraph 3", "quote": "The diagnostic potential of the identified potential biomarkers was further evaluated by constructing receiver operating characteristic (ROC) curves using the “pROC” package in R. The area under the curve (AUC) was calculated to quantify diagnostic accuracy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/https://www.genecards.org.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40612110", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/https://www.genecards.org."}], "state": "complete"}, {"id": "claim-target-result", "statement": "RT-qPCR validation demonstrated that ZBP1 expression was significantly elevated in periodontitis tissues compared to healthy periodontal tissues (P < 0.05).", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "RT-qPCR validation demonstrated that ZBP1 expression was significantly elevated in periodontitis tissues compared to healthy periodontal tissues (P < 0.05)."}, "evidence": [{"asset": "source:pmid:40612110", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "RT-qPCR validation demonstrated that ZBP1 expression was significantly elevated in periodontitis tissues compared to healthy periodontal tissues (P < 0.05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "RT-qPCR validation demonstrated that ZBP1 expression was significantly elevated in periodontitis tissues compared to healthy periodontal tissues (P < 0.05).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40612110"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10334"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10334"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40612110", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2025.1619002", "sha256": "sha256:fc01bb46f715e880d870eb521d53ccd40c6682979c8424ece3b03dae571e6f96", "bytes": 33614, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcell.2025.1619002", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12222123", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE10334", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10334", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE10334", "version": null, "note": "Publicly available datasets were analyzed in this study. 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