Machine Learning and Mendelian Randomization Reveal Molecular Mechanisms and Causal Relationships of Immune-Related Biomarkers in Periodontitis.

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Differentially expressed gene (DEG) analysis was performed on periodontitis datasets GSE16134 and GSE10334 from the Gene Expression Omnibus (GEO) database, followed by weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules. Various machine learning algorithms were utilized to construct predictive models, highlighting core genes, while MR assessed the causal relationships between these genes and periodontitis. Additionally, immune infiltration analysis and single-cell sequencing were employed to explore the roles of key genes in immunity and their expression across different cell types. The integration of machine learning, MR, and single-cell sequencing represents a novel approach that significantly enhances our understanding of the immune dynamics and gene interactions in periodontitis. The study identified 682 significant DEGs, with WGCNA revealing seven gene modules associated with periodontitis and 471 core candidate genes. Among the 113 machine learning algorithms tested, XGBoost was the most effective in identifying periodontitis samples, leading to the selection of 19 core genes. MR confirmed significant causal relationships between CD93, CD69, and CXCL6 and periodontitis. Further analysis showed that these genes were correlated with various immune cells and exhibited specific expression patterns in periodontitis tissues. The findings suggest that CD93, CD69, and CXCL6 are closely related to the progression of periodontitis, with MR confirming their causal links to the disease. These genes have potential applications in the diagnosis and treatment of periodontitis, offering new insights into the disease's molecular mechanisms and providing valuable resources for precision medicine approaches in periodontitis management. Limitations of this study include the demographic and sample size constraints of the datasets, which may impact the generalizability of the findings. Future research is needed to validate these biomarkers in larger, diverse cohorts and to investigate their functional roles in the pathogenesis of periodontitis.", "claims": [{"id": "claim-method", "statement": "The limma package was used to analyze DEGs between periodontitis samples and healthy controls [31].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39717623", "locator": "sections[4] \"2.2. Differentially Expressed Genes (DEGs) Analysis\" paragraph 2", "quote": "The limma package was used to analyze DEGs between periodontitis samples and healthy controls [31]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used in this study are available in the Gene Expression Omnibus (GEO) database under the accession numbers “GSE16134,” “GSE10334,” “GSE223924,” and “GSE171213.” The expression quantitative trait loci (eQTL) data used for Mendelian randomization analysis can be accessed from the IEU Open GWAS database (https://gwas.mrcieu.ac.uk/), and the protein quantitative trait loci (pQTL) data can be accessed from the deCODE Genetics database (https://www.decode.com/summarydata/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39717623", "locator": "sections[24] \"Data Availability Statement\" paragraph 2", "quote": "The datasets used in this study are available in the Gene Expression Omnibus (GEO) database under the accession numbers “GSE16134,” “GSE10334,” “GSE223924,” and “GSE171213.” The expression quantitative trait loci (eQTL) data used for Mendelian randomization analysis can be accessed from the IEU Open GWAS database (https://gwas.mrcieu.ac.uk/), and the protein quantitative trait loci (pQTL) data can be accessed from the deCODE Genetics database (https://www.decode.com/summarydata/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified 682 significant differentially expressed genes in periodontitis samples compared with controls, including 447 upregulated and 235 downregulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39717623", "locator": "sections[14] \"3.1. Identification of Periodontitis-Related Genes\" paragraph 3", "quote": "After initial screening, we identified 682 significantly DEGs in the periodontitis group compared to the control group, including 447 upregulated and 235 downregulated genes (Figure 2A,B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified 682 significant differentially expressed genes in periodontitis samples compared with controls, including 447 upregulated and 235 downregulated genes.", "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:39717623"], "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. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:39717623", "data:geo:GSE10334"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE10334"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:43:29Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14074, "completion_tokens": 1864, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39717623", "type": "paper", "label": "pmid:39717623"}, {"id": "source:pmid:39717623", "type": "manuscript", "label": "PMID 39717623 full text"}, {"id": "data:geo:GSE10334", "type": "dataset", "label": "GEO GSE10334"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39717623", "type": "paper", "label": "pmid:39717623"}, {"id": "source:pmid:39717623", "type": "manuscript", "label": "PMID 39717623 full text"}, {"id": "data:geo:GSE10334", "type": "dataset", "label": "GEO GSE10334"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39717623", "type": "extraction", "used": ["source:pmid:39717623"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE10334", "type": "accession_resolution", "used": ["data:geo:GSE10334"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39717623", "parent_hash": "sha256:11d8bdfc877f79d48cf8dee690ea9fcbdb7f1fe0955c4afecd6ae4c57379df66", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE10334 licence not read", "asset:data:geo:GSE10334 not downloaded", "asset:data:geo:GSE16134 licence not read", "asset:data:geo:GSE16134 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The limma package was used to analyze DEGs between periodontitis samples and healthy controls [31]."], "order_basis": "analysis_only", "id": "assay:1"}]}