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pmid:40362543

Integration of Bioinformatics and Machine Learning Strategies Identifies Ferroptosis and Immune Infiltration Signatures in Peri-Implantitis.

Jieying Huang · Yaokun Zou · Huizhi Deng · Jun Zha · Janak Lal Pathak · Yaxin Chen · Qing Ge · Liping Wang

International journal of molecular sciences · 2025 doi:10.3390/ijms26094306

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

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title
Integration of Bioinformatics and Machine Learning Strategies Identifies Ferroptosis and Immune Infiltration Signatures in Peri-Implantitis.
doi
10.3390/ijms26094306
pmid
40362543
authors array · 8 items
[0]
Jieying Huang
[1]
Yaokun Zou
[2]
Huizhi Deng
[3]
Jun Zha
[4]
Janak Lal Pathak
[5]
Yaxin Chen
[6]
Qing Ge
[7]
Liping Wang
journal
International journal of molecular sciences
year
2025
abstract
Peri-implantitis (PI) is a chronic inflammatory disease that ultimately leads to the dysfunction and loss of implants with established osseointegration. Ferroptosis has been implicated in the progression of PI, but its precise mechanisms remain unclear. This study explores the molecular mechanisms of ferroptosis in the pathology of PI through bioinformatics, offering new insights into its diagnosis and treatment. The microarray datasets for PI (GSE33774 and GSE106090) were retrieved from the GEO database. The differentially expressed genes (DEGs) and ferroptosis-related genes (FRGs) were intersected to obtain PI-Ferr-DEGs. Using three machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Boruta, we successfully identified the most crucial biomarkers. Additionally, these key biomarkers were validated using a verification dataset (GSE223924). Gene set enrichment analysis (GSEA) was also utilized to analyze the associated gene enrichment pathways. Moreover, immune cell infiltration analysis compared the differential immune cell profiles between PI and control samples. Also, we targeted biomarkers for drug prediction and conducted molecular docking analysis on drugs with potential development value. A total of 13 PI-Ferr-DEGs were recognized. Machine learning and validation confirmed toll-like receptor-4 (TLR4) and FMS-like tyrosine kinase 3 (FLT3) as ferroptosis biomarkers in PI. In addition, GSEA was significantly enriched by the biomarkers in the cytokine-cytokine receptor interaction and chemokine signaling pathway. Immune infiltration analysis revealed that the levels of B cells, M1 macrophages, and natural killer cells differed significantly in PI. Ibudilast and fedratinib were predicted as potential drugs for PI that target TLR4 and FLT3, respectively. Finally, the occurrence of ferroptosis and the expression of the identified key markers in gingival fibroblasts under inflammatory conditions were validated by RT-qPCR and immunofluorescence analysis. This study identified TLR4 and FLT3 as ferroptosis and immune cell infiltration signatures in PI, unraveling potential novel targets to treat PI.
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sights into its diagnosis and treatment. The microarray datasets for PI (GSE33774 and GSE106090) wer
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40362543", "kind": "science-record", "revision": 3, "content_hash": "sha256:3ef8f453a30cca6b7ff90dd3947d380dbe989a89905dad6353b91e4e7392fb1e", "created_at": "2026-11-01T20:30:31Z", "updated_at": "2026-11-01T22:14:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integration of Bioinformatics and Machine Learning Strategies Identifies Ferroptosis and Immune Infiltration Signatures in Peri-Implantitis.", "doi": "10.3390/ijms26094306", "pmid": "40362543", "authors": ["Jieying Huang", "Yaokun Zou", "Huizhi Deng", "Jun Zha", "Janak Lal Pathak", "Yaxin Chen", "Qing Ge", "Liping Wang"], "journal": "International journal of molecular sciences", "year": 2025, "abstract": "Peri-implantitis (PI) is a chronic inflammatory disease that ultimately leads to the dysfunction and loss of implants with established osseointegration. Ferroptosis has been implicated in the progression of PI, but its precise mechanisms remain unclear. This study explores the molecular mechanisms of ferroptosis in the pathology of PI through bioinformatics, offering new insights into its diagnosis and treatment. The microarray datasets for PI (GSE33774 and GSE106090) were retrieved from the GEO database. The differentially expressed genes (DEGs) and ferroptosis-related genes (FRGs) were intersected to obtain PI-Ferr-DEGs. Using three machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Boruta, we successfully identified the most crucial biomarkers. Additionally, these key biomarkers were validated using a verification dataset (GSE223924). Gene set enrichment analysis (GSEA) was also utilized to analyze the associated gene enrichment pathways. Moreover, immune cell infiltration analysis compared the differential immune cell profiles between PI and control samples. Also, we targeted biomarkers for drug prediction and conducted molecular docking analysis on drugs with potential development value. A total of 13 PI-Ferr-DEGs were recognized. Machine learning and validation confirmed toll-like receptor-4 (TLR4) and FMS-like tyrosine kinase 3 (FLT3) as ferroptosis biomarkers in PI. In addition, GSEA was significantly enriched by the biomarkers in the cytokine-cytokine receptor interaction and chemokine signaling pathway. Immune infiltration analysis revealed that the levels of B cells, M1 macrophages, and natural killer cells differed significantly in PI. Ibudilast and fedratinib were predicted as potential drugs for PI that target TLR4 and FLT3, respectively. Finally, the occurrence of ferroptosis and the expression of the identified key markers in gingival fibroblasts under inflammatory conditions were validated by RT-qPCR and immunofluorescence analysis. This study identified TLR4 and FLT3 as ferroptosis and immune cell infiltration signatures in PI, unraveling potential novel targets to treat PI.", "claims": [{"id": "claim-method", "statement": "Comparisons were performed using the Wilcoxon test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40362543", "locator": "sections[32] \"4.17. Statistical Analysis\" paragraph 2", "quote": "Comparisons were performed using the Wilcoxon test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets are freely available at http://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 August 2024). All data originate from public sources.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40362543", "locator": "sections[39] \"notes\" paragraph 1", "quote": "Datasets are freely available at http://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 August 2024). All data originate from public sources."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified TLR4 and FLT3 as ferroptosis and immune cell infiltration signatures in peri-implantitis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40362543", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "This study identified TLR4 and FLT3 as ferroptosis and immune cell infiltration signatures in PI, unraveling potential novel targets to treat PI."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study identified TLR4 and FLT3 as ferroptosis and immune cell infiltration signatures in peri-implantitis.", "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:40362543"], "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:GSE106090"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE106090"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40362543", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/ijms26094306", "sha256": "sha256:7ec88031a87b4f4e554bdf39ff482b3cee3a8f771be82190cdcc1e8e2f6b39e3", "bytes": 59434, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/ijms26094306", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12072437", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE106090", "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=GSE106090", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE106090", "version": null, "note": "Datasets are freely available at http://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 August 2024). 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All data originate from public sources.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Datasets are freely available at http://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 August 2024). All data originate from public sources.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM7006826", "GSM7006827", "GSM7006828", "GSM7006829", "GSM7006830", "GSM7006831", "GSM7006832", "GSM7006833", "GSM7006834", "GSM7006835", "GSM7006836", "GSM7006837", "GSM7006838", "GSM7006839", "GSM7006840", "GSM7006841", "GSM7006842", "GSM7006843", "GSM7006844", "GSM7006845", "GSM7006846", "GSM7006847", "GSM7006848", "GSM7006849", "GSM7006850", "GSM7006851", "GSM7006852", "GSM7006853", "GSM7006854", "GSM7006855"], "sample_count": 30, "group_sizes": null, "group_sizes_basis": null, "platform": "24676", "taxon": "Homo sapiens", "title": "Comparative transcriptome analysis of periodontitis and peri-implantitis in the same subject", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE33774", "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=GSE33774", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE33774", "version": null, "note": "Datasets are freely available at http://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 August 2024). 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All data originate from public sources.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM835234", "GSM835235", "GSM835236", "GSM835237", "GSM835238", "GSM835239", "GSM835240", "GSM835241", "GSM835242", "GSM835243", "GSM835244", "GSM835245", "GSM835246", "GSM835247", "GSM835248", "GSM835249", "GSM835250", "GSM835251", "GSM835252", "GSM835253", "GSM835254", "GSM835255"], "sample_count": 22, "group_sizes": null, "group_sizes_basis": null, "platform": "6244", "taxon": "Homo sapiens", "title": "Expression data from gingival tissue", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE106090 not downloaded", "asset:data:geo:GSE106090 licence not read", "asset:data:geo:GSE223924 not downloaded", "asset:data:geo:GSE223924 licence not read", "asset:data:geo:GSE33774 not downloaded", "asset:data:geo:GSE33774 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Comparisons were performed using the Wilcoxon test."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "PCA"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "Support Vector Machine"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-5", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "PPI"}}, {"id": "method-6", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "targets": [], "gaps": ["method.group_comparison_wilcoxon"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: wilcoxon."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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The microarray datasets for PI (GSE33774 and GSE106090) wer"], "order_basis": "paper_text_order:517", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["er inflammatory conditions were validated by RT-qPCR and immunofluorescence analysis. This study", "inflammatory conditions were validated by RT-qPCR and immunofluorescence analysis. This study"], "order_basis": "paper_text_order:2146", "id": "assay:2"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ory conditions were validated by RT-qPCR and immunofluorescence analysis. 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