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pmid:40796870

FGFR2 identified as a NETs-associated biomarker and therapeutic target in diabetic foot ulcers.

Linrui Dai · Shunli Rui · Mengling Yang · Shiyan Yu · Qingqing Chen · Hongyan Wang · Bo Deng · Liling Deng · Wei Hao · Xiaohua Wu · David G Armstrong · Zhidong Cao · Xiaodong Duan · Wuquan Deng

European journal of medical research · 2025 doi:10.1186/s40001-025-03012-5

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40796870
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science-record
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sha256:abd57436ebc1b2b9790bdba6c7889e67726dfcc21ebc14b371d07347aa642803
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created_at
2026-11-01T20:30:53Z
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2026-11-01T22:57:00Z
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paper object · 10 fields
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title
FGFR2 identified as a NETs-associated biomarker and therapeutic target in diabetic foot ulcers.
doi
10.1186/s40001-025-03012-5
pmid
40796870
authors array · 14 items
[0]
Linrui Dai
[1]
Shunli Rui
[2]
Mengling Yang
[3]
Shiyan Yu
[4]
Qingqing Chen
[5]
Hongyan Wang
[6]
Bo Deng
[7]
Liling Deng
[8]
Wei Hao
[9]
Xiaohua Wu
[10]
David G Armstrong
[11]
Zhidong Cao
[12]
Xiaodong Duan
[13]
Wuquan Deng
journal
European journal of medical research
year
2025
abstract
BACKGROUND: Diabetic foot ulcer (DFU) is characterized by impaired wound healing and chronic inflammation, partly driven by the excessive formation of neutrophil extracellular traps (NETs). However, the molecular mediators linking NETs to failed tissue regeneration remain poorly understood. This study aimed to identify and validate novel NETs-associated biomarkers in DFU using an integrative bioinformatics and machine learning approach. METHODS: Differentially expressed genes (DEGs) were identified from the GEO dataset GSE134431. These DEGs were intersected with a NETs-related gene set to identify NETs-associated DEGs (NETDEGs). LASSO logistic regression and Random Forest algorithms were applied to the NETDEGs to select key feature genes. The top candidate, Fibroblast Growth Factor Receptor 2 (FGFR2), was validated in two independent datasets (GSE7014 and GSE147890). Its expression was further confirmed in human DFU tissues and diabetic mouse models using qPCR and immunohistochemistry. The effect of a high-glucose environment on FGFR2 expression in neutrophils was assessed in vitro. Finally, molecular docking technique was used to screen for existing drugs targeting FGFR2, with top candidates validated at the cellular level. RESULTS: Our analysis identified FGFR2 as a key downregulated gene at the intersection of DFU pathology and NETs-related pathways. FGFR2 expression was significantly reduced in DFU tissues across all datasets and in our experimental models, where its downregulation correlated with increased NETs accumulation. FGFR2 demonstrated strong diagnostic potential, with an AUC of 1.00 in the training set. In vitro, high glucose conditions suppressed FGFR2 expression in neutrophils. From 32 glucose-lowering drugs, Canagliflozin and Gliquidone were found to significantly upregulate FGFR2 protein expression in neutrophils, suggesting a potential modulatory effect. CONCLUSIONS: FGFR2 is a promising potential biomarker associated with NET-driven inflammation in DFU. Its downregulation in the diabetic wound microenvironment and its modulation by existing pharmacological agents suggest that targeting the FGFR2 pathway may be a viable future therapeutic strategy for improving DFU healing outcomes. Further preclinical validation is warranted.
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The R package limma (version 3.40.6) was used for differential expression analysis between DFU and control groups.
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Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/.
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Our analysis identified FGFR2 as a key downregulated gene at the intersection of DFU pathology and NETs-related pathways.
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Our analysis identified FGFR2 as a key downregulated gene at the intersection of DFU pathology and NETs-related pathways.
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Our analysis identified FGFR2 as a key downregulated gene at the intersection of DFU pathology and NETs-related pathways.
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Deregulated immune signature orchestrated by FOXM1 impairs human diabetic wound healing
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Transcriptomic characterization of the delayed wound healing response in a diabetic skin humanized mice model
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Transcriptional profiling of a wound healing process in skin and oral mucosa
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confirmed in human DFU tissues and diabetic mouse models using qPCR and immunohistochemistry.
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reduced neutrophil FGFR2 expression, and the in vivo observations in DFU. Availability of data an
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with our hypothesis, the expression of Ly6G (in mice) and CD15 (in humans) was significantly upre
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FGFR2 during wound healing using the public mouse transcriptomic dataset GSE23006 [50]. This a
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med these findings in human DFU tissues. Our qPCR analysis showed significantly lower FGFR2 mR
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3); i, j Quantification of ly6G and CD15; k Flow cytometry validation of ly6G-labeled neutrophil ratio
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40796870", "kind": "science-record", "revision": 4, "content_hash": "sha256:abd57436ebc1b2b9790bdba6c7889e67726dfcc21ebc14b371d07347aa642803", "created_at": "2026-11-01T20:30:53Z", "updated_at": "2026-11-01T22:57:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "FGFR2 identified as a NETs-associated biomarker and therapeutic target in diabetic foot ulcers.", "doi": "10.1186/s40001-025-03012-5", "pmid": "40796870", "authors": ["Linrui Dai", "Shunli Rui", "Mengling Yang", "Shiyan Yu", "Qingqing Chen", "Hongyan Wang", "Bo Deng", "Liling Deng", "Wei Hao", "Xiaohua Wu", "David G Armstrong", "Zhidong Cao", "Xiaodong Duan", "Wuquan Deng"], "journal": "European journal of medical research", "year": 2025, "abstract": "BACKGROUND: Diabetic foot ulcer (DFU) is characterized by impaired wound healing and chronic inflammation, partly driven by the excessive formation of neutrophil extracellular traps (NETs). However, the molecular mediators linking NETs to failed tissue regeneration remain poorly understood. This study aimed to identify and validate novel NETs-associated biomarkers in DFU using an integrative bioinformatics and machine learning approach.\nMETHODS: Differentially expressed genes (DEGs) were identified from the GEO dataset GSE134431. These DEGs were intersected with a NETs-related gene set to identify NETs-associated DEGs (NETDEGs). LASSO logistic regression and Random Forest algorithms were applied to the NETDEGs to select key feature genes. The top candidate, Fibroblast Growth Factor Receptor 2 (FGFR2), was validated in two independent datasets (GSE7014 and GSE147890). Its expression was further confirmed in human DFU tissues and diabetic mouse models using qPCR and immunohistochemistry. The effect of a high-glucose environment on FGFR2 expression in neutrophils was assessed in vitro. Finally, molecular docking technique was used to screen for existing drugs targeting FGFR2, with top candidates validated at the cellular level.\nRESULTS: Our analysis identified FGFR2 as a key downregulated gene at the intersection of DFU pathology and NETs-related pathways. FGFR2 expression was significantly reduced in DFU tissues across all datasets and in our experimental models, where its downregulation correlated with increased NETs accumulation. FGFR2 demonstrated strong diagnostic potential, with an AUC of 1.00 in the training set. In vitro, high glucose conditions suppressed FGFR2 expression in neutrophils. From 32 glucose-lowering drugs, Canagliflozin and Gliquidone were found to significantly upregulate FGFR2 protein expression in neutrophils, suggesting a potential modulatory effect.\nCONCLUSIONS: FGFR2 is a promising potential biomarker associated with NET-driven inflammation in DFU. Its downregulation in the diabetic wound microenvironment and its modulation by existing pharmacological agents suggest that targeting the FGFR2 pathway may be a viable future therapeutic strategy for improving DFU healing outcomes. Further preclinical validation is warranted.", "claims": [{"id": "claim-method", "statement": "The R package limma (version 3.40.6) was used for differential expression analysis between DFU and control groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40796870", "locator": "sections[4] \"Data processing and differentially expressed gene screening\" paragraph 2", "quote": "The R package limma (version 3.40.6) was used for differential expression analysis between DFU and control groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. 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This data can be found here: https://www.ncbi.nlm.nih.gov/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our analysis identified FGFR2 as a key downregulated gene at the intersection of DFU pathology and NETs-related pathways.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40796870", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Our analysis identified FGFR2 as a key downregulated gene at the intersection of DFU pathology and NETs-related pathways."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our analysis identified FGFR2 as a key downregulated gene at the intersection of DFU pathology and NETs-related pathways.", "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:40796870"], "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:GSE134431"], "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:GSE134431"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40796870", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s40001-025-03012-5", "sha256": "sha256:76c2d892db32e5268a236620f34b2eef33d04a6a79338274d192d0b376d7ab61", "bytes": 56222, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s40001-025-03012-5", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12344841", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE134431", "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=GSE134431", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE134431", "version": null, "note": "Publicly available datasets were analyzed in this study. 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