Identifying and Validating GSTM5 as an Immunogenic Gene in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38145013", "kind": "science-record", "revision": 4, "content_hash": "sha256:ea7421d7eb1ee30d7f7f462a297e3541f68fcc5936c940b34912958c8f93b310", "created_at": "2026-11-01T19:20:05Z", "updated_at": "2026-11-01T22:53:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying and Validating GSTM5 as an Immunogenic Gene in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning.", "doi": "10.2147/JIR.S442388", "pmid": "38145013", "authors": ["Hongshuo Shi", "Xin Yuan", "Guobin Liu", "Weijing Fan"], "journal": "Journal of inflammation research", "year": 2023, "abstract": "Background: A diabetic foot ulcer (DFU) is a serious, long-term condition associated with a significant risk of disability and mortality. However, research on its biomarkers is still limited. This study utilizes bioinformatics and machine learning methods to identify immune-related biomarkers for DFU and validates them through external datasets and animal experiments.\nMethods: This study used bioinformatics and machine learning to analyze microarray data from the Gene Expression Omnibus (GEO) database to identify key genes associated with DFU. Animal experiments were conducted to validate these findings. This research employs the datasets GSE68183 and GSE80178 retrieved from the GEO database as the training dataset for building a gene machine learning model, and after conducting differential analysis on the data, this study used package glmnet and package e1071 to construct LASSO and SVM-RFE machine learning models, respectively. Subsequently, we validated the model using the training set and validation set (GSE134431). We conducted enrichment analysis, including GSEA and GSVA, on the model genes. We also performed immune functional analysis and immune-related analysis on the model genes. Finally, we conducted immunohistochemistry (IHC) validation on the model genes.\nResults: This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods. Subsequent validation through external datasets and IHC experiments also confirms GSTM5 as a critical biomarker for DFU. The gene may be associated with T cells regulatory (Tregs) and T cells follicular helper, and it influences the NF-κB, GnRH, and MAPK signaling pathway.\nConclusion: This study identified and validated GSTM5 as a biomarker for DFU. This finding may potentially provide a target for immune therapy for DFU.", "claims": [{"id": "claim-method", "statement": "The LASSO method is used to identify essential features from large-scale data and screen gene models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38145013", "locator": "sections[5] \"Machine Learning\" paragraph 2", "quote": "The LASSO method is used to identify essential features from large-scale data and screen gene models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All generated data have been included in the published manuscript. The datasets of gene expression generated during the current study are available in the NCBI Gene Expression Omnibus (GEO) database (accession number: GSE68183, GSE80178, and GSE134431). For all other information, contact with the author (Hongshuo Shi, jf17510413109@163.com).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38145013", "locator": "sections[24] \"Data Sharing Statement\" paragraph 2", "quote": "All generated data have been included in the published manuscript. The datasets of gene expression generated during the current study are available in the NCBI Gene Expression Omnibus (GEO) database (accession number: GSE68183, GSE80178, and GSE134431). For all other information, contact with the author (Hongshuo Shi, jf17510413109@163.com)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38145013", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods.", "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:38145013"], "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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The datasets of gene expression generated during the current study are available in the NCBI Gene Expression Omnibus (GEO) database (accession number: GSE68183, GSE80178, and GSE134431). For all other information, contact with the author (Hongshuo Shi, jf17510413109@163.com).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All generated data have been included in the published manuscript. The datasets of gene expression generated during the current study are available in the NCBI Gene Expression Omnibus (GEO) database (accession number: GSE68183, GSE80178, and GSE134431). 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Finally, we conducted immunohistochemistry (IHC) validation on the model genes. Results", "Finally, we conducted immunohistochemistry (IHC) validation on the model genes. Results: Thi", "ent validation through external datasets and IHC experiments also confirms GSTM5 as a critica"], "order_basis": "paper_text_order:1307", "id": "assay:2"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The LASSO method is used to identify essential features from large-scale data and screen gene models."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}