Identification and validation of diagnostic genes associated with neutrophil extracellular traps of type 2 diabetes mellitus.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39296548", "kind": "science-record", "revision": 3, "content_hash": "sha256:c14f8bd9e797e31fe682235f0c0caa646e0eec3ed6bbc850e85bcfac8d8c5054", "created_at": "2026-11-01T20:29:28Z", "updated_at": "2026-11-01T22:12:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of diagnostic genes associated with neutrophil extracellular traps of type 2 diabetes mellitus.", "doi": "10.3389/fgene.2024.1373807", "pmid": "39296548", "authors": ["Meifang He", "Jin Niu", "Haihua Cheng", "Chaoying Guo"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Background: Neutrophil extracellular traps (NETs) cause delayed wound closed up in type 2 diabetes mellitus (T2DM), but the specific regulatory mechanism of NETs-related genes (NETs-RGs) in T2DM is unclear.\nMethods: We acquired GSE21321 and GSE15932 datasets from gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between T2DM and control samples of GSE21321 dataset were sifted out by differential expression analysis. NETs scores were calculated for all samples in GSE21321 dataset, and key module genes associated with NETs scores were screened by constructing co-expression network. Then, DEGs and key module genes were intersected to yield intersection genes, and candidate genes were identified by constructing a protein protein interaction (PPI) network. Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA). Finally, immune characteristic analysis was carried out, and we constructed the gene-drug and transcription factor (TF)-miRNA-mRNA networks. Besides, we validated the expression of diagnostic genes by quantitative real-time polymerase chain reaction (qRT-PCR).\nResults: In total, 23 candidate genes were gained by PPI analysis. The 5 diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and calcium voltage-gated channel subunit alpha1 C (CACNA1C), were identified via LASSO analysis, and they were involved in carboxylic acid transport, axonogenesis, etc. M2 Macrophage, Monocyte, Natural killer (NK) cell, and Myeloid dendritic cells (DC) were remarkably different between T2DM and control samples. Diagnostic genes had the strongest and the most significant positive correlation with B cells. The gene-drug network included CACNA1C-Isradipine, CACNA1C-Benidipine and other relationship pairs. Totally 76 nodes and 44 edges constituted the TF-miRNA-mRNA network, including signal transducer and activator of transcription 1(STAT1) -hsa-miR-3170-AGER, CCCTC-binding factor (CTCF)-hsa-miR-455-5p-CACNA1C, etc. Moreover, qRT-PCR suggested that the expression trends of FGF1 and AGER were in keeping with the results of bioinformatic analysis. FGF1 and AGER were markedly regulated downwards in the T2DM group.\nConclusion: Through bioinformatic analysis, we identified NETs-related diagnostic genes (ITIH3, FGF1, NRCAM, AGER, CACNA1C) in T2DM, and explored their mechanism of action from different aspects, providing new ideas for the studies related to diagnosis and treatment of T2DM.", "claims": [{"id": "claim-method", "statement": "Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39296548", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39296548", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The five diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and CACNA1C, were identified when the lambda min = 0.0106 via LASSO analysis (Figure 3A), and they were all downregulated in T2DM samples of the GSE21321 dataset (Supplementary Figure S2).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39296548", "locator": "sections[14] \"3.3 Excellent diagnostic power of diagnostic genes for T2DM\" paragraph 2", "quote": "The five diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and CACNA1C, were identified when the lambda min = 0.0106 via LASSO analysis (Figure 3A), and they were all downregulated in T2DM samples of the GSE21321 dataset (Supplementary Figure S2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The five diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and CACNA1C, were identified when the lambda min = 0.0106 via LASSO analysis (Figure 3A), and they were all downregulated in T2DM samples of the GSE21321 dataset (Supplementary Figure S2).", "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:39296548"], "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:GSE15932"], "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:GSE15932"], "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:39296548", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2024.1373807", "sha256": "sha256:0e26fb771229ddb4996e2b0c2d3846b1e9f7d03f3f57e6bc1ea21ec6043046c6", "bytes": 37517, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2024.1373807", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11408200", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15932", "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=GSE15932", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15932", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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