Integrated bioinformatics and machine learning reveal pan-apoptosis and immune infiltration signatures in diabetic nephropathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41479901", "kind": "science-record", "revision": 4, "content_hash": "sha256:2f337e6daddc96c00cdcb08cab57f5e85996df31e3284a36ffd75bc4c4557a61", "created_at": "2026-11-01T20:31:27Z", "updated_at": "2026-11-01T22:57:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated bioinformatics and machine learning reveal pan-apoptosis and immune infiltration signatures in diabetic nephropathy.", "doi": "10.3389/fimmu.2025.1659065", "pmid": "41479901", "authors": ["Yu Liu", "Wenqian Lu", "Zhicong Xiang", "Yuli Shen", "Baoyi Ni", "Hequn Zou", "Xiaofei Shao"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Background: Diabetic nephropathy (DN) is one of the vascular complications of diabetes and a leading cause of end-stage renal disease (ESRD) and mortality in diabetic patients. PANoptosis has been defined a unique form of programmed cell death that integrates pyroptosis, apoptosis, and necroptosis. However, the role of other biomarkers in modulating PANoptosis and their impact on DN remains unexplored.\nObjective: This study aimed to explore panoptosis-related genes and potential therapeutic drugs in DN.\nMethods: We downloaded DN datasets from the GEO database and identified differentially expressed genes (DEGs) through integrated differential expression analysis and weighted gene co-expression network analysis (WGCNA). The intersection between DN-related DEGs and panoptosis-related genes was obtained, and LASSO and SVM machine learning algorithms were applied to screen candidate biomarkers. The area under the receiver operating characteristic curve (AUC) was calculated for evaluation. Validation was performed using the merged dataset of GSE30529 and GSE4713. The CIBERSORT algorithm was used to assess immune cell infiltration, and Spearman correlation analysis was conducted to examine the association of biomarker genes. The Kidney Integrative Transcriptomics database was employed to explore the distribution of core genes across 12 cell populations. Potential drug molecules interacting with core genes were screened using the DSigDB database on the Enrichr platform, and molecular docking was performed using AutoDock Vina to evaluate binding affinity. The qRT-PCR was used to validate the expression of these hub mitochondria-related genes.\nResults: Analysis of the DN dataset yielded 17 intersecting genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these genes were significantly associated with immune and inflammatory responses, pyroptosis, extrinsic apoptosis, necroptosis, and related pathways. Using LASSO and SVM machine learning algorithms, eight candidate biomarkers were identified: CD44, CRIP1, CEBPB, TNFRSF1B, CAV1, IGF1, GZMB, and LY96. ROC curve analysis demonstrated that these biomarkers had strong diagnostic value for DN patients. Further investigation into immune infiltration in DN samples using CIBERSORT showed that core genes were closely related to dendritic cells (resting), macrophages (M1), mast cells (activated), neutrophils, T cells (CD4 memory activated, CD4 memory resting, CD8, and gamma delta). Drug screening via DSigDB on Enrichr identified imatinib as a significantly enriched drug interacting with core genes, and molecular docking confirmed its strong binding affinity.\nConclusion: Through comprehensive bioinformatics approaches, this study identified CD44, CRIP1, CEBPB, TNFRSF1B, CAV1, IGF1, GZMB and LY96 as potential diagnostic biomarkers for DN, providing new insights into disease diagnosis.", "claims": [{"id": "claim-method", "statement": "To identify PANoptosis-related core genes in diabetic nephropathy, we employed two machine learning algorithms: least absolute shrinkage and selection operator (LASSO) via the “glmnet” package and support vector machine (SVM) via the “caret” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41479901", "locator": "sections[9] \"Machine learning-based identification of core genes\" paragraph 2", "quote": "To identify PANoptosis-related core genes in diabetic nephropathy, we employed two machine learning algorithms: least absolute shrinkage and selection operator (LASSO) via the “glmnet” package and support vector machine (SVM) via the “caret” package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41479901", "locator": "sections[44] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using LASSO and SVM machine learning algorithms, eight candidate biomarkers were identified: CD44, CRIP1, CEBPB, TNFRSF1B, CAV1, IGF1, GZMB, and LY96.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41479901", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Using LASSO and SVM machine learning algorithms, eight candidate biomarkers were identified: CD44, CRIP1, CEBPB, TNFRSF1B, CAV1, IGF1, GZMB, and LY96."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using LASSO and SVM machine learning algorithms, eight candidate biomarkers were identified: CD44, CRIP1, CEBPB, TNFRSF1B, CAV1, IGF1, GZMB, and LY96.", "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:41479901"], "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:GSE104948"], "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:GSE104948"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41479901", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1659065", "sha256": "sha256:c0f3039c71a54e1a6ebffe8a0753d202cd7e51020a8406aa94ed8cdabd7a0aa7", "bytes": 54022, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1659065", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12753429", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE104948", "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=GSE104948", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE104948", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary Material. 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