Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40312440", "kind": "science-record", "revision": 4, "content_hash": "sha256:69d6f0040b26ae5ad7be7b89b8804f03f31379246c25576f79db1f51359f6cd0", "created_at": "2026-11-01T20:30:27Z", "updated_at": "2026-11-01T22:56:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.", "doi": "10.1038/s41598-025-99395-w", "pmid": "40312440", "authors": ["Jing Zhang", "Lulu Cheng", "Shan Jiang", "Duosheng Zhu"], "journal": "Scientific reports", "year": 2025, "abstract": "Anoikis and immune cell infiltration are pivotal factors in the pathophysiological mechanism of diabetic nephropathy (DN), yet a comprehensive understanding of the mechanism is lacking. This work aimed to pinpoint distinctive anoikis-related genes (ARGs) in DN and delve into their impact on the immune landscape. Three datasets (GSE30528, GSE47184, and GSE96804) were downloaded from the gene expression omnibus (GEO) dataset. Differentially expressed genes (DEGs) were identified using the \"limma\" package, while ARGs were obtained from GSEA, GeneCard, and Harmonizome datasets. The intersection of DEGs and ARGs was analyzed for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The CIBERSORT algorithm was employed to estimate the infiltration percentage of 22 immune cell types in DN renal tissue. Subsequently, the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms were adopted to screen key ARGs related to DN. After that, receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of each gene and the real-time quantitative polymerase chain reaction (RT-qPCR) was adopted to quantitatively detect the expression of biomarkers in DN cell models. Finally, correlations between key genes and immune cell infiltration were analyzed, and a competitive endogenous ribonucleic acid (RNA) (ceRNA) network based on key genes was constructed. A total of 59 DEARGs were identified. GO functional annotation enrichment analysis revealed their involvement in kidney development, extracellular matrix (ECM), cytoplasmic vesicle cavity, immunoinflammatory response, and cytokine effect. KEGG pathway analysis indicated that MAPK, PI3K -Akt, IL -17, TNF, and HIF- 1 signaling pathways are critical for DN. In addition, seven key genes, including PDK4, S100A8, HTRA1, CHI3L1, WT1, CDKN1B, and EGF, were screened by machine learning algorithm. Most of these genes exhibited low expression in renal tissue of DN patients and positive correlation with neutrophils, and their expressions were verified in an external dataset cell model. The ceRNA analysis suggested potential regulatory pathways (H19/miR-15b-5p/PDK4 and KCNQ1T1/miR-1207-3p/WT1) influencing early DN progression. This work provided a comprehensive analysis of the role of DEARGs in DN for the first time, offering valuable insights for further understanding the disease mechanism and guiding clinical diagnosis, treatment, and research of DN.", "claims": [{"id": "claim-method", "statement": "Differential gene expression analysis was performed using the limma package in R, with the screening criteria set as p.adjust < 0.05 and |log2 fold change (log2FC)| ≥ 115.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40312440", "locator": "sections[4] \"Identification of differentially expressed ARGs between DN and normal samples\" paragraph 2", "quote": "Differential gene expression analysis was performed using the limma package in R, with the screening criteria set as p.adjust < 0.05 and |log2 fold change (log2FC)| ≥ 115."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40312440", "locator": "sections[24] \"Data availability\" paragraph 2", "quote": "The datasets used and/or analysed during the current study available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 59 DEARGs were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40312440", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A total of 59 DEARGs were identified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 59 DEARGs were identified.", "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:40312440"], "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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