Identification and clinical validation of the role of anoikis-related genes in diabetic foot.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38468369", "kind": "science-record", "revision": 4, "content_hash": "sha256:e650bdde23a3f21030a1094f00d864890abfce2de90c50e322e94053cfa2c5cc", "created_at": "2026-11-01T19:20:25Z", "updated_at": "2026-11-01T22:53:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and clinical validation of the role of anoikis-related genes in diabetic foot.", "doi": "10.1111/iwj.14771", "pmid": "38468369", "authors": ["Nan Su", "Jiwei Wang", "Hengrui Zhang", "Haoyong Jin", "Baojian Miao", "Jiangli Zhao", "Xuchen Liu", "Chao Li", "Xinyu Wang", "Ning Yang"], "journal": "International wound journal", "year": 2024, "abstract": "This study aims to investigate the role of anoikis-related genes in diabetic foot (DF) by utilizing bioinformatics analysis to identify key genes associated with anoikis in DF. We selected the GEO datasets GSE7014, GSE80178 and GSE68183 for the extraction and analysis of differentially expressed anoikis-related genes (DE-ARGs). GO analysis and KEGG analysis indicated that DE-ARGs in DF were primarily enriched in apoptosis, positive regulation of MAPK cascade, anoikis, focal adhesion and the PI3K-Akt signalling pathway. Based on the LASSO and SVM-RFE algorithms, we identified six characteristic genes. ROC curve analysis revealed that these six characteristic genes had an area under the curve (AUC) greater than 0.7, indicating good diagnostic efficacy. Expression analysis in the validation set revealed downregulation of CALR in DF, consistent with the training set results. GSEA results demonstrated that CALR was mainly enriched in blood vessel morphogenesis, endothelial cell migration, ECM-receptor interaction and focal adhesion. The HPA database revealed that CALR was moderately enriched in endothelial cells, and CALR was found to interact with 63 protein-coding genes. Functional analysis with DAVID suggested that CALR and associated genes were enriched in the phagosome component. CALR shows promise as a potential marker for the development and treatment of DF.", "claims": [{"id": "claim-method", "statement": "The analysis of differences in gene expression data was assessed using Limma, 16 which employs linear models and Bayesian methods. In this study, we employed Lasso logistic regression and the SVM‐RFE algorithm to perform feature selection for the identification of biomarkers associated with diabetic foot.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38468369", "locator": "sections[4] \"Anoikis‐related DEG screening\" paragraph 2", "quote": "The analysis of differences in gene expression data was assessed using Limma, 16 which employs linear models and Bayesian methods."}, {"asset": "source:pmid:38468369", "locator": "sections[6] \"Machine learning for biomarker identification\" paragraph 2", "quote": "In this study, we employed Lasso logistic regression and the SVM‐RFE algorithm to perform feature selection for the identification of biomarkers associated with diabetic foot."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38468369", "locator": "sections[21] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The data that support the findings of this study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO logistic regression and SVM-RFE identified six diagnostic biomarker genes, whose combined logistic-regression model achieved an AUC of 1.000 in the training dataset, with CALR downregulation validated in the external dataset.", "kind": "quantitative", "target": {"value": 0.019, "unit": "p-value", "tolerance": null, "source": "The results showed no difference in the expression of the SIK2, CD36, TSC2, NDRG1 and S100A7 genes (Figure 4B–F), while the expression of CALR (p = 0.019) in the DF group was lower than that in the control group (Figure 4A)."}, "evidence": [{"asset": "source:pmid:38468369", "locator": "sections[14] \"Identification of DF‐related characteristic DE‐ARGs\" paragraph 2", "quote": "Finally, by comparing the results from the two machine learning algorithms, the following six key diagnostic biomarker genes were identified: CALR, SIK2, CD36, TSC2, NDRG1 and S100A7 (Figure 3E)."}, {"asset": "source:pmid:38468369", "locator": "sections[14] \"Identification of DF‐related characteristic DE‐ARGs\" paragraph 4", "quote": "After establishing a logistic regression model using the R ‘glm’ package, the AUC value for the combination of the six hub DE‐FRGs in the training dataset was 1.000 (Figure 3G)."}, {"asset": "source:pmid:38468369", "locator": "sections[14] \"Identification of DF‐related characteristic DE‐ARGs\" paragraph 5", "quote": "The results showed no difference in the expression of the SIK2, CD36, TSC2, NDRG1 and S100A7 genes (Figure 4B–F), while the expression of CALR (p = 0.019) in the DF group was lower than that in the control group (Figure 4A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LASSO logistic regression and SVM-RFE identified six diagnostic biomarker genes, whose combined logistic-regression model achieved an AUC of 1.000 in the training dataset, with CALR downregulation validated in the external dataset.", "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:38468369"], "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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