Identification and validation of aging-related genes in neuropathic pain using bioinformatics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39113685", "kind": "science-record", "revision": 4, "content_hash": "sha256:3e22c38e5c7cfa126e8d2bc05cfb5e8c55f2839d4cd13458587a56e706dcc203", "created_at": "2026-11-01T20:29:12Z", "updated_at": "2026-11-01T22:54:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of aging-related genes in neuropathic pain using bioinformatics.", "doi": "10.3389/fgene.2024.1430275", "pmid": "39113685", "authors": ["Hui Gao", "Guoqi Dong", "Yong Yao", "Huayuan Yang"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Background: Neuropathic pain (NP) is a debilitating and refractory chronic pain with a higher prevalence especially in elderly patients. Cell senescence considered a key pathogenic factor in NP. The objective of this research is to discover genes associated with aging in peripheral blood of individuals with NP using bioinformatics techniques.\nMethods: Two cohorts (GSE124272 and GSE150408) containing peripheral blood samples of NP were downloaded from the GEO database. By merging the two cohorts, differentially expressed aging-related genes (DE-ARGs) were obtained by intersection with aging-related genes. The potential biological mechanisms of DE-ARGs were further analyzed through GO and KEGG. Three machine learning methods, namely, LASSO, SVM-RFE, and Random Forest, were utilized to identify diagnostic biomarkers. A Nomogram model was developed to assess their diagnostic accuracy. The validation of biomarker expression and diagnostic effectiveness was conducted in three distinct pain cohorts. The CIBERSORT algorithm was employed to evaluate the immune cell composition in the peripheral blood of patients with NP and investigate its association with the expression of diagnostic biomarkers.\nResults: This study identified a total of 24 DE-ARGs, mainly enriched in \"Chemokine signaling pathway,\" \"Inflammatory mediator regulation of TRP channels,\" \"HIF-1 signaling pathway\" and \"FOXO signaling pathway\". Three machine learning algorithms identified a total of four diagnostic biomarkers (CEBPA, CEACAM1, BTG3 and IL-1R1) with good diagnostic performance and the similar expression difference trend in different types of pain cohorts. The expression levels of CEACAM1 and IL-1R1 exhibit a positive correlation with the percentage of neutrophils.\nConclusion: Using machine learning techniques, our research identified four diagnostic biomarkers related to aging in peripheral blood, providing innovative approaches for the diagnosis and treatment of NP.", "claims": [{"id": "claim-method", "statement": "ROC curves were generated using the “pROC” package (version 1.18.0) to assess the diagnostic value of biomarkers for NP, with AUC and 95% confidence intervals calculated.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39113685", "locator": "sections[7] \"2.5 Establishment and evaluation of nomogram\" paragraph 2", "quote": "ROC curves were generated using the “pROC” package (version 1.18.0) to assess the diagnostic value of biomarkers for NP, with AUC and 95% confidence intervals calculated."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39113685", "locator": "sections[19] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Additionally, the nomogram demonstrates a high diagnostic potential for NP with an AUC of 0.902 (95% CI: 0.810–0.971) (Figure 6D).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Additionally, the nomogram demonstrates a high diagnostic potential for NP with an AUC of 0.902 (95% CI: 0.810–0.971) (Figure 6D)."}, "evidence": [{"asset": "source:pmid:39113685", "locator": "sections[13] \"3.4 The diagnostic efficacy of the biomarkers\" paragraph 2", "quote": "Additionally, the nomogram demonstrates a high diagnostic potential for NP with an AUC of 0.902 (95% CI: 0.810–0.971) (Figure 6D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Additionally, the nomogram demonstrates a high diagnostic potential for NP with an AUC of 0.902 (95% CI: 0.810–0.971) (Figure 6D).", "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:39113685"], "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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