Construction of a Novel Necroptosis-Related Signature in Rat DRG for Neuropathic Pain.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39802520", "kind": "science-record", "revision": 4, "content_hash": "sha256:f19635a175b46ed0365290f6e1e05ccac70eafb522775d29fe87d385f754ec3d", "created_at": "2026-11-01T20:30:06Z", "updated_at": "2026-11-01T22:55:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a Novel Necroptosis-Related Signature in Rat DRG for Neuropathic Pain.", "doi": "10.2147/JIR.S494286", "pmid": "39802520", "authors": ["Yang Liu", "Shikang Hao", "Hongyu Hao", "Guona Zheng", "Jie Bing", "Lin Kang", "Jia Li", "Huanfen Zhao", "Han Hao"], "journal": "Journal of inflammation research", "year": 2025, "abstract": "Background: Recent studies have shown necroptosis may play a role in the development of inflammation-associated pain. However, research on the correlation between necroptosis-related genes and neuropathic pain in the dorsal root ganglia (DRG) is limited. This study aims to identify a gene signature related to necroptosis in DRG that can predict neuropathic pain.\nMethods: The mRNA expression profiles associated with neuropathic pain (GSE24982 and GSE30691) were acquired from the Gene Expression Omnibus (GEO) database. The Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) regressions were performed in GSE24982 database to constructed the necroptosis-related diferentially expressed genes (NRDEGs) signature related to neuropathic pain. Nomogram, Receiver Operating Characteristic (ROC), GSE30691 database analysis and basic experiments were used to verify the accuracy of the signature. Go and KEGG analysis, interaction network and immune infiltration were used to analyze the biological function of the signature.\nResults: A predictive signature targeting rat DRG for neuropathic pain through a variety of methods to verify the accuracy was developed based on 3 NRDEGs (TLR4, CAPN2, RIPK3). Significantly enriched KEGG and GO pathways, drug target prediction and non-coding RNAs related to the signature holded promise for advancing our understanding of potential avenues for treatment and the mechanisms underlying neuropathic pain. Immune infiltration analysis revealed which types of immune cells related to the NRDEGs signature played an important role in the occurrence and development of neuropathic pain. Basic experiments provided crucial evidence that the 3 NRDEGs in DRG served as important regulators of neuropathic pain.\nConclusion: The prediction signature based on 3 key NRDEGs showed promise in predicting the presence of neuropathic pain, which may open up new avenues for the development of novel therapies for neuropathic pain.", "claims": [{"id": "claim-method", "statement": "The feature selection process for identifying NRDEGs related to neuropathic pain involved the utilization of two machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (Lasso) regression, which was performed with 10-fold cross-validation, a significance level of 0.05, and 1,000 cycles using the “glmnet” R package. Additionally, the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique was implemented with the “e1071” R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39802520", "locator": "sections[6] \"Identification of Feature Gene Signature\" paragraph 2", "quote": "The feature selection process for identifying NRDEGs related to neuropathic pain involved the utilization of two machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (Lasso) regression, which was performed with 10-fold cross-validation, a significance level of 0.05, and 1,000 cycles using the “glmnet” R package. Additionally, the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique was implemented with the “e1071” R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39802520", "locator": "sections[27] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets used and analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A predictive signature based on three necroptosis-related differentially expressed genes—TLR4, CAPN2, and RIPK3—showed promise in predicting the presence of neuropathic pain.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39802520", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A predictive signature targeting rat DRG for neuropathic pain through a variety of methods to verify the accuracy was developed based on 3 NRDEGs (TLR4, CAPN2, RIPK3)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A predictive signature based on three necroptosis-related differentially expressed genes—TLR4, CAPN2, and RIPK3—showed promise in predicting the presence of neuropathic pain.", "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:39802520"], "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:GSE24982"], "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:GSE24982"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39802520", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S494286", "sha256": "sha256:ee5eb3e90f792b765ece3a9f6292702aea4e4a2386d922e3bb6e194d6c526810", "bytes": 37767, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/JIR.S494286", "version": null, "note": "Full article fetched from PMC; 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Results: A predictive signature targeting rat DRG for neuropathic pain through a variety o"], "order_basis": "paper_text_order:13", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The feature selection process for identifying NRDEGs related to neuropathic pain involved the utilization of two machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (Lasso) regression, which was performed with 10-fold cross-validation, a significance level of 0.05, and 1,000 cycles using the “glmnet” R package. 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