Development and Validation of a Prognostic Signature Based on the Lysine Crotonylation Regulators in Head and Neck Squamous Cell Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36814797", "kind": "science-record", "revision": 3, "content_hash": "sha256:6c9f676291962c670bbd6942f778f40d76bd395936f2f4ac1a1c8b25f73185da", "created_at": "2026-11-01T19:01:24Z", "updated_at": "2026-11-01T22:08:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and Validation of a Prognostic Signature Based on the Lysine Crotonylation Regulators in Head and Neck Squamous Cell Carcinoma.", "doi": "10.1155/2023/4444869", "pmid": "36814797", "authors": ["Linlin Jiang", "Xiteng Yin", "Hongbo Zhang", "Xinyu Zhang", "Zichen Cao", "Meng Zhou", "Wenguang Xu"], "journal": "BioMed research international", "year": 2023, "abstract": "Background: Lysine crotonylation (Kcr) is a newly identified posttranslational modification type regulated by various enzymes and coenzymes, including lysine crotonyltransferase, lysine decrotonylase, and binding proteins. However, the role of Kcr regulators in head and neck squamous cell carcinoma (HNSCC) remains unknown. The aim of this study was to establish and validate a Kcr-related prognostic signature of HNSCC and to assess the clinical predictive value of this signature.\nMethods: The mRNA expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA) database were downloaded to explore the clinical significance and prognostic value of these regulators in HNSCC. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate the Kcr-related prognostic signature for HNSCC. Subsequently, the GSE65858 dataset from the Gene Expression Omnibus (GEO) database was used to validate the signature. The prognostic value of the signature was evaluated using the Kaplan-Meier survival, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox regression analyses.\nResults: We established a nine-gene risk signature associated with the prognosis of HNSCC based on Kcr regulators. High-risk patients demonstrated significantly poorer overall survival (OS) than low-risk patients in the training (TCGA) and validation (GEO) datasets. Then, the time-dependent receiver operating characteristic (ROC) curve analysis showed that the nine-gene risk signature was more accurate for predicting the 5-year OS than other clinical parameters, including age, gender, T stage, N stage, and histologic grade in the TCGA and GEO datasets. Moreover, the Cox regression analysis showed that the constructed risk signature was an independent risk factor for HNSCC.\nConclusion: Our study identified and validated a nine-gene signature for HNSCC based on Kcr regulators. These results might contribute to prognosis stratification and treatment escalation for HNSCC patients.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate the Kcr-related prognostic signature for HNSCC.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36814797", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate the Kcr-related prognostic signature for HNSCC."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available in TCGA database at https://www.cancer.gov/ and GEO database at https://www.ncbi.nlm.nih.gov/geo/ (reference number: GSE65858).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36814797", "locator": "sections[20] \"Data Availability\" paragraph 2", "quote": "The data that support the findings of this study are available in TCGA database at https://www.cancer.gov/ and GEO database at https://www.ncbi.nlm.nih.gov/geo/ (reference number: GSE65858)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We established a nine-gene risk signature associated with the prognosis of HNSCC based on Kcr regulators.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36814797", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We established a nine-gene risk signature associated with the prognosis of HNSCC based on Kcr regulators."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We established a nine-gene risk signature associated with the prognosis of HNSCC based on Kcr regulators.", "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:36814797"], "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:GSE65858"], "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:GSE65858"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36814797", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2023/4444869", "sha256": 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