Construction of a 10-gene prognostic score model of predicting recurrence for laryngeal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36376905", "kind": "science-record", "revision": 3, "content_hash": "sha256:fc58d9ed684813a8d40a19c83215b2ba1ae25e0e983d6d1f57017114b3b0469c", "created_at": "2026-11-01T18:15:49Z", "updated_at": "2026-11-01T21:29:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a 10-gene prognostic score model of predicting recurrence for laryngeal cancer.", "doi": "10.1186/s40001-022-00829-2", "pmid": "36376905", "authors": ["Yanan Liu", "Zhiguang Gao", "Cheng Peng", "Xingli Jiang"], "journal": "European journal of medical research", "year": 2022, "abstract": "We constructed a prognostic score (PS) model to predict the recurrence risk in patients previously diagnosed with laryngeal cancer (LC). Here the training dataset, consisting of 82 LC samples, was downloaded from The Cancer Genome Atlas (TCGA). The PS model then divided the LC samples into high- and low-risk groups, which predicted well the survival time of LC in three datasets (TCGA dataset: AUC = 0.899; GSE27020: AUC = 0.719; and GSE25727: AUC = 0.662). Therefore, the PS model based on the 10 genes and its nomogram is proposed to help predict the recurrence risk in patients with LC.", "claims": [{"id": "claim-method", "statement": "Furthermore, the Cox proportional hazard model [11] based on the L1-penalized (Lasso) in the penalized package (version 0.950; http://bioconductor.org/packages/penalized/) [12] of the R3.4.1 language was used to screen out the optimized prognostic-associated signature DEG combinations based on the aforementioned DEGs related to the prognosis [13].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36376905", "locator": "sections[5] \"Establishment and verification of a risk assessment model\" paragraph 3", "quote": "Furthermore, the Cox proportional hazard model [11] based on the L1-penalized (Lasso) in the penalized package (version 0.950; http://bioconductor.org/packages/penalized/) [12] of the R3.4.1 language was used to screen out the optimized prognostic-associated signature DEG combinations based on the aforementioned DEGs related to the prognosis [13]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data were collected and analyzed by the authors, and are not ready to share their data because the data have not been published.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36376905", "locator": "sections[20] \"Availability of data and materials\" paragraph 2", "quote": "The raw data were collected and analyzed by the authors, and are not ready to share their data because the data have not been published."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic score model accurately predicted patient survival time in the TCGA training dataset and both validation datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36376905", "locator": "sections[10] \"Evaluation and comparison of the prognostic risk prediction model’s effectiveness\" paragraph 4", "quote": "Furthermore, the ROC curves based on the PS prediction model indicated that this PS model accurately predicted the patient survival time in both TCGA dataset (AUC = 0.899) and validation dataset (GSE27020: AUC = 0.719; GSE25727: AUC = 0.662)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The prognostic score model accurately predicted patient survival time in the TCGA training dataset and both validation datasets.", "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:36376905"], "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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