Identification of an individualized therapy prognostic signature for head and neck squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37106442", "kind": "science-record", "revision": 3, "content_hash": "sha256:fe3e150403e484d79b047c4792d8da34bec32c02c80c839222f4b0146bbe237d", "created_at": "2026-11-01T19:01:46Z", "updated_at": "2026-11-01T22:09:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of an individualized therapy prognostic signature for head and neck squamous cell carcinoma.", "doi": "10.1186/s12864-023-09325-1", "pmid": "37106442", "authors": ["Cheng Lin", "Yuebing Chen", "Jianji Pan", "Qiongjiao Lu", "Pengjie Ji", "Shuiqin Lin", "Chunfeng Liu", "Shaojun Lin", "Meifang Li", "Jingfeng Zong"], "journal": "BMC genomics", "year": 2023, "abstract": "BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) are the most common cancers in the head and neck. Therapeutic response-related genes (TRRGs) are closely associated with carcinogenesis and prognosis in HNSCC. However, the clinical value and prognostic significance of TRRGs are still unclear. We aimed to construct a prognostic risk model to predict therapy response and prognosis in TRRGs-defined subgroups of HNSCC.\nMETHODS: The multiomics data and clinical information of HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA). The profile data GSE65858 and GSE67614 chip was downloaded from public functional genomics data Gene Expression Omnibus (GEO). Based on TCGA-HNSC database, patients were divided into a remission group and a non-remission group according to therapy response, and differentially expressed TRRGs between those two groups were screened. Using Cox regression analysis and Least absolute shrinkage and selection operator (LASSO) analysis, candidate TRRGs that can predict the prognosis of HNSCC were identified and used to construct a TRRGs-based signature and a prognostic nomogram.\nRESULT: A total of 1896 differentially expressed TRRGs were screened, including 1530 upregulated genes and 366 downregulated genes. Then, 206 differently expressed TRRGs that was significantly associated with the survival were chosen using univariate Cox regression analysis. Finally, a total of 20 candidate TRRGs genes were identified by LASSO analysis to establish a signature for risk prediction, and the risk score of each patient was calculated. Patients were divided into a high-risk group (Risk-H) and a low-risk group (Risk-L) based on the risk score. Results showed that the Risk-L patients had better overall survival (OS) than Risk-H patients. Receiver operating characteristic (ROC) curve analysis revealed great predictive performance for 1-, 3-, and 5-year OS in TCGA-HNSC and GEO databases. Moreover, for patients treated with post-operative radiotherapy, Risk-L patients had longer OS and lower recurrence than Risk-H patients. The nomogram involves risk score and other clinical factors had good performance in predicting survival probability.\nCONCLUSIONS: The proposed risk prognostic signature and Nomogram based on TRRGs are novel promising tools for predicting therapy response and overall survival in HNSCC patients.", "claims": [{"id": "claim-method", "statement": "First, univariate Cox regression analysis was used to screen 206 TRRGs that were significantly related to overall survival (OS). Log rank p < 0.05 was set as the threshold. Then, 20 TRRGs were obtained using Least absolute shrinkage and selection operator (LASSO) regression analysis, which was useful to establish the most feasible and predictable TRRGs signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37106442", "locator": "sections[7] \"Construction of the TRRGs signature\" paragraph 2", "quote": "First, univariate Cox regression analysis was used to screen 206 TRRGs that were significantly related to overall survival (OS). Log rank p < 0.05 was set as the threshold. Then, 20 TRRGs were obtained using Least absolute shrinkage and selection operator (LASSO) regression analysis, which was useful to establish the most feasible and predictable TRRGs signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated or analyzed during this study are freely available in the Cancer Genome Atlas (https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.HNSC.sampleMap%2FH iSeqV2_exon.gz). The GSE65858 database was downed from the website as follows, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858. The GSE67614 dataset was downloaded from website as follows, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE67614.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37106442", "locator": "sections[27] \"Availability of data and materials\" paragraph 2", "quote": "The datasets generated or analyzed during this study are freely available in the Cancer Genome Atlas (https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.HNSC.sampleMap%2FH iSeqV2_exon.gz). The GSE65858 database was downed from the website as follows, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858. The GSE67614 dataset was downloaded from website as follows, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE67614."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Receiver operating characteristic (ROC) curve analysis revealed great predictive performance for 1-, 3-, and 5-year OS in TCGA-HNSC and GEO databases.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37106442", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Receiver operating characteristic (ROC) curve analysis revealed great predictive performance for 1-, 3-, and 5-year OS in TCGA-HNSC and GEO databases."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Receiver operating characteristic (ROC) curve analysis revealed great predictive performance for 1-, 3-, and 5-year OS in TCGA-HNSC and GEO databases.", "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:37106442"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37106442", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12864-023-09325-1", "sha256": "sha256:9d40c69e2df300b66529d7e7e79433fa326806983ac895eefab56db4b616c885", "bytes": 49560, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12864-023-09325-1", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10142243", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "First, univariate Cox regression analysis was used to screen 206 TRRGs that were significantly related to overall survival (OS). Log rank p < 0.05 was set as the threshold. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The datasets generated or analyzed during this study are freely available in the Cancer Genome Atlas (https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.HNSC.sampleMap%2FH iSeqV2_exon.gz). The GSE65858 database was downed from the website as follows, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858. 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