A comprehensive prognostic score for head and neck squamous cancer driver genes and phenotype traits.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37897503", "kind": "science-record", "revision": 2, "content_hash": "sha256:39fd04e88a6bcbac8639a337e65dd57b909ee38eeec151d92932085e534c928c", "created_at": "2026-11-01T19:02:46Z", "updated_at": "2026-11-01T21:31:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A comprehensive prognostic score for head and neck squamous cancer driver genes and phenotype traits.", "doi": "10.1007/s12672-023-00796-y", "pmid": "37897503", "authors": ["Wen Zeng", "Fangfang Xie", "Yiyun Pan", "Zhengcong Chen", "Hailong Chen", "Xiaomei Liu", "Keqiang Tian", "Dechang Xu"], "journal": "Discover oncology", "year": 2023, "abstract": "BACKGROUND: Head and neck squamous cancer (HNSCC) presents variable phenotype and progression features. Clinically applicable, high-accuracy multifactorial prognostic models for HNSCC survival outcomes are warranted and an active area of research. This study aimed to construct a comprehensive prognostic tool for HNSCC overall survival by integrating cancer driver genes with tumor clinical and phenotype information.\nMETHODS: Key overall survival-related cancer driver genes were screened from among main effector and reciprocal gene pairs using TCGA data using univariate Cox proportional hazard regression analysis. Independent validation was performed using the GSE41613 dataset. The main effector genes among these were selected using LASSO regression and transcriptome score modeling was performed using multivariate Cox regression followed by validation analysis of the prognostic score. Next, multivariate Cox regression analysis was performed using the transcriptome score combined with age, grade, gender, and stage. An 'Accurate Prediction Model of HNSCC Overall Survival Score' (APMHO) was computed and validated. Enriched functional pathways, gene mutational landscape, immune cell infiltration, and immunotherapy sensitivity markers associated with high and low APMHO scores were analyzed.\nRESULTS: Screening 107 overall survival-related cancer genes and 402 interacting gene pairs, 6 genes: CRLF2, HSP90AA1, MAP2K1, PAFAH1B2, MYCL and SET genes, were identified and a transcriptional score was obtained. Age, stage and transcriptional score were found to be significant predictors in Cox regression analysis and used to construct a final APMHO model showing an AUC > 0.65 and validated. Transcriptional score, age, pathologic_N, pathologic_T, stage, and TCGA_subtype were significantly different in distribution between high and low APMHO groups. High APMHO samples showed significantly higher mutation rate, enriched tumor-related pathways including Hypoxia, unfold_protein_response, Glycolysis, and mTORC1 signaling, along with differences in immune cell infiltration and immune checkpoint, interferon-γ pathway and m6A regulator expression patterns.\nCONCLUSION: The APMHO score combining transcriptional and clinical variables showed good prognostic ability for HNSCC overall survival outcomes and was associated with different patterns of phenotypical features, immune and mutational landscape, and immunotherapy sensitivity marker expression. Future studies should validate this score in independent clinical cohorts.", "claims": [{"id": "claim-method", "statement": "Further combining Transcriptional_Score, age, gender, and stage for multivariate Cox regression analysis, Transcriptional_Score, age and stage were identified as significant factors, so the final APMHO score was constructed using these 3 elements, i.e. APMHO = 0.8904 * Transcriptional_Score + 0.0224 *age + 0.7614*stage.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37897503", "locator": "sections[11] \"Computation of ‘accurate prediction model of HNSCC overall survival score’ (APMHO) integrating clinical features and transcriptome score: (APMHO) and validation\" paragraph 4", "quote": "Further combining Transcriptional_Score, age, gender, and stage for multivariate Cox regression analysis, Transcriptional_Score, age and stage were identified as significant factors, so the final APMHO score was constructed using these 3 elements, i.e. APMHO = 0.8904 * Transcriptional_Score + 0.0224 *age + 0.7614*stage."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data will be provided on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37897503", "locator": "sections[21] \"Data availability\" paragraph 2", "quote": "Data will be provided on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The APMHO score combining transcriptional and clinical variables showed good prognostic ability for HNSCC overall survival outcomes and was associated with different patterns of phenotypical features, immune and mutational landscape, and immunotherapy sensitivity marker expression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37897503", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The APMHO score combining transcriptional and clinical variables showed good prognostic ability for HNSCC overall survival outcomes and was associated with different patterns of phenotypical features, immune and mutational landscape, and immunotherapy sensitivity marker expression."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The APMHO score combining transcriptional and clinical variables showed good prognostic ability for HNSCC overall survival outcomes and was associated with different patterns of phenotypical features, immune and mutational landscape, and immunotherapy sensitivity marker expression.", "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:37897503"], "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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