Head and neck squamous cell carcinoma-specific prognostic signature and drug sensitive subtypes based on programmed cell death-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38025757", "kind": "science-record", "revision": 3, "content_hash": "sha256:3638e9b936326e97442437d544c32e0913e59a008804f525493a851823c9fc29", "created_at": "2026-11-01T19:02:57Z", "updated_at": "2026-11-01T22:10:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Head and neck squamous cell carcinoma-specific prognostic signature and drug sensitive subtypes based on programmed cell death-related genes.", "doi": "10.7717/peerj.16364", "pmid": "38025757", "authors": ["Chengbo Xu", "Hongfang Xu", "Baimei Liu"], "journal": "PeerJ", "year": 2023, "abstract": "Background: As a complex group of malignancies, head and neck squamous cell carcinoma (HNSC) is one of the leading causes of cancer mortality. This study aims to establish a reliable clinical classification and gene signature for HNSC prognostic prediction and precision treatments.\nMethods: A consensus clustering analysis was performed to group HNSC patients in The Cancer Genome Atlas (TCGA) database based on genes linked to programmed cell death (PCD). Differentially expressed genes (DEGs) between subtypes were identified using the \"limma\" R package. The TCGA prognostic signature and PCD-related prognostic genes were found using a least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis. The robustness of the LASSO analysis was validated using datasets GSE65858 and GSE41613. A cell counting kit-8 (CCK-8) test, Western blot, and real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) were used to evaluate the expression and viability of prognostic genes.\nResults: Four molecular subtypes were identified in PCD-related genes. Subtype C4 had the best prognosis and the highest immune score, while subtype C1 exhibited the most unfavorable outcomes. Three hundred shared DEGs were identified among the four subtypes, and four prognostic genes (CTLA4, CAMK2N1, PLAU and CALML5) were used to construct a TCGA-HNSC prognostic model. High-risk patients manifested poorer prognosis, more inflammatory pathway enrichment, and lower immune cell infiltration. High-risk patients were more prone to immune escape and were more likely to be resistant to Cisplatin and 5-Fluorouracil. Prognosis prediction was validated in external datasets. The expression of CTLA4, CAMK2N1, PLAU and CALML5 was enhanced in CAL-27 and SCC-25 cell lines, and CALML5 inhibited CAL-27 and SCC-25 cell viability.\nConclusion: This study shares novel insights into HNSC classification and provides a reliable PCD-related prognostic signature for prognosis prediction and treatment for patients with HNSC.", "claims": [{"id": "claim-method", "statement": "To prevent overfitting issues, the “glmnet” R package (version 4.1-2, https://cran.r-project.org/web/packages/glmnet/index.html) was used in the LASSO analysis (Hastie, Qian & Tay, 2021).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38025757", "locator": "sections[10] \"Development and verification of the prognostic signature\" paragraph 2", "quote": "To prevent overfitting issues, the “glmnet” R package (version 4.1-2, https://cran.r-project.org/web/packages/glmnet/index.html) was used in the LASSO analysis (Hastie, Qian & Tay, 2021)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data AvailabilityThe following information was supplied regarding data availability:The code is available at GitHub and Zenodo:–https://github.com/BaimeiLiu/code.git.- BaimeiLiu. (2023). BaimeiLiu/code: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8415684 The datasets are available at GEO: GSE65858 and GSE41613. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38025757", "locator": "sections[36] \"Additional Information and Declarations\" paragraph 4", "quote": "Data AvailabilityThe following information was supplied regarding data availability:The code is available at GitHub and Zenodo:–https://github.com/BaimeiLiu/code.git.- BaimeiLiu. (2023). BaimeiLiu/code: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8415684 The datasets are available at GEO: GSE65858 and GSE41613. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613"}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene TCGA-HNSC prognostic model was constructed and externally validated, with 1-year, 3-year, and 5-year AUCs of 0.66, 0.7, and 0.62, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38025757", "locator": "sections[27] \"Construction and validation of HNSC prognostic signature\" paragraph 4", "quote": "HNSC patients considered high risk had a higher OS rate than that of low-risk patients, with a 1-year area under the curve (AUC) of 0.66, 3-year AUC of 0.7, and 5-year AUC of 0.62 (Figs. 7D–7E)."}, {"asset": "source:pmid:38025757", "locator": "sections[27] \"Construction and validation of HNSC prognostic signature\" paragraph 4", "quote": "The predictive value of the HNSC prognostic signature was then validated using the GSE65858 and GSE41613 datasets (Figs. 7F–7I), and a strong performance in HNSC prognosis prediction was observed."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene TCGA-HNSC prognostic model was constructed and externally validated, with 1-year, 3-year, and 5-year AUCs of 0.66, 0.7, and 0.62, respectively.", "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:38025757"], "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:GSE41613"], "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:GSE41613"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38025757", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.16364", "sha256": "sha256:54a32f8535b57cbc6a0d6bd2100d755e1f3a6405e773ec953aae6d29d9183f99", "bytes": 46252, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.16364", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10668860", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE41613", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE41613", "version": null, "note": "Data AvailabilityThe following information was supplied regarding data availability:The code is available at GitHub and Zenodo:–https://github.com/BaimeiLiu/code.git.- BaimeiLiu. (2023). BaimeiLiu/code: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8415684 The datasets are available at GEO: GSE65858 and GSE41613. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Data AvailabilityThe following information was supplied regarding data availability:The code is available at GitHub and Zenodo:–https://github.com/BaimeiLiu/code.git.- BaimeiLiu. (2023). BaimeiLiu/code: v1.0.0 (v1.0.0). 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE65858", "GSE41613"], "resolved_accessions": ["GSE65858", "GSE41613"], "statements": ["Data AvailabilityThe following information was supplied regarding data availability:The code is available at GitHub and Zenodo:–https://github.com/BaimeiLiu/code.git.- BaimeiLiu. (2023). BaimeiLiu/code: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8415684 The datasets are available at GEO: GSE65858 and GSE41613. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613"], "basis": "The paper names GSE65858, GSE41613 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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