Development of a prognostic pyroptosis-related gene signature for head and neck squamous cell carcinoma patient.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35123464", "kind": "science-record", "revision": 4, "content_hash": "sha256:d0730e87b16ef6ac2940abc8ffa11597cc865d69b07377d1e2ce910b5eb294e1", "created_at": "2026-11-01T17:36:52Z", "updated_at": "2026-11-01T22:05:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of a prognostic pyroptosis-related gene signature for head and neck squamous cell carcinoma patient.", "doi": "10.1186/s12935-022-02476-3", "pmid": "35123464", "authors": ["Weiwen Zhu", "Jiayi Zhang", "Mengyao Wang", "Rundong Zhai", "Yanbin Xu", "Jie Wang", "Mengqi Wang", "Hang Zhang", "Laikui Liu"], "journal": "Cancer cell international", "year": 2022, "abstract": "OBJECTIVE: Head and neck squamous cell carcinoma (HNSCC) is a major threat to public health. Pyroptosis is a form of inflammatory programmed cell death that is still incompletely understood. The role of pyroptotic cell death in HNSCC remains to be fully defined. As such, the present study was developed to explore the potential prognostic utility of a pyroptosis-related gene (PRG) signature in HNSCC.\nMETHODS: PRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858). The relative expression of the genes in the prognostic model was assessed via RT-qPCR in tumor and paired adjacent normal tissue samples from a 32-patient cohort. Potential predictors of patient outcomes associated with this 6-gene model were identified through topological degree analyses of a protein-protein interaction network. Moreover, the prognostic value of NLRP3 as a predictor of HNSCC patient prognosis was established through immunohistochemical (IHC) analyses of samples from 176 HNSCC patients. Lastly, in vitro studies were performed to further demonstrate the relevance of NLRP3 in the context of HNSCC development.\nRESULTS: Differentially expressed PRGs were able to readily differentiate between HNSCC tumors and normal tissues. Risk scores derived from the 6-gene PRG model were independent predictors of HNSCC patient prognosis, and genes that were differentially expressed between low- and high-risk groups were associated with tumor immunity. RT-qPCR assays also showed the potential protective role of NLRP3 in HNSCC patients. IHC analyses further supported the value of NLRP3 as a predictor of HNSCC patient outcomes. Invasion and migration assays demonstrated the potential role of NLRP3 in the inhibition of HNSCC development.\nCONCLUSIONS: Overall, these results highlight a novel prognostic gene signature that offers value in the context of HNSCC patient evaluation, although additional research will be essential to elucidate the mechanisms linking these PRGs to HNSCC outcomes.", "claims": [{"id": "claim-method", "statement": "Next, least absolute shrinkage and selection operator (LASSO) (Fig. 4B, C) and Cox regression analyses (Fig. 4D) were utilized to construct a 6-gene model based on an optimal λ value.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35123464", "locator": "sections[16] \"PRG-associated prognostic model development\" paragraph 2", "quote": "Next, least absolute shrinkage and selection operator (LASSO) (Fig. 4B, C) and Cox regression analyses (Fig. 4D) were utilized to construct a 6-gene model based on an optimal λ value."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35123464", "locator": "sections[32] \"notes\" paragraph 1", "quote": "The datasets used during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "PRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35123464", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "PRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "PRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858).", "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:35123464"], "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. 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