Natural killer cell-related prognosis signature characterizes immune landscape and predicts prognosis of HNSCC.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36263048", "kind": "science-record", "revision": 4, "content_hash": "sha256:3ae1e70ca186b9542a59b1c5bbaddf1b04d0c07371ad50a9bf9fb714c9b0cd1b", "created_at": "2026-11-01T18:15:39Z", "updated_at": "2026-11-01T22:07:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Natural killer cell-related prognosis signature characterizes immune landscape and predicts prognosis of HNSCC.", "doi": "10.3389/fimmu.2022.1018685", "pmid": "36263048", "authors": ["Hao Chi", "Xixi Xie", "Yingjie Yan", "Gaoge Peng", "Dorothee Franziska Strohmer", "Guichuan Lai", "Songyun Zhao", "Zhijia Xia", "Gang Tian"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Head and neck squamous cell carcinoma (HNSCC), the most common head and neck cancer, is highly aggressive and heterogeneous, resulting in variable prognoses and immunotherapeutic outcomes. Natural killer (NK) cells play essential roles in malignancies' development, diagnosis, and prognosis. The purpose of this study was to establish a reliable signature based on genes related to NK cells (NRGs), thus providing a new perspective for assessing immunotherapy response and prognosis of HNSCC patients.\nMethods: In this study, NRGs were used to classify HNSCC from the TCGA-HNSCC and GEO cohorts. The genes were evaluated using univariate cox regression analysis based on the differential analysis of normal and tumor samples in TCGA-HNSCC conducted using the \"limma\" R package. Thereafter, we built prognostic gene signatures using LASSO-COX analysis. External validation was carried out in the GSE41613 cohort. Immunity analysis based on NRGs was performed via several methods, such as CIBERSORT, and immunotherapy response was evaluated by TIP portal website.\nResults: With the TCGA-HNSCC data, we established a nomogram based on the 17-NRGs signature and a variety of clinicopathological characteristics. The low-risk group exhibited a better effect when it came to immunotherapy.\nConclusions: 17-NRGs signature and nomograms demonstrate excellent predictive performance and offer new perspectives for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology research.", "claims": [{"id": "claim-method", "statement": "LASSO-Cox regression model was used to construct NRGs signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36263048", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "LASSO-Cox regression model was used to construct NRGs signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36263048", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 17-NRGs signature and nomograms demonstrated excellent predictive performance and offered new perspectives for assessing pre-immune efficacy and advancing precision immuno-oncology research.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36263048", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "17-NRGs signature and nomograms demonstrate excellent predictive performance and offer new perspectives for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology research."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The 17-NRGs signature and nomograms demonstrated excellent predictive performance and offered new perspectives for assessing pre-immune efficacy and advancing precision immuno-oncology research.", "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:36263048"], "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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