Transcriptome analysis reveals the prognostic and immune infiltration characteristics of glycolysis and hypoxia in head and neck squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35361159", "kind": "science-record", "revision": 4, "content_hash": "sha256:a38e8f4f64cbf645fed11f6ca7a8db0bb465f6ac41eb9f166480885cd6229f8e", "created_at": "2026-11-01T18:14:10Z", "updated_at": "2026-11-01T22:06:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcriptome analysis reveals the prognostic and immune infiltration characteristics of glycolysis and hypoxia in head and neck squamous cell carcinoma.", "doi": "10.1186/s12885-022-09449-9", "pmid": "35361159", "authors": ["Jun Liu", "Jianjun Lu", "Wenli Li"], "journal": "BMC cancer", "year": 2022, "abstract": "BACKGROUND: This study aims to construct a new prognostic gene signature in survival prediction and risk stratification for patients with Head and neck squamous cell carcinoma (HNSCC).\nMETHOD: The transcriptome profiling data and hallmark gene sets in the Molecular Signatures Database was used to explore the cancer hallmarks most relevant to the prognosis of HNSCC patients. Differential gene expression analysis, weighted gene co-expression network analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct the prognostic gene signature. The predictive ability of gene signature was verified in the TCGA HNSCC cohort as the training set and the GEO HNSCC cohorts (GSE41613 and GSE42743) as the validation sets, respectively. Moreover, the correlations between risk scores and immune infiltration patterns, as well as risk scores and genomic changes were explored.\nRESULTS: A total of 3391 differentially expressed genes in HNSCC were screened. Glycolysis and hypoxia were screened as the main risk factors for OS in HNSCC. Using univariate Cox analysis, 97 prognostic candidates were identified (P < 0.05). Top 10 important genes were then screened out by random forest. Using multiple combinatorial screening, a combination with less genes and more significant P value was used to construct the prognostic gene signature (RNF144A, STC1, P4HA1, FMNL3, ANO1, BASP1, MME, PLEKHG2 and DKK1). Kaplan-Meier analysis showed that patients with higher risk scores had worse overall survival (p < 0.001). The ROC curve showed that the risk score had a good predictive efficiency (AUC > 0.66). Subsequently, the predictive ability of the risk score was verified in the validation sets. Moreover, the two-factor survival analysis combining the cancer hallmarks and risk scores suggested that HNSCC patients with the high hypoxia or glycolysis & high risk-score showed the worst prognosis. Besides, a nomogram based on the nine-gene signature was established for clinical practice. Furthermore, the risk score was significantly related to tumor immune infiltration profiles and genome changes.\nCONCLUSION: This nine-gene signature associated with glycolysis and hypoxia can not only be used for prognosis prediction and risk stratification, but also may be a potential therapeutic target for patients with HNSCC.", "claims": [{"id": "claim-method", "statement": "The Kaplan–Meier method was used to perform survival analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35361159", "locator": "sections[5] \"Survival analysis based on the risk score\" paragraph 2", "quote": "The Kaplan–Meier method was used to perform survival analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in the current study are available in the TCGA repository (http://cancergenome.nih.gov/) and the GEO (https://www.ncbinlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35361159", "locator": "sections[31] \"Availability of data and materials\" paragraph 2", "quote": "The datasets analyzed in the current study are available in the TCGA repository (http://cancergenome.nih.gov/) and the GEO (https://www.ncbinlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ROC curve showed that the risk score had a good predictive efficiency (AUC > 0.66).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35361159", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The ROC curve showed that the risk score had a good predictive efficiency (AUC > 0.66)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The ROC curve showed that the risk score had a good predictive efficiency (AUC > 0.66).", "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:35361159"], "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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