Hypoxia-related LncRNAs signature predicts prognosis and is associated with immune infiltration and progress of head and neck squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35818500", "kind": "science-record", "revision": 5, "content_hash": "sha256:3efe3f232b71b2d3d1870527e817edf267440c214524c98b5d03c1fc43136755", "created_at": "2026-11-01T18:14:53Z", "updated_at": "2026-11-01T22:49:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Hypoxia-related LncRNAs signature predicts prognosis and is associated with immune infiltration and progress of head and neck squamous cell carcinoma.", "doi": "10.1016/j.bbrep.2022.101304", "pmid": "35818500", "authors": ["Minhan Li", "Shaowei Mao", "Lixing Li", "Muyun Wei"], "journal": "Biochemistry and biophysics reports", "year": 2022, "abstract": "Background: Disclosing prognostic information is necessary to enable good treatment selection and improve patient outcomes. Previous studies suggest that hypoxia is associated with an adverse prognosis in patients with HNSCC and that long non-coding RNAs (lncRNAs) show functions in hypoxia-associated cancer biology. Nevertheless, the understanding of lncRNAs in hypoxia related HNSCC progression remains confusing.\nMethods: Data were downloaded from TCGA and GEO database. Bioinformatic tools including R packages GEOquery, limma, pheatmap, ggplot2, clusterProfiler, survivalROC and survcomp and LASSO cox analysis were utilized. Si-RNA transfection, CCK8 and real-time quantified PCR were used in functional study.\nResults: GEO data (GSE182734) revealed that lncRNA regulation may be important in hypoxia related response of HNSCC cell lines. Further analysis in TCGA data identified 314 HRLs via coexpression analysis between differentially expressed lncRNAs and hypoxia-related mRNAs. 23 HRLs were selected to build the prognosis predicting model using lasso Cox regression analyses. Our model showed excellent performance in predicting survival outcomes among patients with HNSCC in both the training and validation sets. We also found that the risk scores were related to tumor stage and to tumor immune infiltration. Moreover, LINC01116 were selected as a functional study target. The knockdown of LINC01116 significantly inhibited the proliferation of HNSCC cells and effected the hypoxia induced immune and the NF-κB/AKT signaling.\nConclusions: Data analysis of large cohorts and functional experimental validation in our study suggest that hypoxia related lncRNAs play an important role in the progression of HNSCC, and its expression model can be used for prognostic prediction.", "claims": [{"id": "claim-method", "statement": "A total of 1000 lasso penalty analyses were further performed on 62 HRLs, which were significantly related to the OS of HNSCC (p ≤ 0.01), more than 900 times.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35818500", "locator": "sections[12] \"Construction of the HRLs prognostic model\" paragraph 4", "quote": "A total of 1000 lasso penalty analyses were further performed on 62 HRLs, which were significantly related to the OS of HNSCC (p ≤ 0.01), more than 900 times."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data we used were obtained from open accessed public database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35818500", "locator": "sections[26] \"Data availability\" paragraph 2", "quote": "The data we used were obtained from open accessed public database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the training dataset, ROC curves were plotted and the AUCs for predicting 2-, 3-, 4- and 5-year survival were 0.72, 0.729, 0.74 and 0.737, respectively, demonstrating excellent prognostic capacity (Fig. 2C).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35818500", "locator": "sections[13] \"Validation of the HRLs prognostic model\" paragraph 2", "quote": "In the training dataset, ROC curves were plotted and the AUCs for predicting 2-, 3-, 4- and 5-year survival were 0.72, 0.729, 0.74 and 0.737, respectively, demonstrating excellent prognostic capacity (Fig. 2C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In the training dataset, ROC curves were plotted and the AUCs for predicting 2-, 3-, 4- and 5-year survival were 0.72, 0.729, 0.74 and 0.737, respectively, demonstrating excellent prognostic capacity (Fig. 2C).", "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:35818500"], "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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