Identification of prognostic genes in oral squamous cell carcinoma microenvironment.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35275524", "kind": "science-record", "revision": 3, "content_hash": "sha256:0f03ea5be2f91294d89b5e606850107ba2f7f48400dcbfa3304ca241ef2ac588", "created_at": "2026-11-01T18:14:03Z", "updated_at": "2026-11-01T21:27:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of prognostic genes in oral squamous cell carcinoma microenvironment.", "doi": "10.3233/CBM-210432", "pmid": "35275524", "authors": ["Longbiao Zhu", "Xinyu Zhang", "Yan Chen", "Donglin Yan", "Jing Han"], "journal": "Cancer biomarkers : section A of Disease markers", "year": 2022, "abstract": "BACKGROUND: Numerous studies reveal the clinical significance of tumor microenvironment (TME) in multiple cancers. The association between TME in oral squamous cell carcinoma (OSCC) and clinical outcomes remains unsolved.\nOBJECTIVE: This study aims to exhibit the TME of OSCC and identified the prognostic marker.\nMETHODS: Gene expression profile and clinical data OSCC patients were from the TCGA database. The validated stage data was from the Gene Expression Omnibus (GSE65858). Immune/stromal scores of each patient were calculated by ESTIMATE algorithm. Biological functional prediction was conducted. Prognostic genes identified by survival analysis. Nomogram and Receiver operating characteristic curves were employed to test the predicting power. TIMER database was applied to evaluated the immune infiltrates.\nRESULTS: Lower immune scores were observed in male patients (P= 0.0107) and different primary tumor sites of oral cavity with different stromal scores (P= 0.0328). The Differentially expressed genes (DEGs) were involved in immune related pathways. HGF gene (hepatocyte growth factor) was prognostic related and with a better prognostic performance when combined with clinical features (AUC=TCGA 0.638, AUC=GEO 0.714). HGF was significantly related with B cell, CD4ï⁢¼⁢T cell, CD8+T cell, macrophage, neutrophils, and dendritic cell infiltration.\nCONCLUSION: The current study analyzed the TME and presented immune related prognostic biomarkers for OSCC.", "claims": [{"id": "claim-method", "statement": "Immune/stromal scores of each patient were calculated by ESTIMATE algorithm. Biological functional prediction was conducted. Prognostic genes identified by survival analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35275524", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Immune/stromal scores of each patient were calculated by ESTIMATE algorithm. Biological functional prediction was conducted. Prognostic genes identified by survival analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Gene expression profile and clinical data OSCC patients were from the TCGA database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35275524", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Gene expression profile and clinical data OSCC patients were from the TCGA database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "HGF gene (hepatocyte growth factor) was prognostic related and with a better prognostic performance when combined with clinical features (AUC=TCGA 0.638, AUC=GEO 0.714).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35275524", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "HGF gene (hepatocyte growth factor) was prognostic related and with a better prognostic performance when combined with clinical features (AUC=TCGA 0.638, AUC=GEO 0.714)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "HGF gene (hepatocyte growth factor) was prognostic related and with a better prognostic performance when combined with clinical features (AUC=TCGA 0.638, AUC=GEO 0.714).", "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:35275524"], "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:GSE65858"], "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:GSE65858"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35275524", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3233/CBM-210432", "sha256": 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