Identification of a Novel Signature Predicting Overall Survival 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:34631779", "kind": "science-record", "revision": 4, "content_hash": "sha256:25cd4f20f5bcfbae91913f8ae08deb91aece44da95b3dd28d66133582383cfb2", "created_at": "2026-11-01T17:36:17Z", "updated_at": "2026-11-01T22:04:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Novel Signature Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma.", "doi": "10.3389/fsurg.2021.717084", "pmid": "34631779", "authors": ["Haige Zheng", "Huixian Liu", "Yumin Lu", "Hengguo Li"], "journal": "Frontiers in surgery", "year": 2021, "abstract": "Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment. Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters. Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.", "claims": [{"id": "claim-method", "statement": "Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34631779", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34631779", "locator": "sections[24] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established and validated the seven-gene HNSCCSig and constructed a nomogram combining HNSCCSig with clinical parameters to identify high-risk HNSCC patients with poor prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34631779", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established and validated the seven-gene HNSCCSig and constructed a nomogram combining HNSCCSig with clinical parameters to identify high-risk HNSCC patients with poor prognosis.", "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:34631779"], "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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Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig)."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "differential_expression", "specific": "edger", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "edgeR"}}, {"id": "method-4", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-5", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE6631", "GSE13601", "GSE30784", "GSE31056", "GSE33205", "GSE37991", "GSE51985", "GSE59102", "GSE138206", "GSE65858"], "resolved_accessions": ["GSE6631", "GSE13601", "GSE30784", "GSE31056", "GSE33205", "GSE37991", "GSE51985", "GSE59102", "GSE138206", "GSE65858"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."], "basis": "The paper names GSE6631, GSE13601, GSE30784, GSE31056, GSE33205, GSE37991, GSE51985, GSE59102, GSE138206, GSE65858 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:34631779", "data:geo:GSE13601"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE13601"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:03:49Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11857, "completion_tokens": 1317, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:34631779", "type": "paper", "label": "pmid:34631779"}, {"id": "source:pmid:34631779", "type": "manuscript", "label": "PMID 34631779 full text"}, {"id": "data:geo:GSE13601", "type": "dataset", "label": "GEO GSE13601"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34631779", "type": "paper", "label": "pmid:34631779"}, {"id": "source:pmid:34631779", "type": "manuscript", "label": "PMID 34631779 full text"}, {"id": "data:geo:GSE13601", "type": "dataset", "label": "GEO GSE13601"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34631779", "type": "extraction", "used": ["source:pmid:34631779"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE13601", "type": "accession_resolution", "used": ["data:geo:GSE13601"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:34631779", "parent_hash": "sha256:0a832018c36504aacd9b08b5774acc22379e5ae03ab9030a4ccb5eb97782b314", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE13601 licence not read", "asset:data:geo:GSE13601 not downloaded", "asset:data:geo:GSE138206 licence not read", "asset:data:geo:GSE138206 not downloaded", "asset:data:geo:GSE30784 licence not read", "asset:data:geo:GSE30784 not downloaded", "asset:data:geo:GSE31056 licence not read", "asset:data:geo:GSE31056 not downloaded", "asset:data:geo:GSE33205 licence not read", "asset:data:geo:GSE33205 not downloaded", "asset:data:geo:GSE37991 licence not read", "asset:data:geo:GSE37991 not downloaded", "asset:data:geo:GSE51985 licence not read", "asset:data:geo:GSE51985 not downloaded", "asset:data:geo:GSE59102 licence not read", "asset:data:geo:GSE59102 not downloaded", "asset:data:geo:GSE65858 licence not read", "asset:data:geo:GSE65858 not downloaded", "asset:data:geo:GSE6631 licence not read", "asset:data:geo:GSE6631 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig)."], "order_basis": "analysis_only", "id": "assay:1"}]}