Molecular Evolutionary Landscape of the Immune Microenvironment of Head and Neck Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37509156", "kind": "science-record", "revision": 4, "content_hash": "sha256:300725e56ca1c5c3d2dd4f2ba0ce79e84306ffa1702bf09005b91296b0260608", "created_at": "2026-11-01T19:02:16Z", "updated_at": "2026-11-01T22:52:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Molecular Evolutionary Landscape of the Immune Microenvironment of Head and Neck Cancer.", "doi": "10.3390/biom13071120", "pmid": "37509156", "authors": ["Baoyi Shao", "Zheng Ye", "Bo Sun", "Zhongdang Xiao"], "journal": "Biomolecules", "year": 2023, "abstract": "Head and neck cancer is a highly heterogeneous malignant tumor. Numerous studies have shown that the immune microenvironment of head and neck cancer has a significant impact on its occurrence and development, as well as its prognosis. However, there have been fewer studies related to the accurate immunophenotyping of head and neck cancer. In this study, we used gene expression profile information and clinical information from the TCGA-HNSC cohort (502 samples) and the GSE655858 cohort (270 samples) to identify and independently validate three immune subtypes (Cluster1-Cluster3) with different immune-related molecular profiles and clinical outcomes. Cluster2, which is mainly dominated by B-lymphocyte infiltration, was found to have the best prognosis. In addition, a support vector machine (SVM)-based classifier was constructed, which could accurately classify HNSC based on 19 genes. Furthermore, the results of the prognostic analysis showed activation of antibody-secreting B-lymphocyte function, which showed a good prognostic effect in all three immune subtypes of HNSC. Finally, the immune evolutionary landscape of HNSC was constructed in an attempt to explain the evolutionary pattern of the immune subtypes of HNSC. In summary, we provide a conceptual framework for understanding the tumor immune microenvironment in HNSC and demonstrate the importance of immune infiltration of B lymphocytes in HNSC. Further research is needed to assess the importance of these immunophenotypes in combination drug therapy and to provide a basis for screening appropriate patients for immunotherapy.", "claims": [{"id": "claim-method", "statement": "The prognosis of immune subtypes was evaluated using the log-rank test and univariate cox regression model, with age as a covariate, and OS and DFS as the focus.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37509156", "locator": "sections[10] \"2.8. Prognostic Evaluation of Immune Subtypes\" paragraph 2", "quote": "The prognosis of immune subtypes was evaluated using the log-rank test and univariate cox regression model, with age as a covariate, and OS and DFS as the focus."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Proteome profiling and clinical and transcriptome data about Head and neck cancer were downloaded from TCGA Database (https://portal.gdc.cancer.gov/) and GEO Database (https://www.ncbi.nlm.nih.gov/geo/). Please contact the author if you want to access the codes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37509156", "locator": "sections[29] \"notes\" paragraph 1", "quote": "Proteome profiling and clinical and transcriptome data about Head and neck cancer were downloaded from TCGA Database (https://portal.gdc.cancer.gov/) and GEO Database (https://www.ncbi.nlm.nih.gov/geo/). Please contact the author if you want to access the codes."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The Kaplan–Meier survival analysis showed that Cluster2 had the best prognosis, Cluster1 the worst, and Cluster3 was in between (hazard ratio: Cluster 2 > Cluster 3 > Cluster 1).", "kind": "quantitative", "target": {"value": 0.01, "unit": "p-value", "tolerance": null, "source": "There is a significant difference in survival between the three immune subtypes (p = 0.01)"}, "evidence": [{"asset": "source:pmid:37509156", "locator": "sections[13] \"3.1. Identification of Potential HNSC Immune Subtypes\" paragraph 2", "quote": "The Kaplan–Meier survival analysis showed that Cluster2 had the best prognosis, Cluster1 the worst, and Cluster3 was in between (hazard ratio: Cluster 2 > Cluster 3 > Cluster 1)."}, {"asset": "source:pmid:37509156", "locator": "sections[13] \"3.1. Identification of Potential HNSC Immune Subtypes\" paragraph 2", "quote": "There is a significant difference in survival between the three immune subtypes (p = 0.01)"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The Kaplan–Meier survival analysis showed that Cluster2 had the best prognosis, Cluster1 the worst, and Cluster3 was in between (hazard ratio: Cluster 2 > Cluster 3 > Cluster 1).", "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:37509156"], "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:37509156", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/biom13071120", "sha256": "sha256:4b3a3781756a1070d7425a3ad451cca8b31f50a44f187b6f487d16016a25bee7", "bytes": 39301, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/biom13071120", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10377423", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE65858", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65858", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE65858", "version": null, "note": "Proteome profiling and clinical and transcriptome data about Head and neck cancer were downloaded from TCGA Database (https://portal.gdc.cancer.gov/) and GEO Database (https://www.ncbi.nlm.nih.gov/geo/). Please contact the author if you want to access the codes.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Proteome profiling and clinical and transcriptome data about Head and neck cancer were downloaded from TCGA Database (https://portal.gdc.cancer.gov/) and GEO Database (https://www.ncbi.nlm.nih.gov/geo/). Please contact the author if you want to access the codes.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1607684", "GSM1607685", "GSM1607686", "GSM1607687", "GSM1607688", "GSM1607689", "GSM1607690", "GSM1607691", "GSM1607692", "GSM1607693", "GSM1607694", "GSM1607695", "GSM1607696", "GSM1607697", "GSM1607698", "GSM1607699", "GSM1607700", "GSM1607701", "GSM1607702", "GSM1607703", "GSM1607704", "GSM1607705", "GSM1607706", "GSM1607707", "GSM1607708", "GSM1607709", "GSM1607710", "GSM1607711", "GSM1607712", "GSM1607713", "GSM1607714", "GSM1607715", "GSM1607716", "GSM1607717", "GSM1607718", "GSM1607719", "GSM1607720", "GSM1607721", "GSM1607722", "GSM1607723", "GSM1607724", "GSM1607725", "GSM1607726", "GSM1607727", "GSM1607728", "GSM1607729", "GSM1607730", "GSM1607731", "GSM1607732", "GSM1607733", "GSM1607734", "GSM1607735", "GSM1607736", "GSM1607737", "GSM1607738", "GSM1607739", "GSM1607740", "GSM1607741", "GSM1607742", "GSM1607743", "GSM1607744", "GSM1607745", "GSM1607746", "GSM1607747", "GSM1607748", "GSM1607749", "GSM1607750", "GSM1607751", "GSM1607752", "GSM1607753", "GSM1607754", "GSM1607755", "GSM1607756", "GSM1607757", "GSM1607758", "GSM1607759", "GSM1607760", "GSM1607761", "GSM1607762", "GSM1607763", "GSM1607764", "GSM1607765", "GSM1607766", "GSM1607767", "GSM1607768", "GSM1607769", "GSM1607770", "GSM1607771", "GSM1607772", "GSM1607773", "GSM1607774", "GSM1607775", "GSM1607776", "GSM1607777", "GSM1607778", "GSM1607779", "GSM1607780", "GSM1607781", "GSM1607782", "GSM1607783", "GSM1607784", "GSM1607785", "GSM1607786", "GSM1607787", "GSM1607788", "GSM1607789", "GSM1607790", "GSM1607791", "GSM1607792", "GSM1607793", "GSM1607794", "GSM1607795", "GSM1607796", "GSM1607797", "GSM1607798", "GSM1607799", "GSM1607800", "GSM1607801", "GSM1607802", "GSM1607803", "GSM1607804", "GSM1607805", "GSM1607806", "GSM1607807", "GSM1607808", "GSM1607809", "GSM1607810", "GSM1607811", "GSM1607812", "GSM1607813", "GSM1607814", "GSM1607815", "GSM1607816", "GSM1607817", "GSM1607818", "GSM1607819", "GSM1607820", "GSM1607821", "GSM1607822", "GSM1607823", "GSM1607824", "GSM1607825", "GSM1607826", "GSM1607827", "GSM1607828", "GSM1607829", "GSM1607830", "GSM1607831", "GSM1607832", "GSM1607833", "GSM1607834", "GSM1607835", "GSM1607836", "GSM1607837", "GSM1607838", "GSM1607839", "GSM1607840", "GSM1607841", "GSM1607842", "GSM1607843", "GSM1607844", "GSM1607845", "GSM1607846", "GSM1607847", "GSM1607848", "GSM1607849", "GSM1607850", "GSM1607851", "GSM1607852", "GSM1607853", "GSM1607854", "GSM1607855", "GSM1607856", "GSM1607857", "GSM1607858", "GSM1607859", "GSM1607860", "GSM1607861", "GSM1607862", "GSM1607863", "GSM1607864", "GSM1607865", "GSM1607866", "GSM1607867", "GSM1607868", "GSM1607869", "GSM1607870", "GSM1607871", "GSM1607872", "GSM1607873", "GSM1607874", "GSM1607875", "GSM1607876", "GSM1607877", "GSM1607878", "GSM1607879", "GSM1607880", "GSM1607881", "GSM1607882", "GSM1607883", "GSM1607884", "GSM1607885", "GSM1607886", "GSM1607887", "GSM1607888", "GSM1607889", "GSM1607890", "GSM1607891", "GSM1607892", "GSM1607893", "GSM1607894", "GSM1607895", "GSM1607896", "GSM1607897", "GSM1607898", "GSM1607899", "GSM1607900", "GSM1607901", "GSM1607902", "GSM1607903", "GSM1607904", "GSM1607905", "GSM1607906", "GSM1607907", "GSM1607908", "GSM1607909", "GSM1607910", "GSM1607911", "GSM1607912", "GSM1607913", "GSM1607914", "GSM1607915", "GSM1607916", "GSM1607917", "GSM1607918", "GSM1607919", "GSM1607920", "GSM1607921", "GSM1607922", "GSM1607923", "GSM1607924", "GSM1607925", "GSM1607926", "GSM1607927", "GSM1607928", "GSM1607929", "GSM1607930", "GSM1607931", "GSM1607932", "GSM1607933", "GSM1607934", "GSM1607935", "GSM1607936", "GSM1607937", "GSM1607938", "GSM1607939", "GSM1607940", "GSM1607941", "GSM1607942", "GSM1607943", "GSM1607944", "GSM1607945", "GSM1607946", "GSM1607947", "GSM1607948", "GSM1607949", "GSM1607950", "GSM1607951", "GSM1607952", "GSM1607953"], "sample_count": 270, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "Gene expression patterns and TP53 mutations are associated with HPV RNA status, lymph node metastasis, and survival in head and neck cancer", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE65858 not downloaded", "asset:data:geo:GSE65858 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The prognosis of immune subtypes was evaluated using the log-rank test and univariate cox regression model, with age as a covariate, and OS and DFS as the focus."}}, {"id": "method-2", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "support vector machine"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cox_ph", "expected_value": 0.01, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold."}], "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": ["GSE65858"], "resolved_accessions": ["GSE65858"], "statements": ["Proteome profiling and clinical and transcriptome data about Head and neck cancer were downloaded from TCGA Database (https://portal.gdc.cancer.gov/) and GEO Database (https://www.ncbi.nlm.nih.gov/geo/). Please contact the author if you want to access the codes."], "basis": "The paper names 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:37509156", "data:geo:GSE65858"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE65858"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:48:02Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10853, "completion_tokens": 2274, "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:37509156", "type": "paper", "label": "pmid:37509156"}, {"id": "source:pmid:37509156", "type": "manuscript", "label": "PMID 37509156 full text"}, {"id": "data:geo:GSE65858", "type": "dataset", "label": "GEO GSE65858"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37509156", "type": "paper", "label": "pmid:37509156"}, {"id": "source:pmid:37509156", "type": "manuscript", "label": "PMID 37509156 full text"}, {"id": "data:geo:GSE65858", "type": "dataset", "label": "GEO GSE65858"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37509156", "type": "extraction", "used": ["source:pmid:37509156"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE65858", "type": "accession_resolution", "used": ["data:geo:GSE65858"], "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:37509156", "parent_hash": "sha256:2da09e5f61dce7a892e2d0ccd6a6d7b1584e17d20c29fafa190bcd319fc8e912", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE65858 licence not read", "asset:data:geo:GSE65858 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": ["The prognosis of immune subtypes was evaluated using the log-rank test and univariate cox regression model, with age as a covariate, and OS and DFS as the focus."], "order_basis": "analysis_only", "id": "assay:1"}]}