Cancer associated fibroblast derived gene signature determines cancer subtypes and prognostic model construction in head and neck squamous cell carcinomas.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36404634", "kind": "science-record", "revision": 3, "content_hash": "sha256:a95b1bdf6a7f7e370d1970d5b2b30a1a66ffebe28e1e007e87db246d9383b414", "created_at": "2026-11-01T19:00:51Z", "updated_at": "2026-11-01T22:08:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Cancer associated fibroblast derived gene signature determines cancer subtypes and prognostic model construction in head and neck squamous cell carcinomas.", "doi": "10.1002/cam4.5383", "pmid": "36404634", "authors": ["Sangqing Wu", "Cheng Huang", "Liangping Su", "Ping-Pui Wong", "Yongsheng Huang", "Renhui Chen", "Peiliang Lin", "Yuchu Ye", "Pang Song", "Ping Han", "Xiaoming Huang"], "journal": "Cancer medicine", "year": 2023, "abstract": "BACKGROUND: Head and neck squamous cell carcinomas (HNSCC) are the most common type of head and neck cancer with an unimproved prognosis over the past decades. Although the role of cancer-associated-fibroblast (CAF) has been demonstrated in HNSCC, the correlation between CAF-derived gene expression and patient prognosis remains unknown.\nMETHODS: A total of 528 patients from TCGA database and 270 patients from GSE65858 database were contained in this study. After extracting 66 CAF-related gene expression data from TCGA database, consensus clustering was performed to identify different HNSCC subtypes. Limma package was used to distinguish the differentially expression genes (DEGs) between these subtypes, followed by Lasso regression analysis to construct a prognostic model. The model was validated by performing Kaplan-Meier survival, ROC and risk curve, univariate and multivariate COX regression analysis. GO, KEGG, GSEA, ESTIMATE and ssGSEA analyses was performed to explort the potential mechanism leading to different prognosis.\nRESULTS: Based on the 66 CAF-related gene expression pattern we stratitied HNSCC patients into two previously unreported subtypes with different clinical outcomes. A prognostic model composed of 15 DEGs was constructed and validated. In addition, bioinformatics analysis showed that the prognostic risk of HNSCC patients was also negatively correlated to immune infiltration, implying the role of tumor immune escape in HNSCC prognosis and treatment option.\nCONCLUSIONS: The study develops a reliable prognostic prediction tool and provides a theoretical treatment guidance for HNSCC patients.", "claims": [{"id": "claim-method", "statement": "R package “survival” was used to perform univariate COX regression analysis to extract DEGs related to patient prognosis (p < 0.05). Subsequently, those DEGs were included to construct a Least Absolute Shrinkage and Selection Operator (Lasso) regression model using the R package “glmnet” and “survival”.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36404634", "locator": "sections[7] \"Survival analysis and construction of prognostic model\" paragraph 2", "quote": "R package “survival” was used to perform univariate COX regression analysis to extract DEGs related to patient prognosis (p < 0.05). Subsequently, those DEGs were included to construct a Least Absolute Shrinkage and Selection Operator (Lasso) regression model using the R package “glmnet” and “survival”."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the TCGA repository, https://portal.gdc.cancer.gov/, and GEO repository, https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36404634", "locator": "sections[30] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The datasets generated and/or analyzed during the current study are available in the TCGA repository, https://portal.gdc.cancer.gov/, and GEO repository, https://www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, the risk score was identified as an independent prognostic indicator according to univariate COX regression (training cohort, HR = 6.358, 95% CI: 3.240–12.477, p < 0.001; validation cohort, HR = 2.496, 95% CI: 1.425–4.372, p = 0.001) and multivariate COX regression (training cohort, HR = 6.343, 95% CI: 3.145–12.795, p < 0.001; validation cohort, HR = 3.363, 95% CI: 1.901–5.950, p < 0.001) (Figure 4G,H).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Furthermore, the risk score was identified as an independent prognostic indicator according to univariate COX regression (training cohort, HR = 6.358, 95% CI: 3.240–12.477, p < 0.001; validation cohort, HR = 2.496, 95% CI: 1.425–4.372, p = 0.001) and multivariate COX regression (training cohort, HR = 6.343, 95% CI: 3.145–12.795, p < 0.001; validation cohort, HR = 3.363, 95% CI: 1.901–5.950, p < 0.001) (Figure 4G,H)."}, "evidence": [{"asset": "source:pmid:36404634", "locator": "sections[17] \"Combined Kaplan–Meier survival curve, ROC curve, risk curve and independent prognostic analysis indicates a good performance of the HNSCC prognostic model\" paragraph 4", "quote": "Furthermore, the risk score was identified as an independent prognostic indicator according to univariate COX regression (training cohort, HR = 6.358, 95% CI: 3.240–12.477, p < 0.001; validation cohort, HR = 2.496, 95% CI: 1.425–4.372, p = 0.001) and multivariate COX regression (training cohort, HR = 6.343, 95% CI: 3.145–12.795, p < 0.001; validation cohort, HR = 3.363, 95% CI: 1.901–5.950, p < 0.001) (Figure 4G,H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Furthermore, the risk score was identified as an independent prognostic indicator according to univariate COX regression (training cohort, HR = 6.358, 95% CI: 3.240–12.477, p < 0.001; validation cohort, HR = 2.496, 95% CI: 1.425–4.372, p = 0.001) and multivariate COX regression (training cohort, HR = 6.343, 95% CI: 3.145–12.795, p < 0.001; validation cohort, HR = 3.363, 95% CI: 1.901–5.950, p < 0.001) (Figure 4G,H).", "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:36404634"], "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:36404634", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/cam4.5383", "sha256": "sha256:4398e9ef43dcd1b03ab4657af0ac4c3a58b91ca2f736a47fa4c96b21093f85c8", "bytes": 33021, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1002/cam4.5383", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10028128", "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": "The datasets generated and/or analyzed during the current study are available in the TCGA repository, https://portal.gdc.cancer.gov/, and GEO repository, https://www.ncbi.nlm.nih.gov/geo/.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analyzed during the current study are available in the TCGA repository, https://portal.gdc.cancer.gov/, and GEO repository, https://www.ncbi.nlm.nih.gov/geo/.", "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": "R package “survival” was used to perform univariate COX regression analysis to extract DEGs related to patient prognosis (p < 0.05). Subsequently, those DEGs were included to construct a Least Absolute Shrinkage and Selection Operator (Lasso) regression model using the R package “glmnet” and “survival”."}}, {"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": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "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": 0.7, "band": "medium", "quote": "Limma"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cox_ph", "expected_value": 95.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}, {"id": "target-published-estimate-p-value", "claim_id": "claim-target-result", "operation": "cox_ph", "expected_value": 0.001, "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": ["The datasets generated and/or analyzed during the current study are available in the TCGA repository, https://portal.gdc.cancer.gov/, and GEO repository, https://www.ncbi.nlm.nih.gov/geo/."], "basis": "The paper names GSE65858 in Gene Expression Omnibus (GEO) database 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:36404634", "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": "2026-11-01T18:16:06Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9717, "completion_tokens": 2237, "cached_tokens": 264}}, "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:36404634", "type": "paper", "label": "pmid:36404634"}, {"id": "source:pmid:36404634", "type": "manuscript", "label": "PMID 36404634 full text"}, {"id": "data:geo:GSE65858", "type": "dataset", "label": "GEO GSE65858"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36404634", "type": "paper", "label": "pmid:36404634"}, {"id": "source:pmid:36404634", "type": "manuscript", "label": "PMID 36404634 full text"}, {"id": "data:geo:GSE65858", "type": "dataset", "label": "GEO GSE65858"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36404634", "type": "extraction", "used": ["source:pmid:36404634"], "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:36404634", "parent_hash": "sha256:3f5675d3f46e7de7693af2556abaccb5b63f75de3ea81f3ef6a979976735fcb1", "fork_of": null, "generation": 2}, "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": ["R package “survival” was used to perform univariate COX regression analysis to extract DEGs related to patient prognosis (p < 0.05). Subsequently, those DEGs were included to construct a Least Absolute Shrinkage and Selection Operator (Lasso) regression model using the R package “glmnet” and “survival”."], "order_basis": "analysis_only", "id": "assay:1"}]}