Inflammation-Related Gene Signature for Predicting the Prognosis 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:35592543", "kind": "science-record", "revision": 3, "content_hash": "sha256:d75cfcaab2f5445dff785f4b10eb4c2a9334c53eac531effe6ed9edcec6b8f60", "created_at": "2026-11-01T18:14:29Z", "updated_at": "2026-11-01T21:27:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Inflammation-Related Gene Signature for Predicting the Prognosis of Head and Neck Squamous Cell Carcinoma.", "doi": "10.2147/IJGM.S354349", "pmid": "35592543", "authors": ["Yilong Lu", "Zengrong Jia"], "journal": "International journal of general medicine", "year": 2022, "abstract": "Purpose: The inflammatory response was associated with the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to perform a novel prognostic signature based on inflammation-related genes (IRGs) for a better understanding of the prognosis of HNSCC.\nPatients and Methods: IRGs were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed to explore potential pathways. Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation. The nomogram model was constructed based on independent prognostic factors after univariate and multivariate Cox regression. The immune cell infiltration level was analyzed via the Tumor Immune Estimation Resource (TIMER) database.\nResults: In this study, we confirmed that 60% IRGs were abnormally expressed in HNSCC samples, and these were associated with important oncobiology. Then, a prognostic signature comprising 7 hub genes was generated based on TCGA database. The results were validated in 97 patients from GSE41613. A nomogram comprising risk score, age, M stage and N stage was generated to improve the accuracy of prognosis evaluation. The immune cell infiltration analysis suggested that 5 hub genes (ADGRE1, OLR1, TIMP1, GPR132 and CCR7) were negatively correlated with tumor purity and positively correlated with the infiltration of immune cells.\nConclusion: Our study established a novel signature consisting of 7 hub genes for the prognostic prediction in patients with HNSCC.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35592543", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Transcriptomic data and corresponding clinical information of 502 HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/), and a total of 97 samples from GSE41613 cohort were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35592543", "locator": "sections[3] \"Data Collection\" paragraph 2", "quote": "Transcriptomic data and corresponding clinical information of 502 HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/), and a total of 97 samples from GSE41613 cohort were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established a seven-hub-gene inflammation-related gene signature for prognostic prediction in patients with HNSCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35592543", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our study established a novel signature consisting of 7 hub genes for the prognostic prediction in patients with HNSCC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established a seven-hub-gene inflammation-related gene signature for prognostic prediction in patients with HNSCC.", "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:35592543"], "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:GSE41613"], "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:GSE41613"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35592543", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S354349", "sha256": "sha256:b93a54b843f2d8cf4029f6993fb7085ab3a2b7ea3aa0da52b58dbc083f2f9537", "bytes": 39354, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/IJGM.S354349", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9113041", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE41613", "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=GSE41613", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE41613", "version": null, "note": "Transcriptomic data and corresponding clinical information of 502 HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/), and a total of 97 samples from GSE41613 cohort were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Transcriptomic data and corresponding clinical information of 502 HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/), and a total of 97 samples from GSE41613 cohort were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds/).", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM1020099", "GSM1020100", "GSM1020101", "GSM1020102", "GSM1020103", "GSM1020104", "GSM1020105", "GSM1020106", "GSM1020107", "GSM1020108", "GSM1020109", "GSM1020110", "GSM1020111", "GSM1020112", "GSM1020113", "GSM1020114", "GSM1020115", "GSM1020116", "GSM1020117", "GSM1020118", "GSM1020119", "GSM1020120", "GSM1020121", "GSM1020122", "GSM1020123", "GSM1020124", "GSM1020125", "GSM1020126", "GSM1020127", "GSM1020128", "GSM1020129", "GSM1020130", "GSM1020131", "GSM1020132", "GSM1020133", "GSM1020134", "GSM1020135", "GSM1020136", "GSM1020137", "GSM1020138", "GSM1020139", "GSM1020140", "GSM1020141", "GSM1020142", "GSM1020143", "GSM1020144", "GSM1020145", "GSM1020146", "GSM1020147", "GSM1020148", "GSM1020149", "GSM1020150", "GSM1020151", "GSM1020152", "GSM1020153", "GSM1020154", "GSM1020155", "GSM1020156", "GSM1020157", "GSM1020158", "GSM1020159", "GSM1020160", "GSM1020161", "GSM1020162", "GSM1020163", "GSM1020164", "GSM1020165", "GSM1020166", "GSM1020167", "GSM1020168", "GSM1020169", "GSM1020170", "GSM1020171", "GSM1020172", "GSM1020173", "GSM1020174", "GSM1020175", "GSM1020176", "GSM1020177", "GSM1020178", "GSM1020179", "GSM1020180", "GSM1020181", "GSM1020182", "GSM1020183", "GSM1020184", "GSM1020185", "GSM1020186", "GSM1020187", "GSM1020188", "GSM1020189", "GSM1020190", "GSM1020191", "GSM1020192", "GSM1020193", "GSM1020194", "GSM1020195"], "sample_count": 97, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "A 13-gene signature prognostic of HPV-negative OSCC: discovery and external validation", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE41613 not downloaded", "asset:data:geo:GSE41613 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": "Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation."}}], "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": ["GSE41613"], "resolved_accessions": ["GSE41613"], "statements": ["Transcriptomic data and corresponding clinical information of 502 HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/), and a total of 97 samples from GSE41613 cohort were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds/)."], "basis": "The paper names GSE41613 in Gene Expression Omnibus (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:35592543", "data:geo:GSE41613"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE41613"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:46:43Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 12018, "completion_tokens": 1365, "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:35592543", "type": "paper", "label": "pmid:35592543"}, {"id": "source:pmid:35592543", "type": "manuscript", "label": "PMID 35592543 full text"}, {"id": "data:geo:GSE41613", "type": "dataset", "label": "GEO GSE41613"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35592543", "type": "paper", "label": "pmid:35592543"}, {"id": "source:pmid:35592543", "type": "manuscript", "label": "PMID 35592543 full text"}, {"id": "data:geo:GSE41613", "type": "dataset", "label": "GEO GSE41613"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35592543", "type": "extraction", "used": ["source:pmid:35592543"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE41613", "type": "accession_resolution", "used": ["data:geo:GSE41613"], "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:35592543", "parent_hash": "sha256:29ea9bb2e5b41509a576b35cab960670f597f979dc39a1eb1ce5e7003963dd77", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE41613 licence not read", "asset:data:geo:GSE41613 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": ["Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation."], "order_basis": "analysis_only", "id": "assay:1"}]}