Characterization of the immune cell function landscape in head and neck squamous carcinoma to assist in prognosis prediction and immunotherapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37955651", "kind": "science-record", "revision": 3, "content_hash": "sha256:0a8b251dfa3f7173131493ad919352173fcd738d67a82b9c7a4312b51587a278", "created_at": "2026-11-01T19:02:51Z", "updated_at": "2026-11-01T22:10:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of the immune cell function landscape in head and neck squamous carcinoma to assist in prognosis prediction and immunotherapy.", "doi": "10.18632/aging.205201", "pmid": "37955651", "authors": ["Wenlun Wang", "Zhouyi Zhang", "Wenming Li", "Dongmin Wei", "Jianing Xu", "Ye Qian", "Shengda Cao", "Dapeng Lei"], "journal": "Aging", "year": 2023, "abstract": "BACKGROUND: The malignant characteristics of cancer depend not only on intrinsic properties of cancer cells but also on the functions of infiltrating immune cells. In this study, we aimed to investigate the functional landscape of immune cells in head and neck squamous cell carcinoma (HNSCC).\nMETHODS: We employed single-sample gene set enrichment analysis to examine the immunophenotypes of HNSCC based on 29 immune cell functions (ICFs) in TCGA and GSE65858 datasets. We analyzed the clinical features, immune microenvironment, molecular profiles, and biological processes. Additionally, we developed and validated an ICF-based risk score for personalized prognosis prediction. We confirmed the value of the ICF score in our cohort using qRT-PCR and immunohistochemistry. Molecular docking was used to predict potential compounds for immunotherapy.\nRESULTS: Three immunophenotypes (Immune-L, Immune-M, and Immune-H) were identified in 769 HNSCC samples. The characteristics of Immune-H were consistent with a \"Hot\" tumor, Immune-L was similar to a \"Cold\" tumor, and Immune-M exhibited intermediate features. The ICF risk score was associated with immune checkpoints, infiltrating immune cells, tumor mutation burden, and sensitivities to targeted/chemotherapeutic agents. Gene set variation analysis implicated the involvement of metabolic reprogramming pathways in the high-risk group. The combination of \"Tumor Immune Dysfunction and Exclusion\" and \"Immunophenoscore\" algorithms indicated that the low-risk group had a higher likelihood of benefiting from immunotherapy. Finally, we identified Eltrombopag and other compounds that may be beneficial for HNSCC immunotherapy.\nCONCLUSION: Our study provides a novel perspective on the tumor microenvironment of HNSCC, aiding in the understanding of HNSCC heterogeneity and the development of personalized/precision medicine.", "claims": [{"id": "claim-method", "statement": "To estimate the enrichment scores of these 29 gene signatures in HNSCC samples, we utilized ssGSEA (single sample gene set enrichment analysis).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37955651", "locator": "sections[4] \"Identification of the immunophenotype based on immune cell function (ICF)\" paragraph 2", "quote": "To estimate the enrichment scores of these 29 gene signatures in HNSCC samples, we utilized ssGSEA (single sample gene set enrichment analysis)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded transcriptome data (FPKM) and clinical information of 499 HNSCC patients from The Cancer Genome Atlas (TCGA) database. The sequencing platform was Illumina HiSeq. GRCh38 was used for gene annotation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37955651", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "We downloaded transcriptome data (FPKM) and clinical information of 499 HNSCC patients from The Cancer Genome Atlas (TCGA) database. The sequencing platform was Illumina HiSeq. GRCh38 was used for gene annotation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Three immunophenotypes (Immune-L, Immune-M, and Immune-H) were identified in 769 HNSCC samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37955651", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Three immunophenotypes (Immune-L, Immune-M, and Immune-H) were identified in 769 HNSCC samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Three immunophenotypes (Immune-L, Immune-M, and Immune-H) were identified in 769 HNSCC samples.", "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:37955651"], "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": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37955651", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205201", "sha256": "sha256:4b325bb577f98e93507ff2483e9e340e7e4ba8ba73f78251bfb90ec6bf18dc81", "bytes": 35407, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.18632/aging.205201", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10683602", "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": "We downloaded transcriptome data (FPKM) and clinical information of 499 HNSCC patients from The Cancer Genome Atlas (TCGA) database. 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