Dendritic cell-related hub genes in head-and-neck squamous cell carcinoma: implications for prognosis and immunotherapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39145060", "kind": "science-record", "revision": 2, "content_hash": "sha256:a65e45a292ff6eb741e29035a4f8256f8c744fc88509884f98c4b389d9d73773", "created_at": "2026-11-01T20:29:15Z", "updated_at": "2026-11-01T21:33:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Dendritic cell-related hub genes in head-and-neck squamous cell carcinoma: implications for prognosis and immunotherapy.", "doi": "10.21037/tcr-23-2360", "pmid": "39145060", "authors": ["Haiyong Jin", "Lei Zheng", "Jie Wang", "Bo Zheng"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: In the context of head-and-neck squamous cell carcinoma (HNSCC), dendritic cells (DCs) assume pivotal responsibilities, acting as architects of antigen presentation and conductors of immune checkpoint modulation. In this study, we aimed to identify hub genes associated with DCs in HNSCC and explore their prognostic significance and implications for immunotherapy.\nMethods: Integrated clinical datasets from The Cancer Genome Atlas (TCGA)-HNSCC and GSE65858 cohorts underwent meticulous analysis. Employing weighted gene co-expression network analysis (WGCNA), we delineated candidate genes pertinent to DCs. Through the application of random survival forest and least absolute shrinkage and selection operator (LASSO) Cox's regression, we derived key genes of significance. Lisa (epigenetic Landscape In Silico deletion Analysis and the second descendent of MARGE) highlighted transcription factors, with Dual-luciferase assays confirming their regulatory role. Furthermore, immunotherapeutic sensitivity was assessed utilizing the Tumor Immune Dysfunction and Exclusion online tool.\nResults: This study illuminated the functional intricacies of HNSCC DC subsets to tailor innovative therapeutic strategies. We leveraged clinical data from the TCGA-HNSCC and GSE65858 cohorts. We subjected the data to advanced analysis, including WGCNA, which revealed 222 DC-related candidate genes. Following this, a discerning approach utilizing random survival forest analysis and LASSO Cox's regression unveiled seven genes associated with the prognostic impact of DCs, notably ACP2 and CPVL, associated with poor overall survival. Differential gene expression analysis between ACP2 + and ACP2 - DC cells revealed 208 differential expressed genes. Lisa analysis identified the top five significant transcription factors as STAT1, SPI1, SMAD1, CEBPB, and IRF1. The correlation between STAT1 and ACP2 was confirmed through quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Dual-luciferase assays in HEK293T cells. Additionally, TP53 and FAT1 mutations were more common in high-risk DC subgroups. Importantly, the sensitivity to immunotherapy differed among the risk clusters. The low-risk cohorts were anticipated to exhibit favorable responses to immunotherapy, marked by heightened expressions of immune system-related markers. In contrast, the high-risk group displayed augmented proportions of immunosuppressive cells, suggesting a less conducive environment for immunotherapeutic interventions.\nConclusions: Our research may yield a robust DC-based prognostic system for HNSCC; this will aid personalized treatment and improve clinical outcomes as the battle against this challenging cancer continues.", "claims": [{"id": "claim-method", "statement": "We employed random survival forest analysis to identify significantly prognostic genes using the “randomForestSRC” and “randomSurvivalForest” packages of R software (24). The LASSO Cox’s regression method was utilized to construct a prognostic model for hub genes associated with DCs, employing the “glmnet” package within the R software (25).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39145060", "locator": "sections[6] \"Random survival forest analysis, least absolute shrinkage and selection operator (LASSO) Cox’s regression model, and survival analysis\" paragraph 2", "quote": "We employed random survival forest analysis to identify significantly prognostic genes using the “randomForestSRC” and “randomSurvivalForest” packages of R software (24). The LASSO Cox’s regression method was utilized to construct a prognostic model for hub genes associated with DCs, employing the “glmnet” package within the R software (25)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In January 2023, we used the Genomic Data Commons (GDC) portal to acquire clinical and gene expression data from a The Cancer Genome Atlas (TCGA)-HNSCC cohort comprising 500 individuals.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39145060", "locator": "sections[4] \"Acquisition of clinical and gene expression information\" paragraph 2", "quote": "In January 2023, we used the Genomic Data Commons (GDC) portal to acquire clinical and gene expression data from a The Cancer Genome Atlas (TCGA)-HNSCC cohort comprising 500 individuals."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Following this, a discerning approach utilizing random survival forest analysis and LASSO Cox’s regression unveiled seven genes associated with the prognostic impact of DCs, notably ACP2 and CPVL, associated with poor overall survival.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39145060", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Following this, a discerning approach utilizing random survival forest analysis and LASSO Cox’s regression unveiled seven genes associated with the prognostic impact of DCs, notably ACP2 and CPVL, associated with poor overall survival."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Following this, a discerning approach utilizing random survival forest analysis and LASSO Cox’s regression unveiled seven genes associated with the prognostic impact of DCs, notably ACP2 and CPVL, associated with poor overall survival.", "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:39145060"], "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:GSE150430"], "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:GSE150430"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39145060", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-2360", "sha256": 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