Aging and head and neck cancer insights from single cell and spatial transcriptomic analyses.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39692961", "kind": "science-record", "revision": 4, "content_hash": "sha256:425539dea41f5e55cdadd6721a04b0e145d6fe05cc92e8bd2776645fce459993", "created_at": "2026-11-01T20:29:59Z", "updated_at": "2026-11-01T22:55:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Aging and head and neck cancer insights from single cell and spatial transcriptomic analyses.", "doi": "10.1007/s12672-024-01672-z", "pmid": "39692961", "authors": ["Yi Pei", "Zhuying Mou", "Lai Jiang", "Jinyan Yang", "Yuheng Gu", "Jie Min", "Lingyi Sunzhang", "Nan Xiong", "Xiang Xu", "Hao Chi", "Ke Xu", "Sinian Liu", "Huiyan Luo"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND: Head and neck squamous cell carcinoma(HNSCC) is the sixth most common malignancy worldwide, with more than 890,000 new cases and 450,000 deaths annually. Its major risk factors include smoking, alcohol abuse, aging, and poor oral hygiene. Due to the lack of early and effective detection and screening methods, many patients are diagnosed at advanced stages with a five-year survival rate of less than 50%. In this study, we deeply explored the expression of Aging-related genes(ARGs) in HNSCC and analyzed their prognostic significance using single-cell sequencing and spatial transcriptomics analysis. This research aims to provide new theoretical support and directions for personalized treatment. Annually, more than 890,000 new cases of head and neck squamous cell carcinoma (HNSCC) are diagnosed globally, leading to 450,000 deaths, making it the sixth most common malignancy worldwide. The primary risk factors for HNSCC include smoking, alcohol abuse, aging, and poor oral hygiene. Many patients are diagnosed at advanced stages due to the absence of early and effective detection and screening methods, resulting in a five-year survival rate of less than 50%. In this research, single cell sequencing and spatial transcriptome analysis were used to investigate the expression of Aging-related genes (ARGs) in HNSCC and to analyse their prognostic significance. This research aims to provide new theoretical support and directions for personalized treatment.\nMETHODS: In this study, we investigated the association between HNSCC and AGRs by utilizing the GSE139324 series in the GEO database alongside the TCGA database, combined with single-cell sequencing and spatial transcriptomics analysis. The data were analyzed using Seurat and tSNE tools to reveal intercellular communication networks. For the spatial transcriptome data, SCTransform and RunPCA were applied to examine the metabolic activities of the cells. Gene expression differences were determined through spacerxr and RCTD tools, while the limma package was employed to identify differentially expressed genes and to predict recurrence rates using Cox regression analysis and column line plots. These findings underscore the potential importance of molecular classification, prognostic assessment, and personalized treatment of HNSCC.\nRESULTS: This study utilized HNSCC single-cell sequencing data to highlight the significance of ARGs in the onset and prognosis of HNSCC. It revealed that the proportion of monocytes and macrophages increased, while the proportion of B cells decreased. Notably, high expression of the APOE gene in monocytes was closely associated with patient prognosis. Additionally, a Cox regression model was developed based on GSTP1 and age to provide personalized prediction tools for clinical use in predicting patient survival.\nCONCLUSIONS: We utilized single-cell sequencing and spatial transcriptomics to explore the cellular characteristics of HNSCC and its interaction with the tumor microenvironment. Our findings reveal that HNSCC tissues show increased mononuclear cells and demonstrate enhanced activity in ARGs, thereby advancing our understanding of HNSCC development mechanisms.", "claims": [{"id": "claim-method", "statement": "Using the R package “limma” [38, 39], specifically designed for microarray data, we identified significantly differentially expressed genes between HNSCC and normal samples (adjusted P < 0.05 and logFC > 1). Subsequently, we conducted univariate and multivariate Cox regression analyses on these genes, and presented the results using forest plots generated by the “forestplot” package [40, 41].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39692961", "locator": "sections[7] \"HNSCC monocyte senescence-related key genes combined with bulk data for prognostic and immunotherapy analysis\" paragraph 2", "quote": "Using the R package “limma” [38, 39], specifically designed for microarray data, we identified significantly differentially expressed genes between HNSCC and normal samples (adjusted P < 0.05 and logFC > 1). Subsequently, we conducted univariate and multivariate Cox regression analyses on these genes, and presented the results using forest plots generated by the “forestplot” package [40, 41]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets utilized in the present investigation are accessible via the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). Moreover, the raw data files for the study can be obtained through the following link: https://www.jianguoyun.com/p/Dea3ZZ8Qn6zvDBjtv9QFIAA and are available for download.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39692961", "locator": "sections[26] \"Data availability\" paragraph 2", "quote": "The datasets utilized in the present investigation are accessible via the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). Moreover, the raw data files for the study can be obtained through the following link: https://www.jianguoyun.com/p/Dea3ZZ8Qn6zvDBjtv9QFIAA and are available for download."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:39692961"], "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:GSE139324"], "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:GSE139324"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39692961", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01672-z", "sha256": "sha256:23888f6ec671d3edd271d7ce73c1c21fc8a99b164c299c1cabe8ac85bfcfb24c", "bytes": 69016, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-024-01672-z", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11655923", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE139324", "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=GSE139324", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE139324", "version": null, "note": "The datasets utilized in the present investigation are accessible via the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). Moreover, the raw data files for the study can be obtained through the following link: https://www.jianguoyun.com/p/Dea3ZZ8Qn6zvDBjtv9QFIAA and are available for download.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets utilized in the present investigation are accessible via the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). 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To increase the depth and breadth of single-cell RNA sequencing data analysis, we employed five different sc", "ssociated genes. Subsequently, we integrated single-cell RNA sequencing data with spatial location information using"], "order_basis": "paper_text_order:4106", "id": "assay:1"}, {"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": ["Using the R package “limma” [38, 39], specifically designed for microarray data, we identified significantly differentially expressed genes between HNSCC and normal samples (adjusted P < 0.05 and logFC > 1). 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