ArticleApplied biochemistry and biotechnology2025
E2F1 Promotes the Occurrence of Head and Neck Squamous Cell Carcinoma and Serves as a Prognostic Biomarker.
Article in Applied biochemistry and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Cancer in transition: discovery of tumor-intrinsic transcriptional programs shaping the immune and microenvironmental landscape.Biomarker research · 2026Review
- E2F expression profiling-based subtypes in head and neck squamous cell carcinoma: clinical relevance, prognostic implications, and personalized therapy.World journal of surgical oncology · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
Head and neck squamous cell carcinoma (HNSCC) is a common malignant tumor occurring in various sites such as the oral cavity, pharynx, larynx, and nasal cavity. This study aimed to explore the biological functions and prognostic value of E2F transcription factor 1 (E2F1) in HNSCC. Transcriptome and single-cell sequencing (scRNA-seq) data of HNSCC patients were analyzed using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). All samples were divided into high and low expression groups based on the expression levels of E2F1. A risk model was constructed based on Lasso-Cox regression, and the differences between the two groups in terms of prognosis were explored. The scRNA-seq data of HNSCC samples were analyzed using the Seurat package to identify cell types. AUCell was used to score different types of cells, and subsequently, the interaction pathways between the high-scoring cell population and other cell populations were explored using the CellChat package. The expression level of E2F1 in tumor tissues was higher than that in normal tissues, which was confirmed by in vitro experiments. Analysis of transcriptome data from TCGA revealed significant differences in overall survival (OS) between the high and low expression groups. Prognostic genes were selected based on DEGs between the two groups, and a risk model was constructed. Subsequently, a nomogram model was constructed based on clinical factors and risk scores, which exhibited good predictive performance. The expression landscape of prognostic genes in different cell types was explored using scRNA-seq data of HNSCC samples. Dendritic cell populations were identified as high-scoring cell populations, and the pathways of interaction between this cell population and other cell populations were explored. We identified E2F1 as an independent prognostic factor closely associated with the prognosis and immune response of HNSCC.
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