ReviewCancers2024
Unravelling the Complexity of HNSCC Using Single-Cell Transcriptomics.
Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Single-cell landscape of peripheral and tumor-infiltrating immune cells in HPV-negative HNSCC.Oncoimmunology · 2026Article
- A unified single-cell atlas of HNSCC: Toward characterizing HPV- and sex-associated TME variability.iScience · 2026Article
- Single-cell and Mendelian analyses reveal shared mechanisms between head and neck neoplasms and aging.Discover oncology · 2026Article
- CELLetter: leveraging large language model and dual-stream network to identify context-specific ligand-receptor interactions for cell-cell communication analysis.Briefings in bioinformatics · 2025Article
- spVelo: RNA velocity inference for multi-batch spatial transcriptomics data.Genome biology · 2025Article
- Cancer stem cells and tumor-associated macrophages as mates in tumor progression: mechanisms of crosstalk and advanced bioinformatic tools to dissect their phenotypes and interaction.Frontiers in immunology · 2025Review
- Prognostic modeling in head and neck cancer: deep learning or handcrafted radiomics?BJR artificial intelligence · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
BACKGROUND/
objectivesHead and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and the most common form of head and neck cancer, posing significant challenges for disease management. The objective of this review is to assess the utility of single-cell RNA sequencing (scRNAseq) in addressing these challenges by enabling a detailed characterization of the tumor microenvironment (TME) at the cellular level.
methodsThis review compiles and analyzes current strategies that utilize scRNAseq and other single-cell technologies in HNSCC research.
resultsFor HNSCC etiology, scRNAseq allows for the construction of cellular atlases, characterization of different cell types, and investigation of genes and processes involved in cancer initiation, development, and progression within the TME. In terms of HNSCC diagnosis and prognosis, the resolution offered by scRNAseq enables the identification of cell type-specific signatures, enhancing prognostic models and disease stratifiers for patient outcome assessments. Regarding HNSCC treatment, scRNAseq provides insights into cellular responses to various treatments, including radiotherapy, chemotherapy, and immunotherapy, contributing to a better understanding of treatment efficacy and patient outcomes.
conclusionsThis review highlights the contributions of scRNAseq to HNSCC research, addressing its cellular and biological complexity, and emphasizes its potential for advancing research and clinical practice in other cancer types.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.