Single‐Cell Transcriptomics Identifies Novel Prognostic Signatures in <scp>HNSCC</scp> Immunotherapy Response

Head and neck squamous cell carcinoma (HNSCC) poses a major therapeutic challenge. In this study, we aimed to analyze tumor immune microenvironment changes and develop a prognostic model based on immunotherapy response. We analyzed single-cell RNA sequencing data from three HNSCC patients receiving TLR8 agonist and anti-PD1 combination therapy, identified cell subpopulations before and after treatment with a focus on six major immune cell types, and developed a LASSO-Cox risk stratification model using combined single-cell and bulk RNA sequencing data. We identified 19 pre-treatment and 13 post-treatment cell subpopulations. Analysis of six major immune cell types revealed differential gene expression patterns. Based on treatment-induced differential genes, we developed a LASSO-Cox model with 51 survival-associated genes, which showed robust predictive performance (AUC: 0.749-0.800) across different timepoints for both HPV-positive and HPV-negative patients. High-risk groups had elevated MDSCs and CAFs, decreased immune cell infiltration (except Th2 CD4+ T cells and common lymphoid progenitors), and increased expression of ICB-related genes. In conclusion, our model effectively captures patients' immune status and provides insights for optimizing HNSCC immunotherapy strategies.

Authors

Institutions

Publication Details

Journal
Cancer Science
Published
2025-08-12
DOI
https://doi.org/10.1111/cas.70171
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Single‐Cell Transcriptomics Identifies Novel Prognostic Signatures in HNSCC Immunotherapy Response

Qianying Wu, Kankui Wu, Bin Liu, Fei Peng et al.
1 links
Cancer Science
Single-cell and spatial transcriptomics
article

Single‐Cell Transcriptomics Identifies Novel Prognostic Signatures in HNSCC Immunotherapy Response

Qianying Wu, Kankui Wu, Bin Liu, Fei Peng, Xiuzhen Chen, Jing Meng, Mingyu Liu, Na Zhang, Yinhong Li
article en

Abstract

Head and neck squamous cell carcinoma (HNSCC) poses a major therapeutic challenge. In this study, we aimed to analyze tumor immune microenvironment changes and develop a prognostic model based on immunotherapy response. We analyzed single-cell RNA sequencing data from three HNSCC patients receiving TLR8 agonist and anti-PD1 combination therapy, identified cell subpopulations before and after treatment with a focus on six major immune cell types, and developed a LASSO-Cox risk stratification model using combined single-cell and bulk RNA sequencing data. We identified 19 pre-treatment and 13 post-treatment cell subpopulations. Analysis of six major immune cell types revealed differential gene expression patterns. Based on treatment-induced differential genes, we developed a LASSO-Cox model with 51 survival-associated genes, which showed robust predictive performance (AUC: 0.749-0.800) across different timepoints for both HPV-positive and HPV-negative patients. High-risk groups had elevated MDSCs and CAFs, decreased immune cell infiltration (except Th2 CD4+ T cells and common lymphoid progenitors), and increased expression of ICB-related genes. In conclusion, our model effectively captures patients' immune status and provides insights for optimizing HNSCC immunotherapy strategies.

Cancer ScienceVol. 116(11)
Sun Yat-sen University (CN), Second Affiliated Hospital of Guangzhou Medical University (CN), The First People's Hospital of Shunde (CN), Foshan Hospital of TCM (CN), Third Affiliated Hospital of Sun Yat-sen University (CN), First Affiliated Hospital of Jinan University (CN), Guangzhou Medical University (CN)
National Natural Science Foundation of China, Jinan University
Good health and well-being
Openalex Percentile: Top 82%
Single-cell and spatial transcriptomics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.