Evidence map›Paper›PMID 40192963›Full record

ArticleDiscover oncology2025

A novel molecular classification system for head and neck squamous cell carcinoma: predicting treatment response and metastatic potential through multi-omics analysis.

XinYu Liu, YuJun Liu, XuTengYue Tian, Yue Xi, MiaoMiao Lu, Xin Zou, WanTao Chen

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In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

XinYu Liu *College of Stomatology, Binzhou Medical University, Yantai, 264003, Shandong, China.
YuJun Liu *Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
XuTengYue TianDepartment of Oral and Maxillofacial & Head and Neck Oncology, Beijing Stomatological Hospital, Capital Medical University, Beijing, China.
Yue XiDepartment of Obstetrics and Gynecology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
MiaoMiao LuCollege of Stomatology, Binzhou Medical University, Yantai, 264003, Shandong, China.
Xin ZouDigital Diagnosis and Treatment Innovation Center for Cancer, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China. xzou@fudan.edu.cn.
WanTao ChenDepartment of Oral and Maxillofacial-Head and Neck Oncology, School of Medicine, Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, PR China. chenwantao196323@sjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHead and neck squamous cell carcinoma (HNSCC) demonstrates significant heterogeneity, necessitating improved molecular classification for precision treatment.

methodsWe integrated single-cell and bulk RNA sequencing data from 59,376 cells across ten datasets using Scissor and scSTAR packages. Molecular subtyping was performed through ssGSEA and WGCNA analysis, with immune infiltration evaluated using CIBERSORT. We developed a machine learning-based risk prediction model using 54 algorithms.

resultsWe identified three molecular subtypes with distinct prognostic implications, showing significant survival differences across independent datasets (TCGA-HNSCC, P < 0.0001; GSE65858, P = 0.018). The C3 subtype showed enhanced immunotherapy response potential, while C2 exhibited the highest genomic alteration rate (97.06%) and TP53 mutations (80%). Macrophages emerged as key players in intercellular communication networks. Our risk prediction model demonstrated robust performance across four validation cohorts.

conclusionThis molecular subtyping framework provides valuable insights for patient stratification and personalized therapeutic strategies in HNSCC, potentially improving clinical outcomes through precise treatment selection.

Indexed as

ChemotherapyHead and neck squamous cell carcinomaImmunotherapy strategiesMachine learningMolecular subtypesPan-cancerRNA sequencingTumor mutation burden

Identifiers

PMID40192963
PMCPMC11977092

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