Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC
Abstract Background Immunotherapy has introduced new breakthroughs in improving the survival of head and neck squamous cell carcinoma (HNSCC) patients, yet drug resistance remains a critical challenge. Developing personalized treatment strategies based on the molecular heterogeneity of HNSCC is esse...
Saved in:
Main Authors: | Xiaoqin Luo, Chao Li, Gang Qin |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2025-02-01
|
Series: | Hereditas |
Subjects: | |
Online Access: | https://doi.org/10.1186/s41065-025-00380-0 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Disulfidptosis-related gene signatures as prognostic biomarkers and predictors of immunotherapy response in HNSCC
by: Haotian Qin, et al.
Published: (2025-01-01) -
The efficacy and safety of a taxane-based chemotherapy regimen combined with a PD-1 inhibitor in HNSCC: a multicenter real-world study
by: Min Ouyang, et al.
Published: (2025-01-01) -
Expression and prognostic significance of the m6A RNA methylation regulator HNRNPC in HNSCC
by: Yulin Zhang, et al.
Published: (2025-02-01) -
Study of the association of the known prognostic variables with EGFR expression in head and neck squamous cell carcinomas
by: Toyaja Jadhav, et al.
Published: (2023-07-01) -
A dynamic co-expression approach reveals Gins2 as a potential upstream modulator of HNSCC metastasis
by: Nasibeh Khayer, et al.
Published: (2025-01-01)