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A novel comprehensive immune-related gene signature as a promising survival predictor for the patients with head and neck squamous cell carcinoma.


ABSTRACT: Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. The TNM stage is not satisfactory for individualized prognostic assessment and it does not predict response to therapy. In the present study, we downloaded the gene expression profiles from TCGA database to establish a training set and GEO database for a validation set. In the training set, we developed an 10 immune-related genes signature which had superior predictive value compared with TNM stage. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature might act as an independent factor for predicting the survival of HNSCC patients. As for the immune microenvironment, our results showed higher immune checkpoint expression (CLTA-4 and PD-1) in low-risk group which might reflect a positive immunotherapy response. Thus, our signature not only provided a promising biomarker for survival prediction, but might be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

SUBMITTER: Fang R 

PROVIDER: S-EPMC8109104 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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A novel comprehensive immune-related gene signature as a promising survival predictor for the patients with head and neck squamous cell carcinoma.

Fang Ruihua R   Iqbal Muhammad M   Chen Lin L   Liao Jing J   Liao Jing J   Luo Jierong J   Wei Fanqin F   Wen Weiping W   Sun Wei W  

Aging 20210417 8


Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. The TNM stage is not satisfactory for individualized prognostic assessment and it does not predict response to therapy. In the present study, we downloaded the gene expression profiles from TCGA database to establish a training set and GEO database for a validation set. In the training set, we developed an 10 immune-related genes signature whic  ...[more]

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