Transcriptome analysis reveals the prognostic and immune infiltration characteristics of glycolysis and hypoxia in head and neck squamous cell carcinoma

This study aims to construct a new prognostic gene signature in survival prediction and risk stratification for patients with Head and neck squamous cell carcinoma (HNSCC). The transcriptome profiling data and hallmark gene sets in the Molecular Signatures Database was used to explore the cancer hal...

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Bibliographic Details
Published in:BMC cancer 2022-03, Vol.22 (1), p.352-352, Article 352
Main Authors: Liu, Jun, Lu, Jianjun, Li, Wenli
Format: Article
Language:English
Subjects:
Accuracy
Biomarkers
Cancer therapies
Care and treatment
Chemotherapy
Data processing
Datasets
Diagnosis
Dkk1 protein
Gene expression
Gene Expression Profiling
Gene signature
Glucose metabolism
Glycolysis
Glycolysis - genetics
Head & neck cancer
Head and neck cancer
Head and neck carcinoma
Head and Neck Neoplasms - genetics
Head and neck squamous cell carcinoma
Health aspects
Human papillomavirus
Humans
Hypoxia
Infiltration
Larynx
Medical prognosis
Metabolism
Metabolites
Metastases
Nomograms
Observations
Overall survival
Patients
Prevention
Prognosis
Radiation therapy
Risk factors
Squamous cell carcinoma
Squamous Cell Carcinoma of Head and Neck - genetics
Survival analysis
Therapeutic targets
Transcriptomes
Tumors
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Summary:This study aims to construct a new prognostic gene signature in survival prediction and risk stratification for patients with Head and neck squamous cell carcinoma (HNSCC). The transcriptome profiling data and hallmark gene sets in the Molecular Signatures Database was used to explore the cancer hallmarks most relevant to the prognosis of HNSCC patients. Differential gene expression analysis, weighted gene co-expression network analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct the prognostic gene signature. The predictive ability of gene signature was verified in the TCGA HNSCC cohort as the training set and the GEO HNSCC cohorts (GSE41613 and GSE42743) as the validation sets, respectively. Moreover, the correlations between risk scores and immune infiltration patterns, as well as risk scores and genomic changes were explored. A total of 3391 differentially expressed genes in HNSCC were screened. Glycolysis and hypoxia were screened as the main risk factors for OS in HNSCC. Using univariate Cox analysis, 97 prognostic candidates were identified (P 
ISSN:1471-2407
1471-2407
DOI:10.1186/s12885-022-09449-9