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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

The Potential of Oxidative Stress Related Genes as Prognostic Biomarkers in Head and Neck Squamous Cell Carcinoma

Author(s): Xin Wang and Gang Zhou*

Volume 25, Issue 11, 2022

Published on: 14 February, 2022

Page: [1952 - 1965] Pages: 14

DOI: 10.2174/1386207325666211207154436

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Abstract

Background: The occurrence of oxidative stress is an important hallmark of tumorigenesis and the development of cancers, including head and neck squamous cell carcinoma (HNSCC). The purpose of this study was to identify a robust oxidative stress-related prognostic model in HNSCC.

Methods: Oxidative stress genes related to the prognosis of HNSCC were identified through multiple bioinformatics methods.

Results: The expression profile of differential genes related to oxidative stress and functional enrichment analysis were obtained from the HNSCC cohort of The Cancer Genome Atlas (TCGAHNSC). Then, the HNSCC prognostic risk model was constructed of thirteen screened genes through univariate Cox analysis, the least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox analysis. Kaplan–Meier curve indicated that the low-risk group had a better survival outcome than the high-risk group. The clinical utility of the risk model was validated in the GSE41613 dataset. The risk score was an independent prognostic indicator in the training and validation sets. In addition, the risk score was in a positive correlation with tumor stage, lymph node infiltration, and the status of the primary site. Gene set enrichment analysis (GSEA) illustrated that many biological processes associated with immunity were significantly enriched in the low-risk group of the training cohort.

Conclusion: The oxidative stress-related risk signature was a promising predictor for the prognosis of HNSCC patients, which might assist in making individualized therapy programs.

Keywords: Head and neck squamous cell carcinoma, oxidative stress, prognostic biomarkers, overall survival, risk model, bioinformatics.

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