Six Glycolysis-Related Genes as Prognostic Risk Markers Can Predict the Prognosis of Patients with Head and Neck Squamous Cell Carcinoma

LangXiong Chen; XiaoSong He; ShiJiang Yi; GuanCheng Liu; Yi Liu; YueFu Ling

doi:10.1155/2021/8824195

Six Glycolysis-Related Genes as Prognostic Risk Markers Can Predict the Prognosis of Patients with Head and Neck Squamous Cell Carcinoma

Biomed Res Int. 2021 Feb 10:2021:8824195. doi: 10.1155/2021/8824195. eCollection 2021.

Authors

LangXiong Chen^{1

2}, XiaoSong He^{1

2}, ShiJiang Yi^{1

2}, GuanCheng Liu^{1

2}, Yi Liu^{1

2}, YueFu Ling^{1

2}

Affiliations

¹ Otolaryngology & Head and Neck Surgery, The Affiliated Hospital of Guilin Medical University, Guilin, Guangxi Zhuang Autonomous Region, China.
² The Guilin Medical University, Guilin, Guangxi Zhuang Autonomous Region, China.

Abstract

Objective: Head and neck squamous cell carcinoma (HNSCC) is one of the worst-prognosis malignant tumors. This study used bioinformatic analysis of the transcriptome sequencing data of HNSCC and the patients' survival and clinical data to construct a prediction signature of glycolysis-related genes as the prognostic risk markers.

Methods: Gene expression profile data about HNSCC tissues (n = 498) and normal tissues in the head and neck (n = 44) were got from The Cancer Genome Atlas (TCGA), as well as patients' survival and clinical data. Then, we obtained core genes; their expression in head and neck squamous cell carcinoma tissues is significantly different from that in normal head and neck tissues. The predicted glycolysis-related genes are screened through univariate Cox regression analysis, and then, the prognostic risk markers were constructed through further correction of multivariate Cox regression analysis. The Kaplan-Meier curve and receiver operating characteristic curve are used to analyze the potential value of these risk markers in diagnosis and prognosis. We also evaluated that the glycolysis-related prognostic risk markers composed of 6 oncogenes are correlated with clinical features, such as age, gender, grade, and clinical stage of the tumor, by univariate and multivariate Cox regression analyses.

Results: Differentially expressed glycolytic genes in HNSCC tissues and normal head and neck tissues were screened from TCGA databases using the bioinformatic method. We confirmed a set of six glycolytic genes that were significantly associated with OS in the test series. According to our analysis, the prognostic risk markers composed of HPRT1, STC2, PLCB3, GPR87, PYGL, and SLC5A12 may be an independent risk factor for the prognosis of HNSCC.

Conclusions: Through this analysis, we constructed new prognostic risk markers related to glycolysis as a prognostic risk marker for patients with HNSCC and provided new ideas and molecular targets for the research and individualized treatment of HNSCC.

MeSH terms

Biomarkers, Tumor* / biosynthesis
Biomarkers, Tumor* / genetics
Databases, Nucleic Acid*
Disease-Free Survival
Female
Gene Expression Regulation, Neoplastic*
Genes, Neoplasm*
Glycolysis*
Head and Neck Neoplasms* / genetics
Head and Neck Neoplasms* / metabolism
Head and Neck Neoplasms* / mortality
Head and Neck Neoplasms* / pathology
Humans
Male
Middle Aged
Neoplasm Proteins* / biosynthesis
Neoplasm Proteins* / genetics
Squamous Cell Carcinoma of Head and Neck* / genetics
Squamous Cell Carcinoma of Head and Neck* / metabolism
Squamous Cell Carcinoma of Head and Neck* / mortality
Squamous Cell Carcinoma of Head and Neck* / pathology
Survival Rate

Substances

Biomarkers, Tumor
Neoplasm Proteins