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. 2022 Jul 7:2022:2762595.
doi: 10.1155/2022/2762595. eCollection 2022.

Identification of Prognostic Markers for Head and NeckSquamous Cell Carcinoma Based on Glycolysis-Related Genes

Xiaobin Ren  1 , Lin Jing Song  2 , Mingzhu Zhang  1 , Wanli Zhang  1 , Ken Lin  3 , Hongbing He  1
Affiliations

Identification of Prognostic Markers for Head and NeckSquamous Cell Carcinoma Based on Glycolysis-Related Genes

Xiaobin Ren et al. Evid Based Complement Alternat Med. .

Abstract

Head and neck squamous cell carcinomas (HNSCCs) comprise a heterogeneous group of tumors. Many patients respond differently to treatment and prognosis due to molecular heterogeneity. There is an urgent need to identify novel biomarkers to predict the prognosis of patients with HNSCC. Glycolysis has an important influence on the progress of HNSCC. Therefore, we investigated the prognostic significance of glycolysis-related genes in HNSCC. Our results showed that ELF3, AURKA, and ADH7 of 20 glycolysis-related DEGs were significantly related to survival and were used to construct the risk signature. The risk score showed high accuracy in distinguishing the overall survival (OS) of HNSCC. The Kaplan-Meier curves demonstrated that the risk score was associated with an unfavorable prognosis in patients with female sex, male sex, grade 3, T1/2 stage, N+ stage, N2 stage, M0 stage, and clinical stage III/IV. Independent prognostic analysis showed that clinical stage and risk score were strongly associated with OS. Moreover, the risk score had higher accuracy in predicting 1-, 3-, and 5-year survival. AURKA and ADH7 were only significantly related to M1 macrophages and neutrophils, respectively, while ELF3 was significantly correlated with M2 macrophages and monocytes (all p < 0.05).The ceRNA network demonstrated that miR-335-5p and miR-9-5p may play core roles in the regulation of these three genes in HNSCC. The risk score constructed based on three glycolysis-related genes showed high accuracy in predicting the prognosis and clinicopathological characteristics of HNSCC.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
(a) Volcano plot showing the distribution of DEGs. (b) Heatmap showing the top 50 DEGs. (c) Venn diagram of DEGs common to the two GEO datasets. DEGs: differentially expressed genes; GEO: Gene Expression Omnibus.
Figure 2
Figure 2
(a) Relationships between risk score and the number of deaths. (b) Overall survival of high-and low-risk patients. (c) ROC curve analysis of the predictive efficiency of the risk signature. (d) Heatmap showing AURKA, ADH7, and ELF3 expression.
Figure 3
Figure 3
Validation set evaluating the risk score system for the three genes on the testing set. (a) Relationships between the risk score and the number of deaths. (b) Overall survival of high- and low-risk patients. (c) ROC curve analysis of the predictive efficiency of the risk signature. (d) Heatmap showing AURKA, ADH7, and ELF3 expression.
Figure 4
Figure 4
Validation set evaluating the risk score system for the three genes on the external validation set. (a) Relationships between the risk score and the number of deaths. (b) Overall survival of high- and. low-risk patients. (c) ROC curve analysis of the predictive efficiency of the risk signature. (d) Heatmap showing AURKA, ADH7, and ELF3 expression.
Figure 5
Figure 5
Kaplan–Meier curves to investigate the correlation of the risk score and patients' overall survival according to different patient clinicopathological characteristics.
Figure 6
Figure 6
(a) The univariate analysis of the correlation on the clinical stage, risk score, and prognosis. (b) The multivariate analysis of the correlation on the clinical stage, risk score, and OS based on the abovementioned significant factors. (c) The OS of patients predicted by the nomogram model. (d) The application of calibration curves in the nomogram model.
Figure 7
Figure 7
(a) CIBERSORT algorithm analyses of the relationship of the genes in the risk signature and 21 immune cells. (b) Correlation analysis of AURKA, ADH7, and ELF3 and differential immune cells. CIBERSORT: cell-type identification by estimating relative subsets of RNA transcripts.
Figure 8
Figure 8
Construction of a ceRNA network based on the genes in the risk signature, ceRNA, and competitive endogenous RNA.
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