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

Abstract

Background 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). Method 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. Results 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 < 0.05). Top 10 important genes were then screened out by random forest. Using multiple combinatorial screening, a combination with less genes and more significant P value was used to construct the prognostic gene signature (RNF144A, STC1, P4HA1, FMNL3, ANO1, BASP1, MME, PLEKHG2 and DKK1). Kaplan–Meier analysis showed that patients with higher risk scores had worse overall survival (p < 0.001). The ROC curve showed that the risk score had a good predictive efficiency (AUC > 0.66). Subsequently, the predictive ability of the risk score was verified in the validation sets. Moreover, the two-factor survival analysis combining the cancer hallmarks and risk scores suggested that HNSCC patients with the high hypoxia or glycolysis & high risk-score showed the worst prognosis. Besides, a nomogram based on the nine-gene signature was established for clinical practice. Furthermore, the risk score was significantly related to tumor immune infiltration profiles and genome changes. Conclusion This nine-gene signature associated with glycolysis and hypoxia can not only be used for prognosis prediction and risk stratification, but also may be a potential therapeutic target for patients with HNSCC.

Clinical and prognostic significance analysis of glycolysis‐related genes in HNSCC

“…Among all the factors, the risk score model in this study has the best predictive ability for both the whole HNSCC cohort and subgroup cohorts. We noted that several pilot reports have identified prognostic models in HNSCC, 35,36 even using glycolysis genes 37,38 . Our work deepens current understanding of the gene set‐based predictions, and provide a more comprehensive and accurate model for the prognostic estimation.…”

“…We noted that several pilot reports have identified prognostic models in HNSCC, 35,36 even using glycolysis genes. 37,38 Our work deepens current understanding of the gene set-based predictions, and provide a more comprehensive and accurate model for the prognostic estimation.…”

BASP1 is a prognostic biomarker associated with immunotherapeutic response in head and neck squamous cell carcinoma

“…In breast cancer, BASP1 could enhance the anticancer effects of tamoxifen treatment, and patients with high BASP1 were associated with better prognosis (42). Besides, BASP1 upregulation was considered as a high-risk factor in lung adenocarcinoma, cervical cancer, as well as in HNSCC (38,43). Nevertheless, the biological roles and mechanism of BASP1 in regulating HNSCC progression remain unclear.…”

Review of biomarkers for response to immunotherapy in HNSCC microenvironment

“…Hypoxia and acidification of TME are both associated with adverse events and survival after chemoradiotherapy ( 18 ), and animal models have shown that hypoxia is also an essential factor for immune checkpoint inhibitor resistance ( 19 ). According to GEO database analysis, hypoxia and glycolysis are the main risk factors of OS in HNSCC ( 20 ). Multiple studies indicated that hypoxia is one of the drivers of immune suppression ( 18 , 21 ), which can suppress anti-tumor immunity and assist tumors in evading immune surveillance.…”