Establishment and Analysis of Hypoxia-Related Model in Oral Squamous Cell Carcinoma

Abstract

Oral squamous cell carcinoma (OSCC) is one of the most common cancers in the world. Hypoxia is closely related to immunity in tumor microenvironment and also affects the prognosis of patients. However, there is still a lack of articles related to tumor hypoxia in oral squamous cell carcinoma. Therefore, we aimed to develop a hypoxia model for future application in patient prognosis analysis and immunotherapy. The transcriptome and survival information of OSCC were downloaded from GEO database. The Cox regression model of the lasso method was used to identify prognostic genes and develop gene characteristics based on hypoxia immunity. According to the median risk value, the patients were divided into high-risk group and low-risk group. Then, the estimated algorithm was used to estimate the relationship between hypoxia and immune status. At the same time, we evaluated the correlation and expression differences of immune-related genes between different risk groups. By using the lasso model, we identified two genes, including PFKP and SERPINE1, to construct gene signatures for risk stratification. We observed that both genes were highly expressed in the high-risk group, which was not conducive to the prognosis of the tumor. In addition, in the analysis of the degree of immune infiltration, we observed that there were differences in the content of a variety of immune cells between the two groups. It can be seen that there were great differences in the immune cells constituting the tumor microenvironment in oral squamous cell carcinoma. There remain significant differences in the expression levels of multitudinous immune-related genes. These immune-related genes include CCL chemokines, Chemokine (C-X-C motif) ligand (CXCL), CD antigens, HSP family, interferon family, and interleukin family. The hypoxia-immune-based gene signature represents a promising tool for risk stratification tool in oral squamous cell carcinoma cancer. It might serve as a prognostic classifier for clinical decision-making regarding individualized prognostication and treatment and follow-up scheduling.