Abstract
Background Oral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy. Increasing evidence shows that there are abnormal immune response and chronic cell hypoxia in the development of OSCC. However, there is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC. Methods The mRNA expression data of OSCC patients were extracted from the TCGA and GEO databases. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used for further evaluation of immune status. The DEGs in different hypoxia and immune status were determined, and univariate Cox regression was used to identify significantly prognostic genes. A machine learning method, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, allowed us to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients. Results A total of 773 DEGs were identified between hypoxia high and low groups. According to immune cell infiltration, patients were divided into immune high, medium, and low groups and immune-associated DEGs were identified. A total of 193 overlapped DEGs in both immune and hypoxia status were identified. With the univariate and LASSO Cox regression model, eight signature mRNAs (FAM122C, RNF157, RANBP17, SOWAHA, KIAA1211, RIPPLY2, INSL3, and DNAH1) were selected for further calculation of their respective risk scores. The risk score showed a significant association with age and perineural and lymphovascular invasion. In the GEO validation cohort, a better OS was observed in patients from the low-risk group in comparison with those in the high-risk group. High-risk patients also demonstrated different immune infiltration characteristics from the low-risk group and the low-risk group showed potentially better immunotherapy efficacy in contrast to high-risk ones. Conclusion The hypoxia-immune-based gene signature has prognostic potential in OSCC.
Highlights
Oral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy
Based on the quantitative score of each hypoxia signature, we found that the “Seigneuric” signature had the lowest correlation with the others and was not included in this study (Figure 1(b)). e remaining seven gene signatures were used in the nonlinear dimensionality reduction algorithm calculation of two-dimensional points between any two patients
Based on the proportions of 28 immune cells quantified by single-sample gene-set enrichment analysis (ssGSEA), OSCC samples in the TCGAHNSC” project in e Cancer Genome Atlas (TCGA) database were classified into high, medium, and low-immune groups (named Immune_High, Immune_Medium and Immune_Low, resp. (Figure 2(a))
Summary
Oral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy. There is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used for further evaluation of immune status. E DEGs in different hypoxia and immune status were determined, and univariate Cox regression was used to identify significantly prognostic genes. A machine learning method, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, allowed us to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients. According to immune cell infiltration, patients were divided into immune high, medium, and low groups and immuneassociated DEGs were identified. E hypoxia-immune-based gene signature has prognostic potential in OSCC Conclusion. e hypoxia-immune-based gene signature has prognostic potential in OSCC
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