Abstract

PurposeGlutathione peroxidase-7 (GPX7) is a newly discovered non-selenium-containing protein with glutathione peroxidase activity, which mainly protects the organism from oxidative damage and is very important for basic biology studies. This study aims to reveal the expression pattern of GPX7 and its prognosis potential from a pan-cancer perspective.MethodsExpression levels of GPX7 in human tumor tissues and normal tissues were evaluated using Human Protein Atlas (HPA), the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and UALCAN databases. The prognostic potential of GPX7 for 33 TCGA tumors was evaluated by Kaplan–Meier analysis and Cox regression analysis. Subsequently, the Chinese Glioma Genome Atlas (CGGA) dataset was used to further verify the expression of GPX7 and its prognostic potential in glioma. We explored the correlation between GPX7 and immune infiltration, tumor mutational burden (TMB) and microsatellite instability (MSI). Furthermore, a nomogram lower-grade glioma (LGG) was constructed to verify the prognostic outcome of patients. Finally, the relationship between GPX7 and treatment regimens for LGG was also explored.ResultsGPX7 was overexpressed in multiple tumors. Elevated expression of GPX7 was associated with poor prognosis of LGG patients (OS hazard ratio (HR) = 1.044, P < 0.0001; DFS HR = 1.035, P < 0.0001; PFS HR = 1.045, P < 0.0001). GPX7 was proved to be an independent prognostic factor of LGG through univariate and multivariate Cox analysis. The nomogram confirmed a better predictability (Concordance index (C-index): 0.845; 95% CI, 0.825–0.865). GPX7 was positively correlated with TMB in LGG. GPX7 expression was negatively correlated with half-maximal inhibitory concentration (IC50) of temozolomide (TMZ) (nmathord{buildrel{lower3pthbox{scriptscriptstylefrown}}over rho } nspearman= −0.59, P =1.3e-48).ConclusionGPX7 was upregulated in multiple tumors, and it was a potential prognostic biomarker in LGG. High-expressed GPX7 can predict the sensitivity of TMZ in LGG patients.

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