Abstract

Lung adenocarcinoma is a highly aggressive subtype of lung cancer with a high mortality rate, which makes the investigation of novel lung adenocarcinoma therapeutic targets essential. Since oxidative stress has been shown to be a cancer-specific deplete in the lung, which is highly associated with oxidative stress, it is crucial to investigate and assess the clinical value of oxidative stress-related target genes in lung adenocarcinoma. In the current study, we obtained 3170 differentially expressed genes via differential analysis and 1058 oxidative stress-related genes via the molecular signatures database. Upon analyzing their intersection, 207 genes were identified. tRNA-histidine guanylyltransferase 1 like protein, glutathione peroxidase 8, and mitogen-activated protein kinase 3 were identified as lung adenocarcinoma biomarkers using machine learning algorithms. The tumor immune infiltrating analysis revealed that the tRNA-histidine guanylyltransferase 1 like protein gene is associated with the infiltration of regulatory T cells. In addition, the multivariate analysis revealed that elevated tRNA-histidine guanylyltransferase 1 like protein expression is a significant, independent predictor of overall survival. Furthermore, the pan-cancer analysis revealed that elevated tRNA-histidine guanylyltransferase 1 like protein expression is related to a worse prognosis in a variety of cancers. In light of these findings, the identification of tRNA-histidine guanylyltransferase 1 like protein as a novel biological marker may shed light on potential function mechanisms and the impact of the immune microenvironment in lung adenocarcinoma.