Hypoxia-related gene signature for predicting LUAD patients’ prognosis and immune microenvironment

Lung adenocarcinoma (LUAD) is a prevalent lung cancer histology with high morbidity and mortality. Moreover, assessment approaches for patients' prognoses are still not effective. Based on mRNA expression and clinical data from the Cancer Genome Atlas (TCGA)-LUAD data set, we utilized hypoxia-related gene set in MsigDB database to identify hypoxia-related differentially expressed genes (DEGs). On the basis of levels of hypoxia-related DEGs, K-means consensus clustering was introduced to divide LUAD patients into subgroups. After hypoxia-related DEGs were analyzed through univariate, Lasso and multivariate Cox regression analyses, 6 of them were determined to be used for evaluating LUAD patients' prognostic signature. With median risk score obtained from hypoxia-related gene signature as threshold, LUAD patients were divided into high- and low-risk groups. Besides, Kaplan-Meier curves, receiver operator characteristic (ROC) curves, univariate and multivariate Cox regression analyses verified that hypoxia-related gene signature was an important prognostic factor independent of clinical features. Gene set enrichment analysis (GSEA) displayed that pathways which showed differences between high- and low-risk groups in activation of pentose-phosphate pathway and p53 signaling pathway. CIBERSORT was utilized to assess infiltration level of each immune cell from two groups, indicating the differences in infiltration abundance of Plasma cells, T cells CD4+ memory activated and Macrophages M1 cells between high- and low-risk groups. We drew a nomogram for predicting one-, three- and five-year survival of LUAD patients following risk scores of hypoxia-related gene signature and six clinical factors. Calibration curves showed a high fit between survival predicted by nomogram and actual survival. In conclusion, hypoxia-related gene signature can be introduced for predicting LUAD patients' prognosis and assessment of the patients' immune microenvironment, guiding clinicians to make appropriate decisions during diagnosis and treatment of LUAD patients.