Adenylate uridylate-rich element genes (AREGs) are crucial in modulating gene expression following transcription. However, the comprehensive role of AREGs in lung squamous carcinoma (LUSC) r emains inadequately understood. Transcriptome data from TCGA and GTEx databases to identify differentially expressed AREGs. Clustering algorithms were used to identify AREGs-related subtypes, and a prognostic model was developed through univariate/multivariate and LASSO regression analyses. Following this, we created a nomogram integrating clinical pathologic characteristics and the risk model. The immune microenvironment was evaluated using CIBERSORT, ESTIMATE, and MCPcounter analyses. We examined the mRNA expression of the signature genes in normal and lung squamous carcinoma cells using RT-qPCR. Finally, we assessed the sensitivity to drugs based on the signature genes in risk patients. Patients with the 2 identified molecular subtypes exhibit distinct prognoses and immune microenvironments. We identified 5 genes with prognostic significance that can serve as independent predictors in clinical practice. The low-risk patients demonstrates more favorable prognostic outcomes, while the high-risk patients show elevated immune scores and increased immune cell infiltration, suggesting a favorable response to immunotherapy. RT-qPCR results showed upregulation of FAM83A and TINAGL1 and downregulation of FGG and ADH1C in LUSC. In addition, the low-risk patients show increased sensitivity to vinorelbine. The molecular subtypes and prognostic model based on AREGs demonstrate reliable clinical prognostic value. This finding may contribute to personalized and precise treatment for patients with LUSC, offering new insights for improving patient outcomes.
Huang et al. (Tue,) studied this question.