Integrated Analysis of the Functions and Prognostic Values of RNA Binding Proteins in Lung Squamous Cell Carcinoma
Wei Li, Xue Li, Lina Gao, Chongge You
Abstract
Lung cancer is the leading cause of deaths related to cancer in the world. RNA binding proteins (RBPs) dysregulation have been found in a variety of cancers and related to the oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma(LUSC) remains unclear. In this study, we obtained the gene expression data and corresponding clinical information of LUSC from the Cancer Genome Atlas (TCGA) database and identified the aberrantly expressed RBPs between tumor and normal tissues. Then we conducted a series of bioinformatics analysis to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained from the LUSC patients, including 59 down-regulated and 241 up-regulated RBPs. Functional enrichment analysis indicated that the differently expressed RBPs were mainly associated with mRNA metabolic process, RNA processing, RNA modification, regulation of translation, TGF-beta signaling pathway, and Toll-like receptor signaling pathway. Nine RBPs genes (A1CF, EIF2B5, LSM1, LSM7, MBNL2, RSRC1, TRMU, TTF2, and ZCCHC5) were identified as prognosis-associated hub gene by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier survival and multivariate Cox regression analyses, and used to construct the prognostic model. Further analysis demonstrated that the patients with high-risk score were significantly related to poor OS based on the model. The area under the curve(AUC) of the time-dependent receiver operator characteristic (ROC) curve of the prognostic model is 0.712 at 3 years, and 0.696 at 5 years. We also developed a nomogram based on nine RBPs gene and internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis of LUSC. Our results provide novel clues for understanding the pathogenesis of LUSC. The nine-RBPs gene signature could predict prognosis of LUSC, which might contribute to clinical decision-making and promote individualized treatment.