Development and validation of a RNA binding protein-associated prognostic model for lung adenocarcinoma.

Wei Li,Pei-Pei Song,Li-Na Gao,Chong-Ge You

doi:10.18632/aging.102828

Abstract

RNA binding proteins (RBPs) dysregulation have been reported in various malignant tumors and associated with the occurrence and development of cancer. However, the role of RBPs in lung adenocarcinoma (LUAD) is poorly understood. We downloaded the RNA sequencing data of LUAD from the Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. The study then systemically investigated the expression and prognostic value of these RBPs by a series of bioinformatics analysis. A total of 223 differently expressed RBPs were identified, including 101 up-regulated and 122 down-regulated RBPs. Eight RBPs (IGF2BP1, IFIT1B, PABPC1, TLR8, GAPDH, PIWIL4, RNPC3, and ZC3H12C) were identified as prognosis related hub gene and used to construct a prognostic model. Further analysis indicated that the patients in the high-risk subgroup had poor overall survival(OS) compared to those in low-risk subgroup based on the model. The area under the curve of the time-dependent receiver operator characteristic curve of the prognostic model are 0.775 in TCGA cohort and 0.814 in GSE31210 cohort, confirming a good prognostic model. We also established a nomogram based on eight RBPs mRNA and internal validation in the TCGA cohort, which displayed a favorable discriminating ability for lung adenocarcinoma.

Highlights

Lung cancer is a very harmful disease that remains a top cause of cancer-related deaths worldwide
The databases of lung adenocarcinoma were downloaded from the Cancer Genome Atlas (TCGA) contained 524 tumor samples and 59 normal lung tissue samples
We found that the area under the receiver operating characteristic (ROC) curve (AUC) of this RNA binding proteins (RBPs) risk score model was 0.775 (Figure 6B), which indicated that it has moderate diagnostic performance

Summary

Introduction

Lung cancer is a very harmful disease that remains a top cause of cancer-related deaths worldwide It is estimated at 220,000 newly diagnosed lung cancer cases and more than 140,000 deaths in the USA in 2019 [1]. The diagnosis of lung cancer primarily depends on histopathological examination, cancer molecular biomarkers, imaging evaluations, and it is difficult to achieve early detection of lung tumor [4, 5]. This may be the most significant cause of high mortality in lung cancer patients. Further understanding the molecular mechanism of lung cancer to develop effective methods for early screening and diagnosis are critical to improve therapeutic effect and quality of life of patients

Methods

Results

Conclusion