Identification of a multidimensional transcriptome prognostic signature for lung adenocarcinoma

Abstract

Background: Lung adenocarcinoma (LUAD) is one of the leading contributors to cancer-related deaths worldwide. The objective of the current study is to identify a multidimensional transcriptome prognostic signature by combining protein-coding gene (PCG) with long non-coding RNA (lncRNA) for patients with LUAD. Methods: We obtained LUAD PCG and lncRNA expression profile data from three datasets in the Gene Expression Omnibus database and conducted survival analyzes for these individuals. Results: We established a predictive model comprising the three PCGs (NHLRC2, PLIN5, GNAI3), and one lncRNA (AC087521.1). This model segregated patients with LUAD into low- and high-risk groups based on significant differences in survival in the training dataset (GSE31210, n = 226, log-rank test P