Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma.

Abstract

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion‐related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion‐related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi‐gene risk model was constructed by Lasso‐Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion‐related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour‐related pathways. A 5‐gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso‐Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5‐gene signature prognostic stratification system based on invasion‐related genes could be used to assess prognostic risk in patients with LUAD.