Development of a Prognostic Model Based on Differentially Expressed Immune Genes of Lung Adenocarcinoma

Abstract

Due to the enormous heterogeneity and molecular complexity, the efficacy of existing lung adenocarcinoma risk prediction models were less than satisfactory. In this study, we downloaded the immune-related genes were from InnateDB, and the differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2 algorithm. In total, 1359 DEIGs were identified. Cytoscape was employed to build a mRNA-miRNA-lncRNA network which was consists of 8 lncRNAs, 7 miRNAs and 117 DEIGs. Functional enrichment analysis indicated that the 117 DEIGs were involved in immune and inflammatory responses and actively involved in a MAPK signaling pathway. A prognostic signature based on ten DEIGs including ASPH, CAV1, FKBP4, GRIK2, FURIN, SLC6A8, FSCN1, CKAP4, HAPLN2 and IL22RA2 which performed well was correlated with tumor burden, tumor stage and metastasis. The similar result was obtained in the validation dataset GSE72094 (p<0.0001). The prognostic index reflected infiltration by B cell, CD4+T cell, CD8+T cell and dendritic cell. We also found that ASPH and FSCN1 were over-expression in A549, H1299, PC-9 cells, and the positive expression of ASPH, FSCN1, MS4A1 and CD40LG had a correlation with the TNM stage, cellular differentiation and the lymphnode metastasis(p<0.05). High levels of ASPH and FSCN1, low levels of MS4A1 and CD40LG expression are all associated with poor overall survival in LUAD (p<0.05). In conclusion, our results identified DEIGs of clinical significance, verified the effect of the DEIGs-based, personalized prediction model for lung adenocarcinoma prognosis. Together, our results revealed that ASPH and FSCN1 could be a prognostic marker for lung adenocarcinoma.