Development and Validation of a Prognostic N6-Methyladenosine-Related Immune Gene Signature for Lung Adenocarcinoma

Pharmgenomics Pers Med. 2021 Nov 30:14:1549-1563. doi: 10.2147/PGPM.S332683. eCollection 2021.

Abstract

Purpose: The prognostic value of an N6-methyladenosine (m6A) methylation-related immune gene signature for lung adenocarcinoma (LUAD) was investigated.

Patients and methods: Gene expression and clinical phenotype data of LUAD patients were downloaded from The Cancer Genome Atlas database. A list of immune-related genes was retrieved from the InnateDB database. Correlation analysis, survival analysis, and univariate and multivariate Cox regression analyses were performed. After allocating patients into a high-risk or a low-risk group, the corresponding survival rates, immune microenvironment, expression of immune checkpoint genes, and modulation of Kyoto Encyclopedia of Genes and Genomes pathways were examined. Finally, the expression levels of prognostic biomarkers were assessed in the GSE126044 dataset.

Results: Seven m6A-related immune prognostic genes were identified. High expression of PSMD10P1, DIDO1, ABCA5, and CIITA was associated with high survival rates, while that of PRC1, ZWILCH, and ANLN was associated with low survival rates. The high- and low-risk groups showed significant differences in terms of the abundance of six tumor-infiltrating immune cell types and expression of 12 immune checkpoint genes. The risk group acted as an independent prognostic factor (hazard ratio = 0.398, 95% confidence interval = 0.217-0.729, P = 0.003). Finally, the developed nomogram could predict most efficiently the 1-, 2-, and 3-year survival probability of LUAD patients with a C-index of 0.833.

Conclusion: A seven-gene risk signature, associated with the immune microenvironment in LUAD, showed independent prognostic value.

Keywords: N6-methyladenosine methylation; immune microenvironment; immune-related genes; lung adenocarcinoma; prognostic signature.

Grants and funding

This study was supported by grants from the National Natural Science Foundation of Hunan Province (2019JJ50353, 2020JJ4418, 2020RC3067), Natural Science Foundation of Hunan Province National Health Commission (B2019091, 20201286), Natural Science Foundation of Changsha Science and Technology Bureau (Kq1901084, Kq1901080, Kq2001024), and Cancer Foundation of China (NCC2018B58), Key Research and Development Project of Hunan Province (2017WK2061); HUI LAN PUBLIC FOUNDATION (HL-HS2020-1); Clinical medical technology innovation guided project (2020SK51112).