Correlation analysis of m6A-modified regulators with immune microenvironment infiltrating cells in lung adenocarcinoma.

Wei Ye,Tianpeng Huang,Afsheen Raza

doi:10.1371/journal.pone.0264384

Wei Ye, Tianpeng Huang + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0264384

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Abstract

Recent studies have demonstrated the epigenetic regulation of immune responses. However, the potential role of N6-methyladenosine methylation (m6A) in the tumor microenvironment (TME) remains unknown. In this study, the m6A modification patterns of LUAD samples were comprehensively evaluated by combining TCGA and GEO data, while these modification patterns were systematically linked to the characteristics of immune infiltrating cells in TME. The m6A score was constructed using the principal component analysis algorithm to quantify the m6A modification mode of a single tumor. There were three distinct patterns of m6A modification identified. The characteristics of TME cell infiltration in these three patterns were highly consistent with these three immune phenotypes of the tumors, including immune rejection, immune-inflammatory, and immune inert phenotypes. Low m6A scores were characterized by immune activation and poor survival rate. Besides, m6A scores were associated with tumor mutational load (TMB) and were able to increase the ability of TMB to predict immunotherapy. Two immunotherapy cohorts confirmed that the patients with lower m6A scores demonstrated significant therapeutic advantages and clinical benefits. m6A modifications play an important role in the development of TME diversity. Assessing the m6A modification pattern of individual tumors can deepen the understanding as to the characteristics of TME infiltration and guide more effective immunotherapy strategies.

Highlights

The lung adenocarcinoma (LUAD) expression data and complete clinical information were sourced from the cancer genome atlas (TCGA) database and the Gene Expression Omnibus (GEO) database
Copy number variation analysis revealed that the significant increase in copy number occurred in YTHDF1, VIRMA, FMR1, RMR1, METTL3, HNRNPC, RBMX, LRPPRC, and HNRNPA2B1, while extensive copy number deletions were present in YTHDF2, YTHDC1, YTHDC2, RBM15, and METLL14 (Fig 1B)
Both LUAD tissues and adjacent noncancerous tissues could be identified according to the copy number variation (CNV) alterations in chromosomes

Summary

Methods

The LUAD expression data and complete clinical information were sourced from the cancer genome atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Transcriptomic expression data (Fragments Per Kilobase of exon model per Million mapped fragments) and the corresponding clinical information data from the TCGA-LUAD dataset, including 535 lung adenocarcinoma tissue samples and 59 paraneoplastic tissue samples, were downloaded from the Genomic Data Commons (GDC https://portal.gdc.cancer.gov/). The transcriptomic expression data was based on the Illumina HiSeq high-throughput sequencing platform. Transcriptomic expression data was annotated according to GENCODE version 29 (https://www.gencodegenes.org/human/). A gene expression profile (GSE26939) was downloaded from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database by searching for "lung adenocarcinoma" (January 2021). The platform annotation file for GSE26939 is Agilent-UNC-custom-4X44K. The TCGA-LUAD metagenomic data was downloaded from GDC for copy number variation (CNV) analysis

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Conclusion