AUTHOR=Wang Huimei , Zhang Yiping , Chen Lin , Liu Yufeng , Xu Chen , Jiang Dongxian , Song Qi , Wang Haixing , Wang Liyan , Lin Yu , Chen Yuanmei , Chen Junqiang , Xu Yuanji , Hou Yingyong 

TITLE=Identification of clinical prognostic features of esophageal cancer based on m6A regulators

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.950365

DOI=10.3389/fimmu.2022.950365

ISSN=1664-3224

ABSTRACT=Abstract
Background: Esophageal cancer (ESCA) is one of the most common malignancies with high morbidity and mortality rates worldwide. N6-methyladenosine (m6A)-related regulators are generally acknowledged to be one of the leading causes of cancer initiation and progression. However, the role of regulators in ESCA remains largely unknown. We aimed to identify the roles of m6A RNA methylation regulators in immune modulation and prognosis of ESCA.
Methods: RNA-seq data downloaded from The Cancer Genome Atlas (TCGA) database were used to analyze the differential expression of m6A RNA methylation regulators in ESCA. An m6A methylation regulator-based signature was further constructed, and its prognostic and predictive values were assessed using survival analysis and nomogram. Patients were classified into low- and high-risk groups. The signature was evaluated from the perspective of survival, single nucleotide polymorphism (SNP), copy number variation (CNV), tumor mutation burden (TMB), and functional enrichment analysis. Quantitative reverse-transcription PCR (qRT-PCR) was used to validate the expression of key m6A-related genes in clinical specimens.
Results: Most of the 23 regulators were significantly differentially expressed in the ESCA tissues. LASSO regression analysis was used to perform a prognostic risk model that included seven m6A-related regulators (FMR1, RBMX, IGFBP1, IGFBP2, ALKBH5, RBM15B, and METTL14). Moreover, we found that this risk model was significantly correlated with biological functions, including base metabolism, DNA repair, and mismatch repair. A nomogram was constructed to predict the prognosis of patients with ESCA. The results of bioinformatics analysis were further validated in human ESCA and normal tissues by qRT-PCR.
Conclusions: Seven m6A-related gene signatures were identified in patients with ESCA. Our results suggest that the three different m6A modification clusters contribute to the immune microenvironment in ESCA, which may provide important clues for developing diagnostic and therapeutic strategies.