%A Liu,Zhicheng %A Li,Shanshan %A Huang,Shan %A Wang,Tao %A Liu,Zhicheng %D 2021 %J Frontiers in Oncology %C %F %G English %K m6A RNA methylation regulators,Long Noncoding RNAs,Tumor Microenvironment,Immune Cell Infiltration,Uveal Melanoma %Q %R 10.3389/fonc.2021.704543 %W %L %M %P %7 %8 2021-July-30 %9 Original Research %+ Zhicheng Liu,School of Biomedical Engineering, Capital Medical University,China,zcliu@ccmu.edu.cn %+ Zhicheng Liu,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University,China,zcliu@ccmu.edu.cn %# %! m6A Regulators in Uveal Melanoma %* %< %T N6-Methyladenosine Regulators and Related LncRNAs Are Potential to be Prognostic Markers for Uveal Melanoma and Indicators of Tumor Microenvironment Remodeling %U https://www.frontiersin.org/articles/10.3389/fonc.2021.704543 %V 11 %0 JOURNAL ARTICLE %@ 2234-943X %X Uveal melanoma (UM) is one of the most common malignant intraocular tumors in adults. Few studies have investigated the effect of N6-methyladenosine (m6A) RNA methylation regulators and related long noncoding RNAs (lncRNAs) on the tumor microenvironment (TME) and survival time of patients with UM. Based on the transcriptome and clinical data from The Cancer Genome Atlas, we systematically identified m6A regulators. Then, we constructed an m6A regulators-based signature to predict the prognostic risk using univariate and LASSO Cox analyses. The signature was then validated by performing Kaplan-Meier, and receiver operating characteristic analyses. Through the correlation analysis, m6A regulators-related lncRNAs were identified, and they were divided into different clustering subtypes according to their expression. We further assessed differences in TME scores, the survival time of patients, and immune cell infiltration levels between different clustering subtypes. Finally, we screened out the common immune genes shared by m6A-related lncRNAs and determined their expression in different risk groups and clustering subtypes. For further validation, we used single-cell sequencing data from the GSE139829 dataset to explore the expression distribution of immune genes in the TME of UM. We constructed a prognostic risk signature representing an independent prognostic factor for UM using 3 m6A regulators. Patients in the low-risk group exhibited a more favorable prognosis and lower immune cell infiltration levels than patients in the high-risk group. Two subtypes (cluster 1/2) were identified based on m6A regulators-related lncRNAs. The TME scores, prognosis, and immune cell infiltration have a marked difference between cluster 1 and cluster 2. Additionally, 13 common immune genes shared by 5 lncRNAs were screened out. We found that these immune genes were differentially expressed in different risk groups and clustering subtypes and were widely distributed in 3 cell types of TME. In conclusion, our study demonstrated the important role of m6A regulators and related lncRNAs in TME remodeling. The signature developed using m6A regulators might serve as a promising parameter for the clinical prediction of UM.