Cargando…

The m6A/m5C/m1A Regulated Gene Signature Predicts the Prognosis and Correlates With the Immune Status of Hepatocellular Carcinoma

RNA modification of m6A/m5C/m1A contributes to the occurrence and development of cancer. Consequently, this study aimed to investigate the functions of m6A/m5C/m1A regulated genes in the prognosis and immune microenvironment of hepatocellular carcinoma (HCC). The expression levels of 45 m6A/m5C/m1A...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Dan, Li, Kai, Zhang, Wei, Yang, Kong-Wu, Mu, De-An, Jiang, Guo-Jun, Shi, Rong-Shu, Ke, Di
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9272990/
https://www.ncbi.nlm.nih.gov/pubmed/35833147
http://dx.doi.org/10.3389/fimmu.2022.918140
Descripción
Sumario:RNA modification of m6A/m5C/m1A contributes to the occurrence and development of cancer. Consequently, this study aimed to investigate the functions of m6A/m5C/m1A regulated genes in the prognosis and immune microenvironment of hepatocellular carcinoma (HCC). The expression levels of 45 m6A/m5C/m1A regulated genes in HCC tissues were determined. The functional mechanisms and protein–protein interaction network of m6A/m5C/m1A regulated genes were investigated. The Cancer Genome Atlas (TCGA) HCC gene set was categorized based on 45 m6A/m5C/m1A regulated genes, and survival analysis was used to determine the relationship between the overall survival of HCC patients in subgroups. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the risk model and nomogram for m6A/m5C/m1A regulated genes. The relationships between m6A/m5C/m1A regulated gene subsets and risk model and immune cell infiltration were analyzed using CIBERSORT. m6A/m5C/m1A regulated genes were involved in mRNA and RNA modifications, mRNA and RNA methylation, mRNA and RNA stability, and other processes. There was a statistically significant difference between cluster1 and cluster2 groups of genes regulated by m6A/m5C/m1A. The prognosis of cluster1 patients was significantly better than that of cluster2 patients. There were statistically significant differences between the two cluster groups in terms of fustat status, grade, clinical stage, and T stage of HCC patients. The risk model comprised the overexpression of YBX1, ZC3H13, YTHDF1, TRMT10C, YTHDF2, RRP8, TRMT6, LRPPRC, and IGF2BP3, which contributed to the poor prognosis of HCC patients. The high-risk score was associated with prognosis, fustat status, grade, clinical stage, T stage, and M stage and was an independent risk factor for poor prognosis in HCC patients. High-risk score mechanisms included spliceosome, RNA degradation, and DNA replication, among others, and high-risk was closely related to stromal score, CD4 memory resting T cells, M0 macrophages, M1 macrophages, resting mast cells, CD4 memory activated T cells, and follicular helper T cells. In conclusion, the cluster subgroup and risk model of m6A/m5C/m1A regulated genes were associated with the poor prognosis and immune microenvironment in HCC and are expected to be the new tools for assessing the prognosis of HCC patients.