Xinghua Pan
The Stem Cells and Immune Cells Biomedical Techniques Integrated Engineering Laboratory of State and Regions, Cell Therapy Technology Transfer Medical Key Laboratory of Yunnan Province, Basic Medical Laboratory, 920th Hospital of the PLA Joint Logistics Support Force, 650032, Yunnan Province, China
Xiangqing Zhu
The Stem Cells and Immune Cells Biomedical Techniques Integrated Engineering Laboratory of State and Regions, Cell Therapy Technology Transfer Medical Key Laboratory of Yunnan Province, Basic Medical Laboratory, 920th Hospital of the PLA Joint Logistics Support Force, 650032, Yunnan Province, China
Lin Liu
The Stem Cells and Immune Cells Biomedical Techniques Integrated Engineering Laboratory of State and Regions, Cell Therapy Technology Transfer Medical Key Laboratory of Yunnan Province, Basic Medical Laboratory, 920th Hospital of the PLA Joint Logistics Support Force, 650032, Yunnan Province, China
Sanbin Wang
The Stem Cells and Immune Cells Biomedical Techniques Integrated Engineering Laboratory of State and Regions, Cell Therapy Technology Transfer Medical Key Laboratory of Yunnan Province, Basic Medical Laboratory, 920th Hospital of the PLA Joint Logistics Support Force, 650032, Yunnan Province, China
Xin Liu
The Second Affiliated Hospital of Kunming Medical University,650000, Yunnan Province, China
Abstract:
Methylation of RNA plays a role in post-transcriptional regulation, which has a significant impact on cell function and the occurrence and development of diseases. M1A methylation plays a role in the occurrence and development of a variety of tumors, but the mechanism of m1A methylation in acute myeloid leukemia (AML) remains unclear. In this study, AML-related data sets were downloaded from Gene Expression Omnibus (GEO) database and UCSC Xena database and divided into Training group and Validation group. The expression and mutation of m1A related genes were analyzed, and the m1A modifier gene consensus clustering model and m1A scoring model were constructed to analyze the effect of m1A scoring model on immune cell infiltration, immunotherapy and clinical prognosis. 121 m1A modifier genes were obtained by screening, and the most frequently mutated bases were from C to T. Univariate COX regression analysis screened 24 suitable m1A modifier genes. Consistency cluster analysis divided the Training group into two clusters (Clusters1 and Clusters2), and the Validation group into three clusters (Clusters1, Clusters2 and Clusters3). K-M survival analysis showed that the survival time of the Cluster 2 was higher than that of the other clusters. The m1A score model showed that the survival outcome of AML patients with high m1A score was significantly better than that of AML patients with low m1A score. AML patients with high m1A score had lower immune cell enrichment score, lower immune checkpoint expression, and better survival outcome. M1A scoring model can be used as an effective biomarker for the clinical prognosis of AML patients
Keywords:acute myeloid leukemia, m1A, immune cell infiltration, immunotherapy, clinical prognosis