Identification and validation of a prognostic risk-scoring model for AML based on m7G-associated gene clustering - Zhang, Chiyi; Wen, Ruiting; Wu, Guocai; Li, Guangru; Wu, Xiaoqing; Guo, Yunmiao; Yang, Zhigang (Frontiers in Oncology, 2024)
Acute myeloid leukemia (AML) patients still suffer from poor 5-year survival and relapse after remission. A better prognostic assessment tool is urgently needed. New evidence demonstrates that 7-methylguanosine (m7G) methylation modifications play an important role in AML, however the exact role of m7G-related genes in the prognosis of AML remains unclear. In this study, 29 m7G-related genes were identified. Based on these genes, AML cases in the TCGA cohort could be divided into three subtypes. These three subgroups characterized by gene clusters were screened by WGCNA. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 6-gene (including CBR1, CCDC102A, LGALS1, RD3L, SLC29A2, and TWIST1) signature was built and classified all AML patients in the TCGA cohort into a low- or high-risk group. The survival rate of low-risk patients was significantly higher than that of high-risk patients (p < 0.0001). The area under the curve values at 1, 3, and 5 years in the training set were 0.871, 0.874, and 0.951, respectively, indicating that this predictive model has an excellent predictive effect. In addition, after univariate and multivariate Cox regression screening, histograms were constructed with clinical characteristics and prognostic risk score models to better predict individual survival. Further analysis showed that the prognostic risk score model was associated with immune cell infiltration. These findings suggest that the scoring model and essential risk genes could provide potential prognostic biomarkers for patients with acute myeloid leukemia.