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)
Background 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. Methods The study obtained AML expression profiles and clinical information from TCGA, GEO, and TARGET databases. Using the patient data from the TCGA cohort as the training set. Consensus clustering was performed based on 29 m7G-related genes. Survival analysis was performed by KM curves. The subgroup characteristic gene sets were screened using WGCNA. And tumor immune infiltration correlation analysis was performed by ssGSEA. Results The patients were classified into 3 groups based on m7G-related genebased cluster analysis, and the differential genes were screened by differential analysis and WGCNA. After LASSO regression analysis, 6 characteristic genes (including CBR1, CCDC102A, LGALS1, RD3L, SLC29A2, and TWIST1) were screened, and a prognostic risk-score model was constructed. 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. Conclusion These findings suggest that the scoring model and essential risk genes could provide potential prognostic biomarkers for patients with acute myeloid leukemia.