Identification of prognostic and driver gene mutations in acute myeloid leukemia by a bioinformatics analysis.

Background: The representative gene mutation in the prognostic groups of acute myeloid leukemia (AML) patients is not yet known. The purpose of this study is to identify representative mutations that can help physicians better predict patient prognosis and thus develop better treatment plans. Methods: The Cancer Genome Atlas (TCGA) database was queried for clinical and genetic information, and individuals with AML were classified into 3 groups based on their AML Cancer and Leukemia Group B (CALGB) cytogenetic risk category. Each group's differentially mutated genes (DMGs) were evaluated. Simultaneously, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to assess the function of DMGs within the 3 distinct groups. We used the driver status and protein impact of DMGs as additional filters to narrow down the list of significant genes. Cox regression analysis was used to examine the survival features of gene mutations in these genes. Results: A cohort of 197 AML patients were divided into 3 groups according to their prognostic subtype: favorable (n=38), intermediate (n=116) and poor (n=43). There were significant differences in age and tumor metastasis rates among the three groups of patients. Patients in the favorable group had the highest rate of tumor metastasis. Different prognosis groups' DMGs were detected. The DMGs were examined for the driver and harmful mutations. We considered the DMGs that had driver and harmful mutations and that affected the survival outcomes in the prognostic groups as the key gene mutations. The group with a favorable prognosis carried specific gene mutations for Conclusions: We performed the systemic analysis of the gene mutation in patients with AML and identified representative and driver mutations between the prognostic group. identification of representative and driver mutations between the prognostic group can help predict the prognosis of patients with AML and guide treatment decisions.