Advances in transcriptomics have improved our understanding of leukemic development and helped to enhance the stratification of patients. The tendency of transcriptomic studies to combine AML samples, regardless of cytogenetic abnormalities, could lead to bias in differential gene expression analysis because of the differential representation of AML subgroups. Hence, we performed a horizontal meta-analysis that integrated transcriptomic data on AML from multiple studies, to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in the development of AML and response to therapy. A total of 28 Affymetrix microarray data sets containing 3940 AML samples were downloaded from the Gene Expression Omnibus database. After stringent quality control, transcriptomic data on 1534 samples from 11 data sets, covering 10 AML cytogenetically defined subgroups, were retained and merged with the data on 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, enabling the unbiased identification of 330 commonly deregulated genes (CODEGs), which showed enriched profiles of myeloid differentiation, leukemic stem cell status, and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGs were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated with microarray data of 5 independent cohorts and by quantitative real time-polymerase chain reaction in a cohort of 142 samples. CODEG22-based stratification of patients, globally and into subpopulations of cytologically healthy and elderly individuals, may complement the European LeukemiaNet classification, for a more accurate prediction of AML outcomes.
© 2020 by The American Society of Hematology.