A Prognostic Model of Pseudogenes in Acute Myeloid Leukemia.

Abstract

Acute myeloid leukemia (AML) is a hematologic malignancy characterized by the abnormal proliferation of myeloid hematopoietic cells and it is urgently needed to develop new molecular biomarkers to predict clinical outcomes and improve therapeutic effects. The differentially expressed genes were identified by comparing TCGA with GETx data. Univariate LASSO and multivariate cox regression analysis were performed to identify prognosis-associated pseudogenes. Based on the overall survival of related pseudogenes, we used them to construct a prognostic model for AML patients. Moreover, we built the pseudogenes-miRNA-mRNA ceRNA networks and explored their involved biological functions and pathways via GO and KEGG enrichment analysis. Seven prognosis-associated pseudogenes were identified, including CCDC150P1, DPY19L1P1, FTH1P8, GTF2IP4, HLA-K, NAPSB, and PDCD6IPP2. The risk model based on these 7 pseudogenes could accurately predict the 1-year, 3-year, and 5-year survival rates. The GO and KEGG enrichment analyses demonstrated that these prognosis-associated pseudogenes were significantly enriched in cell cycle, myeloid leukocyte differentiation, regulation of hemopoiesis, and other critical cancer-related biological functions and pathways. We systematically and comprehensively analyzed the prognostic role of pseudogenes in AML. The prognostic model of pseudogenes we identified is an independent predictor of overall survival in AML and could be used as biomarker for AML treatment.