A Prognostic Model of Pseudogenes in Acute Myeloid Leukemia.
Clinical Laboratory 2023 May 2
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
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