Analysis of mRNA biomarker predicting progression of acute lymphoblastic leukaemia by big data mining

Abstract

Abstract Acute lymphoblastic leukaemia (ALL) is a hematologic malignancy. In this study, we focus on the research of the progressed biomarker of the ALL disease based on the ALL phase II to phase III mRNA expression data from Therapeutically Applicable Research to Generate Effective Treatment (TARGET). 204 differentially expressed mRNAs (DEmRNAs) were screened from the mRNA matrix (P-value < 0.01 and |LogFoldChange| > 4). And the DEmRNAs were enriched in 16 MF, 15 CC and 49 BP groups of the gene ontology (GO) terms (Count > 2 and P-value < 0.1) and 3 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (P-value < 0.05, q-value < 0.05) by the GO and KEGG pathway analysis. The survival analysis done by Kaplan-meier method was shown that the DEmRNAs and their enriched GOterms were closely related with the high risk of ALL progression. And the DemRNAs would be the progressed biomarker of ALL when they were proved by the receiver operating characteristic (ROC) analysis.