Open AccessAnalysis of mRNA biomarker predicting progression of acute lymphoblastic leukaemia by big data mining
Author(s) -
Jianzhi Deng,
Xiaohui Cheng,
Yong Zhou
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/332/3/032022
Subject(s) - kegg , biomarker , malignancy , encyclopedia , messenger rna , gene , value (mathematics) , computational biology , receiver operating characteristic , oncology , gene expression , medicine , biology , gene ontology , bioinformatics , genetics , computer science , machine learning , library science
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 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.