Transcriptome Analysis Of Potential Biomarkers For Acute Myeloid Leukemia

ObjectiveAcute myeloid leukemia(AML)is a malignant hematological tumor.Although various treatments have made progress,there are still many problems.In this study,clinical bioinformatics analysis was used to analyze the transcriptomic data of AML patients in order to find biomarkers related to the occurrence,development and prognosis of AML,thus providing guidance for basic research and clinical treatment of AML.MethodsUsing "acute myeloid leukemia" as a key word,relevant omics data were retrieved in GEO and TCGA.Download the qualified AML experimental data from the GEO database,and construct the core gene at the mRNA level by differential analysis,GO function enrichment analysis,pathway enrichment analysis,and protein interaction network;download the RNA sequencing data that meets the requirements from the TCGA database.miRNA and lncRNA were extracted and differentially analyzed.The differential lncRNA were compared with the differential miRNA using the miRcode database to obtain the corresponding relationship between the differential miRNA and the differential lncRNA.The miRNA-targeted mRNA was searched by miRTarBase,TargetScan,and miRDB databases,and the mRNA shared by the three databases was used as the target mRNA.After de-reprocessing,the intersection of the differential mRNA and the miRNA-targeted mRNA analyzed by the GEO database was used as final target gene of the miRNA.After arranging lncRNA and miRNA relationship files and miRNA and mRNA relationship files,the Cytoscape visualization software was imported to construct a ceRNA regulatory network.Finally,the survival analysis of the mRNA core gene,miRNA and lncRNA in ceRNA network were performed to screen the mRNA,lncRNA and miRNA with high significance between the high expression group and the low expression group as the biomarkers recommended in this study.ResultsThe core genes at the mRNA level were CXCL1,FPR2,LPAR2,STAT1,JUN,TLR4,CCR7,PPBP,CCL5 and FPR1 after the analysis of AML data in GEO database.lncRNA in ceRNA control network include: LINC00449,BLACE,LINC00092,LINC00461;The included miRNA were: hsa-mir-106 a,hsa-mir-145,hsa-mir-25,hsa-mir-363,hsa-mir-96,hsa-mir-210,hsa-mir-150,hsa-mir-193 b,hsa-mir-212,hsa-mir-200 a,hsa-mir-508,and hsamir-31.mRNA included: KIAA0513,ANKRD28,SOX4,MAP2K4,CRK,MAP3K3,TGFBR2,POU2AF1,BTG2,PFKP,MYB,NACC2,SLC25A36,NAGK,BCL11 B,NRIP3,TGFBR3,PLXNC1,RPS6KA5,MEST,IVNS1 ABP,FAM129A and NEDD9.In the survival analysis,there were statistically significant differences in the expression of STAT1,JUN,has-mir-106 a,has-mir-363,has-mir-96 and has-mir-508 in the core mRNA and miRNA between the high expression group and the low expression group,while there were no statistically significant differences in the expression of other genes between the two groups(all P <0.05).Survival rate of the low STAT1 expression group was significantly higher than that of the high expression group,survival rate of the high JUN expression group was significantly higher than that of the low expression group,survival rate of the low expression group of has-mir-106 a,has-mir-363,has-mir-96 and has-mir-508 was higher than that of the high expression group.ConclusionSTAT1,JUN,has-mir-106 a,has-mir-363,has-mir-96 and has-mir-508 are closely related to AML and can be used as candidate biomarkers for the occurrence,development,drug resistance,recurrence and prognosis of AML.Four lncRNA,LINC00449,LINC00092,LINC00461 and BLACE,are recommended as biomarkers for further basic research on AML.