Identification of the key genes and microRNAs in adult acute myeloid leukemia with FLT3 mutation by bioinformatics analysis

Shuyi Chen; Yimin Chen; Zhiguo Zhu; Huo Tan; Jielun Lu; Pengfei Qin; Lihua Xu

doi:10.7150/ijms.46441

Identification of the key genes and microRNAs in adult acute myeloid leukemia with FLT3 mutation by bioinformatics analysis

Int J Med Sci. 2020 May 18;17(9):1269-1280. doi: 10.7150/ijms.46441. eCollection 2020.

Authors

Shuyi Chen^{1

2}, Yimin Chen^{1

2}, Zhiguo Zhu², Huo Tan¹, Jielun Lu³, Pengfei Qin¹, Lihua Xu^{1

2}

Affiliations

¹ Department of Hematology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China.
² Department of Urology & Minimally Invasive Surgery center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangdong, China.
³ Department of Pediatrics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China.

Abstract

Background: Associated with poor prognosis, FMS-like tyrosine kinase 3 (FLT3) mutation appeared frequently in acute myeloid leukemia (AML). Herein, we aimed to identify the key genes and miRNAs involved in adult AML with FLT3 mutation and find possible therapeutic targets for improving treatment. Materials: Gene and miRNA expression data and survival profiles were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EdgeR of R platform was applied to identify the differentially expressed genes and miRNAs (DEGs, DE-miRNAs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape and DAVID. And protein-protein interaction network, miRNA-mRNA regulatory network and clustering modules analyses were performed by STRING database and Cytoscape software. Results: Survival analysis showed FLT3 mutation led to adverse outcome in AML. 24 DE-miRNAs (6 upregulated, 18 downregulated) and 250 DEGs (54 upregulated, 196 downregulated) were identified. Five miRNAs had prognostic value and the results matched their expression levels (miR-1-3p, miR-10a-3p, miR-10a-5p, miR-133a-3p and miR-99b-5p). GO analysis showed DEGs were enriched in skeletal system development, blood vessel development, cartilage development, tissue morphogenesis, cartilage morphogenesis, cell morphogenesis involved in differentiation, response to growth factor, cell-substrate adhesion and so on. The KEGG analysis showed DEGs were enriched in PI3K-Akt signaling pathway, ECM-receptor interaction and focal adhesion. Seven genes (LAMC1, COL3A1, APOB, COL1A2, APP, SPP1 and FSTL1) were simultaneously identified by hub gene analysis and module analysis. SLC14A1, ARHGAP5 and PIK3CA, the target genes of miR-10a-3p, resulted in poor prognosis. Conclusion: Our study successfully identified molecular markers, processes and pathways affected by FLT3 mutation in AML. Furthermore, miR-10a-3p, a novel oncogene, might involve in the development of FLT3 mutation adult AML by targeting SLC14A1, ARHGAP5 and PIK3CA.

Keywords: FLT3 mutation; acute myeloid leukemia; bioinformatics analysis; differentially expressed miRNAs and genes; miR-10a-3p.

MeSH terms

Computational Biology / methods*
Gene Expression Profiling
Gene Expression Regulation, Neoplastic / genetics
Gene Expression Regulation, Neoplastic / physiology
Gene Ontology
Humans
Leukemia, Myeloid, Acute / genetics
Leukemia, Myeloid, Acute / metabolism*
MicroRNAs / genetics
MicroRNAs / metabolism*
fms-Like Tyrosine Kinase 3 / genetics*

Substances

MicroRNAs
fms-Like Tyrosine Kinase 3