Circular RNA regulatory network reveals cell-cell crosstalk in acute myeloid leukemia extramedullary infiltration

Background: Acute myeloid leukemia can develop as myoblasts infiltrate into organs and tissues anywhere other than the bone marrow, which called extramedullary infiltration (EMI), indicating a poor prognosis. Circular RNAs (circRNAs) are a novel class of non-coding RNAs that feature covalently closed continuous loops, suggesting their potential as micro RNA (miRNA) "sponges" that can participate in biological processes and pathogenesis. However, investigations on circRNAs in EMI were conducted rarely. In this study, the overall alterations of circRNAs and their regulatory network between EMI and non-EMI AML were delineated.

Methods: CircRNA and whole genome microarrays derived from EMI and non-EMI AML bone marrow mononuclear cells were carried out. Functional analysis was performed via Gene Ontology and KEGG test methods. The speculated functional roles of circRNAs were based on mRNAs and predicted miRNAs that played intermediate roles. Integrated bioinformatic analysis was conducted to further characterize the circRNA/miRNA/mRNA regulatory network and identify the functions of distinct circRNAs. The Cancer Genome Atlas (TCGA) data were acquired to evaluate the poor prognosis of distinct target genes of circRNAs. Reverse transcription-quantitative polymerase chain reaction was conducted to identify the expression of has_circRNA_0004520. Connectivity map (CMap) analysis was further performed to predict potential therapeutic agents for EMI.

Results: 253 circRNAs and 663 genes were upregulated and 259 circRNAs and 838 genes were downregulated in EMI compared to non-EMI AML samples. GO pathways were enriched in progress including cell adhesion (GO:0030155; GO:0007155), migration (GO:0016477; GO:0030334), signal transduction (GO:0009966; GO:0007165) and cell-cell communication. Overlapping circRNAs envolved in pathways related to regulate cell-cell crosstalk, 17 circRNAs were chosen based on their putative roles. 7 target genes of 17 circRNAs (LRRK1, PLXNB2, OLFML2A, LYPD5, APOL3, ZNF511, and ASB2) indicated a poor prognosis, while overexpression of PAPLN and NRXN3 indicated a better one based on data from TCGA. LY-294002, trichostatin A and SB-202190 were identified as therapeutic candidates for EMI by the CMap analysis.

Conclusion: Taken together, this study reveals the overall alterations of circRNA and mRNA involved in EMI and suggests potential circRNAs may act as biomarkers and targets for early diagnosis and treatment of EMI.