Long Non-coding RNAs in Myeloid Malignancies

Abstract

Acute myeloid leukemia (AML) represents 80% of adult leukemias and 15-20% of childhood leukemias. AML are characterized by the presence of 20% blasts or more in the bone marrow, or defining cytogenetic abnormalities. Laboratory diagnoses of myelodysplastic syndromes (MDS) depend on morphological changes based on dysplasia in peripheral blood and bone marrow, including peripheral blood smears, bone marrow aspirate smears, and bone marrow biopsies. As leukemic cells are not functional, the patient develops anemia, neutropenia, and thrombocytopenia, leading to fatigue, recurrent infections, and hemorrhage. The genetic background and associated mutations in AML blasts determine the clinical course of the disease. Over the last decade, non-coding RNAs transcripts that do not codify for proteins but play a role in regulation of functions have been shown to have multiple applications in the diagnosis, prognosis and therapeutic approach of various types of cancers, including myeloid malignancies. After a comprehensive review of current literature, we found reports of multiple long non-coding RNAs (lncRNAs) that can differentiate between AML types and how their exogenous modulation can dramatically change the behavior of AML cells. These lncRNAs include: H19, LINC00877, RP11-84C10, CRINDE, RP11848P1.3, ZNF667-AS1, AC111000.4-202, SFMBT2, LINC02082-201, MEG3, AC009495.2, PVT1, HOTTIP, SNHG5, and CCAT1. In addition, by performing an analysis on available AML data in The Cancer Genome Atlas (TCGA), we found 10 lncRNAs with significantly differential expression between patients in favorable, intermediate/normal, or poor cytogenetic risk categories. These are: DANCR, PRDM16-DT, SNHG6, OIP5-AS1, SNHG16, JPX, FTX, KCNQ1OT1, TP73-AS1, and GAS5. The identification of a molecular signature based on lncRNAs has the potential for have deep clinical significance, as it could potentially help better define the evolution from low-grade MDS to high-grade MDS to AML, changing the course of therapy. This would allow clinicians to provide a more personalized, patient-tailored therapeutic approach, moving from transfusion-based therapy, as is the case for low-grade MDS, to the introduction of azacytidine-based chemotherapy or allogeneic stem cell transplantation, which is the current treatment for high-grade MDS.

Keywords: clinical impact; diagnostic tool; myeloid malignancies; non-coding RNAs; prognostic tools.

Figure 1

Granulopoiesis, the generation of myeloid cells, French-American-British (FAB) classification of AML and the specific long non-coding RNAs for each AML subtype.

Figure 2

(A) Bone marrow, May-Grunwald-Giemsa stain, 1,000x. Myelodysplastic syndrome. Hyperplasic erythroid lineage, megaloblasts, oxyphilic megaloblast with sprouted nuclei, (B) myelodysplastic syndrome, dysmegakaryocytopoiesis. (C) MDS karyotype classified as intermediate risk. (D) Myeloblasts. Acute myeloid leukemia, (E) weakly differentiated myeloblasts. Acute myeloid leukemia, (F) AML karyotype classified as intermediate risk.

Figure 3

(A) Heat map comparing the expression level of lncRNAs in Poor vs. Normal risk category. (B) Library Size Analysis between TCGA samples belonging to Poor or Normal risk category. (C) Volcano plot analysis of lncRNA expression differentiating Poor vs. Normal risk category. The set FC was at 1 and p value at <0.05. (D) Table of lncRNAs with significant different expression between the Poor and Normal risk category, their fold change, average expression and p-value. (E) LncRNA-microRNA interaction network analyzing the common ceRNA activities of the lncRNAs with different expression between Poor or Normal risk category.

Figure 4

(A) Heat map comparing the expression level of lncRNAs in Favorable vs. Normal risk category. (B) Library Size Analysis between TCGA samples belonging to Favorable or Normal risk category. (C) Volcano plot analysis of lncRNA expression differentiating Favorable vs. Normal risk category. The set FC was at 1 and p value at <0.05. (D) Table of lncRNAs with significant different expression between the Favorable and Normal risk category, their fold change, average expression, and p-value. (E) LncRNA-microRNA interaction network analyzing the common ceRNA activities of the lncRNAs with different expression between Favorable or Normal risk category.

Figure 5

(A) Heat map comparing the expression level of lncRNAs in Favorable vs. Poor risk category. (B) Volcano plot analysis of lncRNA expression differentiating Poor vs. Normal risk category. The set FC was at 1 and p value at <0.05. (C) Principal component analysis (PCA) comparing the Favorable risk category vs. Poor risk category. (D) Table of lncRNAs with significant different expression between the Poor and Normal risk category, their fold change, average expression and p-value. (E) LncRNA-microRNA interaction network analyzing the common ceRNA activities of the lncRNAs with different expression between Poor or Normal risk category.

Figure 6

Violin plot of lncRNA with different expression between Normal, Favorable, and Poor risk category for PRDM16-DT, DANCR, OIP5-AS1, SNHG16 lncRNAs. ^** represents statistically significant data. ^*** represents very highly statistically significant data.

Figure 7

Violin plot of lncRNA with different expression between Normal, Favorable, and Poor risk category for SNHG16, JPX, FTX, and KCNQ1OT1 lncRNAs. ^* represents statistically significant data. ^** represents very highly statistically significant data.

Figure 8

Violin plot of lncRNA with different expression between Normal, Favorable, and Poor risk category for TP73-AS1 and GAS5 lncRNAs.