Myelodysplastic syndromes (MDS) are hematopoietic stem cell disorders with large heterogeneity at the clinical and molecular levels. As diagnostic procedures shift from bone marrow biopsies towards less invasive techniques, circulating small noncoding RNAs (sncRNAs) have become of particular interest as potential novel noninvasive biomarkers of the disease. We aimed to characterize the expression profiles of circulating sncRNAs of MDS patients and to search for specific RNAs applicable as potential biomarkers. We performed small RNA-seq in paired samples of total plasma and plasma-derived extracellular vesicles (EVs) obtained from 42 patients and 17 healthy controls and analyzed the data with respect to the stage of the disease, patient survival, response to azacitidine, mutational status, and RNA editing. Significantly higher amounts of RNA material and a striking imbalance in RNA content between plasma and EVs (more than 400 significantly deregulated sncRNAs) were found in MDS patients compared to healthy controls. Moreover, the RNA content of EV cargo was more homogeneous than that of total plasma, and different RNAs were deregulated in these two types of material. Differential expression analyses identified that many hematopoiesis-related miRNAs (e.g., miR-34a, miR-125a, and miR-150) were significantly increased in MDS and that miRNAs clustered on 14q32 were specifically increased in early MDS. Only low numbers of circulating sncRNAs were significantly associated with somatic mutations in the SF3B1 or DNMT3A genes. Survival analysis defined a signature of four sncRNAs (miR-1237-3p, U33, hsa_piR_019420, and miR-548av-5p measured in EVs) as the most significantly associated with overall survival (HR = 5.866, p < 0.001). In total plasma, we identified five circulating miRNAs (miR-423-5p, miR-126-3p, miR-151a-3p, miR-125a-5p, and miR-199a-3p) whose combined expression levels could predict the response to azacitidine treatment. In conclusion, our data demonstrate that circulating sncRNAs show specific patterns in MDS and that their expression changes during disease progression, providing a rationale for the potential clinical usefulness of circulating sncRNAs in MDS prognosis. However, monitoring sncRNA levels in total plasma or in the EV fraction does not reflect one another, instead, they seem to represent distinctive snapshots of the disease and the data should be interpreted circumspectly with respect to the type of material analyzed.

• Keywords
• biomarkers, circulating small noncoding RNAs, extracellular vesicles, myelodysplastic syndromes,
• MeSH
• Azacitidine pharmacology   MeSH
• Biomarkers blood   MeSH
• Models, Biological MeSH
• RNA Editing genetics   MeSH
• Extracellular Vesicles metabolism   MeSH
• Kaplan-Meier Estimate MeSH
• Humans MeSH
• RNA, Small Untranslated blood   genetics   MeSH
• MicroRNAs genetics   metabolism   MeSH
• Multivariate Analysis MeSH
• Mutation genetics   MeSH
• Myelodysplastic Syndromes blood   genetics   pathology   MeSH
• Prognosis MeSH
• Proportional Hazards Models MeSH
• Gene Expression Regulation MeSH
• Reproducibility of Results MeSH
• Signal Transduction genetics   MeSH
• Treatment Outcome MeSH
• High-Throughput Nucleotide Sequencing MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Azacitidine MeSH
• Biomarkers MeSH
• RNA, Small Untranslated MeSH
• MicroRNAs MeSH

The DLK1⁻DIO3 region contains a large miRNA cluster, the overexpression of which has previously been associated with myelodysplastic syndromes (MDS). To reveal whether this overexpression is epigenetically regulated, we performed an integrative analysis of miRNA/mRNA expression and DNA methylation of the regulatory sequences in the region (promoter of the MEG3 gene) in CD34+ bone marrow cells from the patients with higher-risk MDS and acute myeloid leukemia with myelodysplasia-related changes (AML-MRC), before and during hypomethylating therapy with azacytidine (AZA). Before treatment, 50% of patients showed significant miRNA/mRNA overexpression in conjunction with a diagnosis of AML-MRC. Importantly, increased level of MEG3 was associated with poor outcome. After AZA treatment, the expression levels were reduced and were closer to those seen in the healthy controls. In half of the patients, we observed significant hypermethylation in a region preceding the MEG3 gene that negatively correlated with expression. Interestingly, this hypermethylation (when found before treatment) was associated with longer progression-free survival after therapy initiation. However, neither expression nor methylation status were associated with future responsiveness to AZA treatment. In conclusion, we correlated expression and methylation changes in the DLK1⁻DIO3 region, and we propose a complex model for regulation of this region in myelodysplasia.

• Keywords
• 14q32, MEG3, acute myeloid leukemia, azacitidine, microRNA, myelodysplastic syndromes,
• Publication type
• Journal Article MeSH

BACKGROUND: Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. METHODS: We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. RESULTS: The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. CONCLUSION: Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

• MeSH
• Gene Ontology MeSH
• Metabolic Networks and Pathways genetics   MeSH
• Operon genetics   MeSH
• Prokaryotic Cells metabolism   MeSH
• Gene Expression Regulation * MeSH
• Machine Learning MeSH
• Transcription Factors genetics   metabolism   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Transcription Factors MeSH