Epigenetic Silencing of BCL2, ETS1, IL27RA and DICER1 in Low-Risk MDS Patients
Bibliographic record
Abstract
Abstract Abstract 1704 Gene expression profiling studies have been performed in MDS to better characterize these diseases. However, the molecular pathogenesis of low-risk MDS is not yet fully understood. Furthermore, the transcriptional activity is dependent on many factors including epigenetic modifications. Therefore the integration of genome-wide epigenetic regulatory marks along with gene expression levels would provide additional information regarding the biological characteristics of low-risk MDS. A total of 83 low-risk MDS patients and 36 age-matched controls were included in the study. A cohort of 18 patients with low-risk MDS and seven controls were included in a simultaneous integrative study of methylation and expression, while the whole series was used as a control group of expression data. Both the RNA and the DNA were isolated from BM mononucleate cells and hybridised with the Human Genome Expression Array (U133 Plus) from Affymetrix and MCAM Array from University Health Network (Canada), respectively. For analysis and interpretation of the hybridisation results, the R/Bioconductor program, DAVID bioinformatic resource, the web-delivered bioinformatics tool set Ingenuity Pathway Analysis and Metacore Analytical Suite were used. The results generated by expression and methylation microarrays were confirmed using Q- PCR and pyrosequencing, respectively. A total of 817 differentially methylated genes were identified as being present in low-risk MDS (p< 0.10); hyper-methylated genes (n=457) were more frequent than hypo-methylated genes (n=360). In addition, mRNA expression profiling identified 1005 genes that significantly differed between low-risk MDS and control group. Integrative analysis of the epigenetic and expression profiles revealed that 66.7% of the hyper-methylated genes were under-expressed in low-risk MDS cases. The most represented categories were regulation of apoptosis, gene expression, immune response and RNA process. BCL2, ETS1, IL27RA and DICER1, all of them hyper-methylated and down-expressed, were the most significant genes related to these functions. 1. Regarding apoptosis and BCL2, an over-expression of BCL2L11 and MYC were found in low-risk MDS. In contrast, BAX and CUX1 were under-expressed with respect to the control group. In addition, SYK gene was also hyper-methylated and under-expressed. 2. Promoter region analysis demonstrated that ETS1 transcription factor was involved in the regulation of 83 target genes included in the down-regulation signature of the low-risk MDS patients. The most significant functions of these target genes revealed that the cell-to-cell signaling and interaction pathway were prominently affected. In addition, apoptosis was identified as the function with the most number of down-regulated target genes. Therefore, the overall apoptosis pathway could be affected in low-risk MDS patients in two ways: methylation and decreased expression of BCL2 with the deregulation of related genes, as well as methylation and decreased expression of the ETS1 transcription factor with the deregulation of the apoptosis-related targets. 3. Regarding immune response, the study showed that besides IL27RA, another nine interleukins and interleukin receptors were under-expressed in the same cohort of patients: IL16, IL32, IL1RAP, IL2RB, IL6R, IL7R, IL10RA, IL10RB and IL13RA1. Three of them (IL16, IL1RAP and IL10RB) had direct genetic interactions with IL27RA. 4. Finally, the identification of DICER1 as a gene significantly altered by methylation and expression in low-risk MDS prompted us to measure the 183 miRNAs expression. A general down-regulation of miRNAs was observed in low-risk MDS cases respect to the control group (p=0.039). Our integrative analysis revealed that aberrant epigenetic regulation is a hallmark of low-risk MDS patients and could play a central role in these diseases. Furthermore, we highlight candidate DNA methylation changes associated with low-risk MDS patients. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".