The Aberrant Methylation Sites Identification and Function Analysis Associated With DNMT3A And IDH Mutations in AML
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Bibliographic record
Abstract
DNA methylation is a major epigenetic modification process. DNA methylation have played an important role in the development of disease cells and normal cells. Mutations in the genetic sequence of DNMT3A and IDH are found in many patients with acute myeloid leukemia. They lead to dysfunction of DNMT3A protein and isocitrate dehydrogenase and bad prognosis. However, the process of how they regulate DNA methylation in acute myeloid leukemia, affecting the development of the disease is not clear. Following work is conducted: In the analysis of survival, we have studied the influence of different mutations on patients’ survival, then we select DNMT3A and IDH genes as the key genes. We use JHU-USC HumanMethylation450K data of 74 AML samples downloaded from The Cancer Genome Atlas ( https://tcga-data.nci.nih.gov),together with 40 normal samples downloaded from The Gene Expression Omnibus ( http://www.ncbi.nlm.nih.gov/geo/),then through QDMR( http://bioinfo.hrbmu.edu.cn/qdmr/ ) and SAM (SAMR package is used to analyze significance of microarrays) method, 1,991 Differentially methylated sites(DMS) are screened eventually, finally these CpG sites are mapped to 1,452 genes. Outcomes from cluster analysis illustrate that there exist little differences in individuals from normal samples. Disease samples have a higher methylation proportion than normal samples. Then, we match the genome for DMS and discover that the hypermethylation inclines to a lower expression in the promoter, DNA methylation and gene expression in the sample indicate a slightly positive correlation on gene body. Functional enrichment analysis illustrates that differentially methylated genes are mostly enriched in cancer pathway and cell adhesion. This topic is based on DNA methylation to classify samples and do function analysis for DMS. It can provide the diagnosis and therapy of acute myeloid leukemia with great help.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it