Identification of the <i>Bona fide</i> Differentially Methylated Gene Markers among Cancers
Bibliographic record
Abstract
DNA methylation plays important roles in the development of cancers. Previous studies have identified the differentially methylated sites (DMSs) between cancer and normal control. However, the methylation variations across multiple cancers have not been revealed. In this study, we identified DMSs among six human cancers (C-DMSs) and DMSs among five normal control tissues (T-DMSs). It is revealed that C-DMSs are highly overlapped with T-DMSs. By excluding the T-DMRs from C-DMRs, 4159 bona fide C-DMSs were selected as methylation variations across multiple cancers. Further analysis confirmed the roles of bona fide C-DMSs in regulation of cancer-related gene expression difference. Moreover, the genes related with these bona fide C-DMSs showed enrichment in the biological processes such as cell membrane components, cell adhesion, cell migration, immune response and cell proliferation, and also the pathways in cancer and bladder cancer. And twenty-eight genes are targeted by hsa-miR-323 which participates in tumorigenesis. In the end, we identified potential cancer-related genes by extracting protein interaction sub-network. This study provides a new framework for mining the potential cancer-specific methylation markers and oncogenes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".