Identification of a novel differentially methylated region adjacent to <i>ATG16L2</i> in lung cancer cells using methyl-CpG binding domain protein-enriched genome sequencing
Bibliographic record
Abstract
Lung cancer is the most common cancer worldwide. Epigenetic modifications like DNA methylation play fundamental roles in the dynamic process of lung cancer. The objective of this study was to use methyl-CpG binding domain protein-enriched genome sequencing (MBD-Seq) to identify novel and high-confidence DNA methylation in lung tumor. We first compared the whole-genome DNA methylation of three lung cancer cell lines, including A549, H1299, and SK-MES-1, against BEAS-2B, a lung/bronchial normal epithelial cell line. We then used pyrosequencing and OneStep qMethyl kit methods to verify the results in the cell line specimens. MBD-Seq identified 279, 8046, and 22 887 differentially methylated regions (DMRs), respectively, with 120 common DMRs among three comparison groups. Three DMRs were consistent with the MBD-Seq results by both pyrosequencing and OneStep qMethyl validations. Furthermore, OneStep qMethyl kit was also performed for functional validation of these three potential DMRs in sputum DNA from clinical participants. We successfully identified one new DMR adjacent to ATG16L2. The novel DMR might have an important function in lung carcinogenesis. Further validation of the finding in clinical specimens of lung cancer patients and functional analysis of this novel DMR in the development of lung cancer through transcriptional silencing of ATG16L2 are warranted.
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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 | 0.000 | 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) | 0.001 | 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".