Genomic DNA methylation analysis reveals that BLNK is a key potential gene in the regulation of autophagy-related thyroid cancer progression
Bibliographic record
Abstract
The purpose of this study was to explore the relationship between autophagy and DNA methylation, and to identify key genes for autophagy-regulated thyroid cancer progression. We divided patients with thyroid cancer into high-autophagy score (AS) group and low-AS group based on their AS values. The results found that AS was associated with the distant metastasis of thyroid cancer, and adversely affected prognosis. Then, we screened 359 differently expressed genes (DEGs) with DNA methylation status consistent with gene expression change. Functional classification analysis demonstrated that the 359 DEGs consistent with DNA methylation status were significantly involved in adhesion, migration, and differentiation of immune cells. To further screen the key genes in the autophagy-related thyroid cancer progression, we constructed a protein-protein interactions (PPI) network and performed prognostic analysis. B cell linker (BLNK) was identified as the key potential gene affecting autophagy-related thyroid cancer progression. Finally, we verified that BLNK promoted the proliferation of thyroid cancer cells, and BLNK expression was regulated by DNA methylation. Our research provides a new perspective for exploring the relationship between autophagy and DNA methylation during the progression of thyroid cancer and provides a new target for the treatment of metastatic thyroid cancer.
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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".