Identification of super-enhancer-associated transcription factors regulating glucose metabolism in poorly differentiated thyroid carcinoma
Bibliographic record
Abstract
Abstract This study aimed to uncover transcription factors that regulate super-enhancers involved in glucose metabolism reprogramming in poorly differentiated thyroid carcinoma (PDTC). TCA cycle and pyruvate metabolism were significantly enriched in PDTC. Differentially expressed genes in PDTC vs. normal control tissues were located in key steps in TCA cycle and pyruvate metabolism. A total of 23 upregulated genes localized in TCA cycle and pyruvate metabolism were identified as super-enhancer-controlled genes. Transcription factor analysis of these 23 super-enhancer-controlled genes related to glucose metabolism was performed, and 20 transcription factors were obtained, of which KLF12, ZNF281 and RELA had a significant prognostic impact. Regulatory network of KLF12, ZNF281 and RELA controlled the expression of these four prognostic target genes (LDHA, ACLY, ME2 and IDH2). In vitro validation showed that silencing of KLF12, ZNF281 and RELA suppressed proliferation, glucose uptake, lactate production and ATP level, but increased ADP/ATP ratio in PDTC cells. In conclusion, KLF12, ZNF281 and RELA were identified as the key transcription factors that regulate super-enhancer-controlled genes related to glucose metabolism in PDTC. Our findings contribute to a deeper understanding of the regulatory mechanisms associated with glucose metabolism in PDTC, and advance the theoretical development of PDTC-targeted therapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".