ETS Family Transcription Factors in Gastric Cancer and the Role of ELF3 in the Core Metaplasia Transcription Factor Network
Bibliographic record
Abstract
Background: E26 transformation-specific (ETS) family transcription factors have confirmed roles in several types of cancers. This study aimed to clarify the role of ETS family transcription factor alterations in gastric cancers. Methods: This study examines molecular alterations of ETS transcription factors in gastric adenocarcinomas based on an analysis of publicly available cohorts from the Protein Atlas and the Cancer Genome Atlas. The expression and relationships of members of the ETS transcription factor family with other important factors in the process of gastric carcinogenesis were evaluated using the same resources. Results: mRNA expression levels of ETS family members in gastric carcinoma tissues were variable, with ELF3, ETS2, EHF, ERF, and ELF1 being the family members with the highest expression. Mutations in individual transcription factors of the ETS family were rare in gastric cancers. The family member ELF3 was well expressed in the mRNA level in a subset of gastric cancers (n = 91), and its expression correlated with the expression of other transcription factors involved in gastric cancer pathogenesis, including HNF4A, HNF1A, CDX2, GATA4, GATA6, and EHF. Cancers with high co-expression of ELF3 and HNF4A were frequently chromosomally instability (CIN), intestinal-type adenocarcinomas, and harbored TP53 mutations and WWOX deletions. Conclusion: Expression of E74 like ETS transcription factor 3 (ELF3), an ETS transcription family member, correlates with expression of other key factors in gastric cancer and confers specific characteristics that may become exploited in targeted therapeutic interventions.
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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 | 0.001 |
| 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".