Transcript level of <i>AKR1C3</i> is down-regulated in gastric cancer
Bibliographic record
Abstract
Steroid hormones have been shown to play a role in gastric carcinogenesis. Large amounts of steroid hormones are locally produced in the peripheral tissues of both genders. Type 5 of 17β-hydroxysteroid dehydrogenase, encoded by the AKR1C3 gene, plays a pivotal role in both androgen and estrogen metabolism, and its expression was found to be deregulated in different cancers. In this study we measured AKR1C3 transcript and protein levels in nontumoral and primary tumoral gastric tissues, and evaluated their association with some clinicopathological features of gastric cancer (GC). We found decreased levels of AKR1C3 transcript (p < 0.0001) and protein (p = 0.0021) in GC tissues compared with the adjacent, apparently histopathologically normal, mucosa. Lower levels of AKR1C3 transcript were observed in diffuse and intestinal types of GC, whereas AKR1C3 protein levels were decreased in tumors with multisite localization, in diffuse histological type, T3, T4, and G3 grades. We also determined the effect of the histone deacetylase inhibitor sodium butyrate (NaBu) on AKR1C3 expression in EPG 85-257 and HGC-27 GC cell lines. We found that NaBu elevates the levels of both AKR1C3 transcript and protein in the cell lines we investigated. Together, our results suggest that decreased expression of AKR1C3 may be involved in development of GC and can be restored by NaBu.
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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 | 0.001 |
| 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".