Characterization of Differential Gene Expression in Adrenocortical Tumors Harboring β-Catenin (CTNNB1) Mutations
Bibliographic record
Abstract
BACKGROUND: Mutations of β-catenin gene (CTNNB1) are frequent in adrenocortical adenomas (AA) and adrenocortical carcinomas (ACC). However, the target genes of β-catenin have not yet been identified in adrenocortical tumors. OBJECTIVE: Our objective was to identify genes deregulated in adrenocortical tumors harboring CTNNB1 genetic alterations and nuclear accumulation of β-catenin. METHODS: Microarray analysis identified a dataset of genes that were differently expressed between AA with CTNNB1 mutations and wild-type (WT) tumors. Within this dataset, the expression profiles of five genes were validated by real time-PCR (RT-PCR) in a cohort of 34 adrenocortical tissues (six AA and one ACC with CTNNB1 mutations, 13 AA and four ACC with WT CTNNB1, and 10 normal adrenal glands) and two human ACC cell lines. We then studied the effects of suppressing β-catenin transcriptional activity with the T-cell factor/β-catenin inhibitors PKF115-584 and PNU74654 on gene expression in H295R and SW13 cells. RESULTS: RT-PCR analysis confirmed the overexpression of ISM1, RALBP1, and PDE2A and the down-regulation of PHYHIP in five of six AA harboring CTNNB1 mutations compared with WT AA (n = 13) and normal adrenal glands (n = 10). RALBP1 and PDE2A overexpression was also confirmed at the protein level by Western blotting analysis in mutated tumors. ENC1 was specifically overexpressed in three of three AA harboring CTNNB1 point mutations. mRNA expression and protein levels of RALBP1, PDE2A, and ENC1 were decreased in a dose-dependent manner in H295R cells after treatment with PKF115-584 or PNU74654. CONCLUSION: This study identified candidate genes deregulated in CTNNB1-mutated adrenocortical tumors that may lead to a better understanding of the role of the Wnt-β-catenin pathway in adrenocortical tumorigenesis.
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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".