XB130, a New Adaptor Protein, Regulates Expression of Tumor Suppressive MicroRNAs in Cancer Cells
Bibliographic record
Abstract
XB130, a novel adaptor protein, promotes cell growth by controlling expression of many related genes. MicroRNAs (miRNAs), which are frequently mis-expressed in cancer cells, regulate expression of targeted genes. In this present study, we aimed to explore the oncogenic mechanism of XB130 through miRNAs regulation. We analyzed miRNA expression in XB130 short hairpin RNA (shRNA) stably transfected WRO thyroid cancer cells by a miRNA array assay, and 16 miRNAs were up-regulated and 22 miRNAs were down-regulated significantly in these cells, in comparison with non-transfected or negative control shRNA transfected cells. We chose three of the up-regulated miRNAs (miR-33a, miR-149 and miR-193a-3p) and validated them by real-time qRT-PCR. Ectopic overexpression of XB130 suppressed these 3 miRNAs in MRO cells, a cell line with very low expression of XB130. Furthermore, we transfected miR mimics of these 3 miRNAs into WRO cells. They negatively regulated expression of oncogenes (miR-33a: MYC, miR-149: FOSL1, miR-193a-3p: SLC7A5), by targeting their 3' untranslated region, and reduced cell growth. Our results suggest that XB130 could promote growth of cancer cells by regulating expression of tumor suppressive miRNAs and their targeted genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".