mi<scp>RNA</scp>‐106a and prostate cancer radioresistance: a novel role for <scp>LITAF</scp> in <scp>ATM</scp> regulation
Why this work is in the frame
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Bibliographic record
Abstract
Recurrence of high-grade prostate cancer after radiotherapy is a significant clinical problem, resulting in increased morbidity and reduced patient survival. The molecular mechanisms of radiation resistance are being elucidated through the study of microRNA (miR) that negatively regulate gene expression. We performed bioinformatics analyses of The Cancer Genome Atlas (TCGA) dataset to evaluate the association between miR-106a and its putative target lipopolysaccharide-induced TNF-α factor (LITAF) in prostate cancer. We characterized the function of miR-106a through in vitro and in vivo experiments and employed transcriptomic analysis, western blotting, and 3'UTR luciferase assays to establish LITAF as a bona fide target of miR-106a. Using our well-characterized radiation-resistant cell lines, we identified that miR-106a was overexpressed in radiation-resistant cells compared to parental cells. In the TCGA, miR-106a was significantly elevated in high-grade human prostate tumors relative to intermediate- and low-grade specimens. An inverse correlation was seen with its target, LITAF. Furthermore, high miR-106a and low LITAF expression predict for biochemical recurrence at 5 years after radical prostatectomy. miR-106a overexpression conferred radioresistance by increasing proliferation and reducing senescence, and this was phenocopied by knockdown of LITAF. For the first time, we describe a role for miRNA in upregulating ATM expression. LITAF, not previously attributed to radiation response, mediates this interaction. This route of cancer radioresistance can be overcome using the specific ATM kinase inhibitor, KU-55933. Our research provides the first report of miR-106a and LITAF in prostate cancer radiation resistance and high-grade disease, and presents a viable therapeutic strategy that may ultimately improve patient outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it