The Prognostic and Therapeutic Potential of Fragile X Mental Retardation 1 (FMR1) Gene Expression in Prostate Adenocarcinoma: Insights into Survival Outcomes and Oncogenic Pathway Modulation
Bibliographic record
Abstract
Prostate adenocarcinoma (PRAD) is the second most common tumor associated with death. The role and mechanisms of the fragile X mental retardation 1 (FMR1) gene in PRAD remain unknown. We conducted an analysis of FMR1 expression in PRAD to determine its prognostic importance and connection to carcinogenic pathways such as PI3K_AKT_mTOR. Survival analyses were utilized to establish a correlation between FMR1 expression and patient outcomes. We used the integration of genomic data with bioinformatic predictions to predict the regulatory factors of the FMR1 gene in PRAD. Our data revealed that individuals with higher levels of FMR1 expression experience worse survival outcomes compared to those with lower expression (hazard ratio [HR] = 5.08, 95% confidence interval [CI] = 1.07 – 24, p = 0.0412). FMR1 expression was significantly higher in patients with advanced pathological tumor stages, particularly in the pT3 and pT4 combined stages and the pN1 nodal stage. Furthermore, patients with high Gleason scores (GSs) (combined GSs 8 and 9) exhibited increased levels of FMR1 expression. Our results further identify a possible regulatory link between FMR1 and key oncogenic pathways, including PI3K_AKT_mTOR, and predict the possible mechanism by which FMR1 is regulated in PRAD. Our data suggest that the FMR1 gene could serve as a biomarker for PRAD progression. However, in-depth investigations, including those with large patient samples and in vitro studies, are needed to validate this finding and understand the mechanisms involved.
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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 | 0.001 | 0.001 |
| 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".