Analysis of MRPL23 protein expression and its role in prostate cancer pathogenesis
Bibliographic record
Abstract
Prostate cancer (PCa) is the fourth most commonly diagnosed malignancy worldwide and remains a major clinical challenge due to its heterogeneous course and lack of reliable prognostic biomarkers. Mitochondrial ribosomal protein L23 (MRPL23) has recently emerged as a potential contributor to cancer progression, but its role in prostate cancer remains poorly understood. Formalin-fixed, paraffin-embedded (FFPE) tissue samples from 67 PCa patients who underwent radical prostatectomy were analyzed. MRPL23 expression was assessed by immunohistochemistry using a semi-quantitative immunoreactive scale (IRS). Clinicopathological data were collected for correlation analysis. Survival outcomes were evaluated using Kaplan-Meier curves and Cox proportional hazards models. MRPL23 expression differed significantly across all tissue types, with higher levels in prostate cancer tissues compared with normal epithelium, and the highest expression observed in lymph node metastases (P < .001). High MRPL23 expression was associated with shorter overall survival (P = .003) and remained an independent prognostic factor in the multivariate analysis (HR 3.99, 95% CI 1.63-9.77, P = .002). Complementary TCGA analysis confirmed elevated MRPL23 mRNA levels in prostate adenocarcinomas compared with normal tissues (P = .01) and demonstrated that high expression predicted shorter disease-free survival (10-year DFS: 75.98% versus 92.92%, log-rank P = .01). MRPL23 is a potential prognostic biomarker in prostate cancer, linked to aggressive tumor behavior and poor outcomes. Its expression in metastatic tissue suggests a role in disease progression, while TCGA data confirm its prognostic value for recurrence risk. MRPL23 may also serve as a therapeutic target in advanced PCa.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".