Biomarkers in Renal Cell Carcinoma: A Systematic Review and Immunohistochemical Validation Study
Bibliographic record
Abstract
Background and Objectives: The worldwide incidence of renal cell carcinoma (RCC) rose by 22% between 2012 and 2022. In Australia, RCC accounted for 2.8% of all cancer diagnoses and contributing to 1.8% of cancer-related deaths. Identification of RCC biomarkers may aid in diagnosis and management. Methods: A systematic review of immunohistochemical markers of RCC studies published between 1990 and 2019 was undertaken to select candidate biomarkers of RCC. Immunohistochemical staining of 73 clear cell RCC tumors and paired normal tissue was undertaken using selected markers. Semi-quantitative and quantitative analysis of staining intensity between paired samples was undertaken to evaluate utility as potential biomarkers, using Chi-square tests and paired t-tests for analysis. As an exploratory analysis, staining intensity was also compared on clinical/demographic variables using linear and logistic regression. Results: There were 123 candidate biomarkers identified in 91 studies. Four candidate markers were selected for further investigation: aminopeptidase A (APA)/cluster of differentiation (CD)249, aminopeptidase N (APN)/CD13, gamma-glutamyl transferase (GGT), and neuron-specific enolase (NSE). APA, GGT, and APN all demonstrated reduced staining intensity in the tumor compared with normal tissue (p < 0.001 for all). NSE demonstrated a statistically significant increase in expression in tumor compared with normal tissue (p < 0.001), and this was more pronounced in patients aged >60 years (p = 0.038). Conclusions: The utility of APA, APN, and GGT as diagnostic biomarkers in clear cell RCC is limited. NSE may have some role as a biomarker for clear cell RCC, particularly among older patients; however, further investigation is required.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".