Patterns of Expression of H<sub>2</sub>S-Producing Enzyme in Human Renal Cell Carcinoma Specimens: Potential Avenue for Future Therapeutics
Bibliographic record
Abstract
Background: Renal cell carcinoma (RCC) is the most common cancer of the kidney. The most common histotype is clear-cell (cc) RCC. Hydrogen sulfide (H2S) is an angiogenic and anti-apoptotic gasotransmitter that is elevated under pseudohypoxic conditions. H2S is endogenously produced by three enzymes: Cystathionine γ-lyase (CSE), cystathionine β-synthase (CBS), and 3-mercaptopyruvate sulfurtransferase (MPST). Seeing as increased expression of these enzymes has been observed in other human cancer types, this study aimed to quantify H2S-producing enzyme expression in human RCC samples and evaluate whether it correlated with clinical outcomes. Patients and Methods: Eighty-eight human kidney tissue specimens, with healthy and cancerous tissue components, were immunohistochemically stained for CSE, CBS, and MPST. The mean pixel intensity of positively stained areas was quantified. A retrospective analysis was conducted to obtain patient demographics, rates of metastasis/recurrence, and prognostic characteristics. Statistical correlations between enzyme expressions and subsequent patient outcomes were evaluated. Results: There was significantly greater expression of CSE, CBS, and MPST in cc-RCC compared to paired healthy tissue (p<0.0001). The difference in expression of CSE in cancerous versus normal tissue was significantly greater than that for CBS and MPST (p<0.0001 and p<0.01, respectively). Enzyme expression patterns in cancerous versus normal tissue did not correlate with nuclear grade, stage, histological type or cancer recurrence/metastasis. Conclusion: To our knowledge, this is the first report of the differential increase in expression of CSE, CBS, and MPST in human RCC. Although these patterns do not appear to correlate with cancer recurrence, metastasis, size or nuclear grade, their differential increase suggests a potential therapeutic target.
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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 | 0.000 |
| 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 | 0.000 | 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".