Expression of hypoxia-inducible factors in clear-cell renal cell carcinoma tumors of adults with and without obstructive sleep apnea
Bibliographic record
Abstract
INTRODUCTION: Upregulation of hypoxia-inducible factors (HIF) is an important pathologic feature shared by clear-cell renal cell carcinoma (ccRCC) and obstructive sleep apnea (OSA). It is unclear whether OSA alters ccRCC pathogenesis via HIF expression. This study aimed to characterize differences in HIF expression in ccRCC tumors in patients with and without OSA. We hypothesized that a diagnosis of OSA was associated with increased HIF expression. METHODS: A cohort of adults who underwent nephrectomy for ccRCC was identified. OSA diagnosis was determined with preoperative STOP-BANG scores or polysomnography, selecting 20 individuals with and 20 without OSA. Tumor sections were immunohistochemically stained for HIF-1α & HIF-2α and assessed by an expert uropathologist. RESULTS: The OSA group exhibited a higher prevalence of hypertension (95% vs. 50%, p=0.001) and greater median body mass index (BMI) (34.8 vs. 29.05, p=0.006). Tumor grades were higher in the OSA group (p=0.039). No differences were noted in tumor stages. Samples of ccRCC tumors in the OSA group demonstrated a higher prevalence of HIF-1α positivity (80% vs. 50%, p=0.048), although median histoscores were not different (4 vs. 2.5, p=0.260). Neither median HIF-2α histoscores (1 vs. 2, p=0.306) nor expression (histoscore >0; 74% vs. 75%, p=0.927) was statistically significant. CONCLUSIONS: In OSA patients, ccRCC tumors exhibited higher HIF-1α positivity and tumor grades; however, no significant differences in median HIF histoscores, HIF-2α expression, or tumor stage were found. Future studies can use our results to perform formal sample size calculations and elucidate the role of OSA in the pathogenesis of ccRCC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".