Risk Prediction Tool for Aggressive Tumors in Clinical T1 Stage Clear Cell Renal Cell Carcinoma Using Molecular Biomarkers
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Bibliographic record
Abstract
Some early-stage clear cell renal cell carcinomas (ccRCCs) of ≤7 cm are associated with a poor clinical outcome. In this study, we investigated molecular biomarkers associated with aggressive clinical T1 stage ccRCCs of ≤7 cm, which were used to develop a risk prediction tool toward guiding the decision of treatment. Among 1069 nephrectomies performed for ccRCC of ≤7 cm conducted between January 2008 and December 2014, 177 cases with available formalin-fixed paraffin-embedded tissue were evaluated. An aggressive tumor was defined as a tumor exhibiting synchronous metastasis, recurrence, or leading to cancer-specific death. Expression levels of six genes (FOXC2, CLIP4, PBRM1, BAP1, SETD2, and KDM5C) were measured by reverse-transcription polymerase chain reaction (qRT-PCR) and their relation to clinical outcomes was investigated. Immunohistochemistry was performed to validate the expression profiles of selected genes significantly associated with clinical outcomes in multivariate analysis. Using these genes, we developed a prediction model of aggressive ccRCC based on logistic regression and deep-learning methods. FOXC2, PBRM1, and BAP1 expression levels were significantly lower in aggressive ccRCC than non-aggressive ccRCC both in univariate and multivariate analysis. The immunohistochemistry result demonstrated the significant downregulation of FOXC2, PBRM1, and BAP1 expression in aggressive ccRCC. Adding immunohistochemical staining results to qRT-PCR, the aggressive ccRCC prediction models had the area under the curve (AUC) of 0.760 and 0.796 and accuracy of 0.759 and 0.852 using the logistic regression method and deep-learning method, respectively. Use of these biomarkers and the developed prediction model can help stratify patients with clinical T1 stage ccRCC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it