Quantitative Proteomic Analysis in Metastatic Renal Cell Carcinoma Reveals a Unique Set of Proteins with Potential Prognostic Significance
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Bibliographic record
Abstract
Metastatic renal cell carcinoma (RCC) is one of the most treatment-resistant malignancies, and patients have a dismal prognosis, with a <10% five-year survival rate. The identification of markers that can predict the potential for metastases will have a great effect in improving patient outcomes. In this study, we used differential proteomics with isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS analysis to identify proteins that are differentially expressed in metastatic and primary RCC. We identified 1256 non-redundant proteins, and 456 of these were quantified. Further analysis identified 29 proteins that were differentially expressed (12 overexpressed and 17 underexpressed) in metastatic and primary RCC. Dysregulated protein expressions of profilin-1 (Pfn1), 14-3-3 zeta/delta (14-3-3ζ), and galectin-1 (Gal-1) were verified on two independent sets of tissues by means of Western blot and immunohistochemical analysis. Hierarchical clustering analysis showed that the protein expression profile specific for metastatic RCC can distinguish between aggressive and non-aggressive RCC. Pathway analysis showed that dysregulated proteins are involved in cellular processes related to tumor progression and metastasis. Furthermore, preliminary analysis using a small set of tumors showed that increased expression of Pfn1 is associated with poor outcome and is a potential prognostic marker in RCC. In addition, 14-3-3ζ and Gal-1 also showed higher expression in tumors with poor prognosis than in those with good prognosis. Dysregulated proteins in metastatic RCC represent potential prognostic markers for kidney cancer patients, and a greater understanding of their involved biological pathways can serve as the foundation of the development of novel targeted therapies for metastatic RCC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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