Differential Protein Expressions in Renal Cell Carcinoma: New Biomarker Discovery by Mass Spectrometry
Bibliographic record
Abstract
Renal cell carcinoma (RCC) is the most common neoplasm in the adult kidney. Unfortunately, there are currently no biomarkers for the diagnosis of RCC. In addition to early detection, biomarkers have a potential use for prognosis, for monitoring recurrence after treatment, and as predictive markers for treatment efficiency. In this study, we identified proteins that are dysregulated in RCC, utilizing a quantitative mass spectrometry analysis. We compared the protein expression of kidney cancer tissues to their normal counterparts from the same patient using LC-MS/MS. iTRAQ labeling permitted simultaneous quantitative analysis of four samples (cancer, normal, and two controls) by separately tagging the peptides in these samples with four cleavable mass-tags (114, 115, 116, and 117 Da). The samples were then pooled, and the tagged peptides resolved first by strong cation exchange chromatography and then by nanobore reverse phase chromatography coupled online to nanoelectrospray MS/MS. We identified a total of 937 proteins in two runs. There was a statistically significant positive correlation of the proteins identified in both runs (r(p) = 0.695, p < 0.001). Using a cutoff value of 0.67 fold for underexpression and 1.5 fold for overexpression, we identified 168 underexpressed proteins and 156 proteins that were overexpressed in RCC compared to normal tissues. These dysregulated proteins in RCC were statistically significantly different from those of transitional cell carcinoma and end-stage glomerulonephritis. We performed an in silico validation of our results using different tools and databases including Serial Analysis of Gene Expression (SAGE), UniGene EST ProfileViewer, Cancer Genome Anatomy Project, and Gene Ontology consortium analysis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".