Exploring the pathogenesis of renal cell carcinoma: pathway and bioinformatics analysis of dysregulated genes and proteins
Bibliographic record
Abstract
We recently identified a group of proteins which are dysregulated in renal cell carcinoma (RCC). In this study, we performed bioinformatics and pathway analysis of these proteins. Proteins were mapped to gene ontology biological processes. The upregulated proteins tend to cluster in processes, such as cancer initiation and progression. In addition, we identified a number of pathways that are significantly enriched in RCC. Some of these are 'common' pathways which are dysregulated in many cancers, but we also identified a number of pathways which were not previously linked to RCC. In addition to their potential prognostic values, many of these pathways have a potential as therapeutic targets for RCC. To verify our findings, we compared our proteins to a pool of datasets from published reports. Although there were only a minimal number of common proteins, there was a significant overlap between the identified pathways in the two groups. Moreover, out of 16 individually discovered genes identified by a literature search, 10 were found to be related to our dysregulated pathways. We also verified the upregulation of the mammalian target of rapamycin signaling pathway in RCC by immunohistochemistry. Finally, we highlight the potential clinical applications of pathway analysis in kidney cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".