Integrative bioinformatical analysis of clear cell renal cell carcinoma (1138.10)
Bibliographic record
Abstract
Background. Clear cell renal cell carcinoma (ccRCC) is the most common adult neoplasm of the kidney. Better understanding of the molecular pathogenesis may lead to new therapeutic options. Aim. Our aim was to assemble transcriptomic, proteomic and miRNA data using an integrative approach. Methods. Available mRNA, miRNA and protein data were collected from 593 ccRCC and 389 normal kidneys. We performed pathway analysis. TCGA database and immunohistochenmitry were used for validation. Functional analyses were undertaken on cell line model. Results. Metabolic processes were the most significant signalling pathways, and we validated Aryl‐Hydrocarbon Receptor (AHR) signalling to be involved in tumorigenesis. Using network analysis, we identified GRHL2 as diagnostic marker and drug target. KIAA0101 was found to enhance tumor cell migration and invasion. We also demonstrated that KIAA0101 overexpression correlates with poor prognosis and may represent a diagnostic marker. We identified miR‐139‐5p as the miRNA mostly affecting the network targeting ZEB1, TCF4, ETS1 and CCND2. Conclusions. Using integrative bioinformatical analyses we identified the most characteristic pathways and molecules. Some of them may be applicable as biomarkers or new, potential drug targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".