An integrated genomic analysis of papillary renal cell carcinoma type 1 uncovers the role of focal adhesion and extracellular matrix pathways
Bibliographic record
Abstract
Papillary renal cell carcinoma (pRCC) is the second most common RCC subtype and can be further classified as type 1 (pRCC1) or 2 (pRCC2). There is currently minimal understanding of pRCC1 pathogenesis, and treatment decisions are mostly empirical. The aim of this study was to identify biological pathways that are involved in pRCC1 pathogenesis using an integrated genomic approach. By microarray analysis, we identified a number of significantly dysregulated genes and microRNAs (miRNAs) that were unique to pRCC1. Integrated bioinformatics analyses showed enrichment of the focal adhesion and extracellular matrix (ECM) pathways. We experimentally validated that many members of these pathways are dysregulated in pRCC1. We identified and experimentally validated the downregulation of miR-199a-3p in pRCC1. Using cell line models, we showed that miR-199a-3p plays an important role in pRCC1 pathogenesis. Gain of function experiments showed that miR-199a-3p overexpression significantly decreased cell proliferation (p = 0.013). We also provide evidence that miR-199a-3p regulates the expression of genes linked to the focal adhesion and ECM pathways, such as caveolin 2 (CAV2), integrin beta 8 (ITGB8), MET proto-oncogene and mammalian target of rapamycin (MTOR). Using a luciferase reporter assay, we further provide evidence that miR-199a-3p overexpression decreases the expression of MET and MTOR. Using an integrated gene/miRNA approach, we provide evidence linking miRNAs to the focal adhesion and ECM pathways in pRCC1 pathogenesis. This novel information can contribute to the development of effective targeted therapies for pRCC1, for which there is none currently available in the clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".