Galectin-1 has potential prognostic significance and is implicated in clear cell renal cell carcinoma progression through the HIF/mTOR signaling axis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Metastatic clear cell renal cell carcinoma (ccRCC) patients have <9% 5-year survival rate, do not respond well to targeted therapy and eventually develop resistance. A better understanding of molecular pathways of RCC metastasis is the basis for the discovery of novel prognostic markers and targeted therapies. METHODS: We investigated the biological impact of galectin-1 (Gal-1) in RCC cell lines by migration and invasion assays. Effect of Gal-1 expression on the mitogen-activated protein kinase pathway was assessed by proteome array. RESULTS: Increased expression of Gal-1 increased cell migration while knocking down Gal-1 expression by siRNA resulted in reduced cellular migration (P<0.001) and invasion (P<0.05). Gal-1 overexpression increased phosphorylation of Akt, mTOR and p70 kinase. Upon hypoxia and increased HIF-1α, Gal-1 increased in a dose-dependent manner. We also found miR-22 overexpression resulted in decreased Gal-1 and HIF-1α. Immunohistochemistry analysis showed that high Gal-1 protein expression was associated with larger size tumor (P=0.034), grades III/IV tumors (P<0.001) and shorter disease-free survival (P=0.0013). Using the Cancer Genome Atlas data set, we found that high Gal-1 mRNA expression was associated with shorter overall survival (41 vs 78 months; P<0.01). CONCLUSIONS: Our data suggest Gal-1 mediates migration and invasion through the HIF-1α-mTOR signaling axis and is a potential prognostic marker and therapeutic target.
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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 | 0.000 | 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 | 0.001 |
| 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