Galectin-1 has potential prognostic significance and is implicated in clear cell renal cell carcinoma progression through the HIF/mTOR signaling axis
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".