PGK1 contributes to tumorigenesis and sorafenib resistance of renal clear cell carcinoma via activating CXCR4/ERK signaling pathway and accelerating glycolysis
Bibliographic record
Abstract
Phosphoglycerate kinase 1 (PGK1) has complicated and multiple functions in cancer occurrence, tumor progression and drug resistance. Sorafenib is the first-line treatment targeted drug for patients with kidney renal clear cell carcinoma (KIRC) as a tyrosine kinase inhibitor, but sorafenib resistance is extremely common to retard therapy efficiency. So far, it is unclear whether and how PGK1 is involved in the pathogenesis and sorafenib resistance of KIRC. Herein, the molecular mechanisms of PGK1-mediated KIRC progression and sorafenib resistance have been explored by comprehensively integrative studies using biochemical approaches, mass spectrometry (MS) identification, microarray assay, nude mouse xenograft model and bioinformatics analysis. We have confirmed PGK1 is specifically upregulated in KIRC based on the transcriptome data generated by our own gene chip experiment, proteomics identification and the bioinformatics analysis for five online transcriptome datasets, and PGK1 upregulation in tumor tissues and serum is indicative with poor prognosis of KIRC patients. In the KIRC tissues, a high expression of PGK1 is often accompanied with an increase of glycolysis-related enzymes and CXCR4. PGK1 exhibits pro-tumorigenic properties in vitro and in a xenograft tumor model by accelerating glycolysis and inducing CXCR4-mediated phosphorylation of AKT and ERK. Moreover, PGK1 promotes sorafenib resistance via increasing CXCR4-mediated ERK phosphorylation. In conclusion, PGK1-invovled metabolic reprogramming and activation of CXCR4/ERK signaling pathway contributes to tumor growth and sorafenib resistance of KIRC.
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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 | 0.000 | 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) | 0.001 | 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".