Role of PI3K, mTOR and Akt2 signalling in hepatic tumorigenesis via the control of PKM2 expression
Bibliographic record
Abstract
To sustain increased growth, rapidly proliferating cells, such as tumour cells, undergo metabolic adaptations. In recent years, the mechanisms of glycolysis activation as a key metabolic adaptation in proliferating cells became the topic of intense research. Although this phenomenon was described more than 50 years ago by Otto Warburg, the molecular mechanisms remained elusive. Only recently, it was demonstrated that the expression of specific glycolytic enzymes, namely PKM2 (pyruvate kinase M2) and HK2 (hexokinase 2), occurs simultaneously with the glycolytic addiction of cancer cells. The PI3K (phosphoinositide 3-kinase)/mTOR [mammalian (or mechanistic) target of rapamycin] signalling pathway is a central signalling hub co-ordinating the growth in response to growth factor signalling and nutrient availability. Not surprisingly, it is found to be activated in the majority of the tumour cells. In the present article, we discuss the requirement of different PI3K/mTOR downstream effectors for the metabolic adaptation in liver cancer cells driven by this signalling pathway. We provide evidence for a selective involvement of the mTOR target Akt2 in tumoral growth. In addition, PTEN (phosphatase and tensin homologue deleted on chromosome 10)-negative human hepatocellular carcinoma cell lines display an up-regulation of PKM2 expression in an Akt2-dependent manner, providing an advantage for cell proliferation and anchorage-independent growth. Our data have implications on the link between the metabolic action of insulin signal transduction and tumorigenesis, identifying Akt2 as a potential therapeutical target in liver malignancies depending on cancer genotype.
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".