Deciphering the Dichotomous Effects of PGC-1α on Tumorigenesis and Metastasis
Bibliographic record
Abstract
Metabolic reprogramming confers cancer cells the ability to grow and survive under nutrient-depleted or stressful microenvironments. The amplification of oncogenes, the loss of tumor suppressors, as well as context- and lineage-specific determinants can converge and profoundly affect the metabolic status of cancer cells. Cumulating evidences suggest that highly glycolytic cells under the influence of oncogenes such as BRAF, or evolving in hypoxic microenvironments, will promote metastasis through modulation of multiple steps of tumorigenesis such as the epithelial-to-mesenchymal transition (EMT). On the contrary, increased reliance on mitochondrial respiration is associated with hyperplasic rather than metastatic disease. The PGC-1α transcriptional coactivator, a master regulator of mitochondrial biogenesis, has recently been shown to exert antimetastatic effects in cancer, notably through inhibition of EMT. Besides, PGC-1α has the opposite role in specific cancer subtypes, in which it appears to provide growth advantages. Thus, the regulation and role of PGC-1α in cancer is not univocal, and its use as a prognostic marker appears limited given its highly dynamic nature and its multifaceted regulation by transcriptional and posttranslational mechanisms. Herein, we expose key oncogenic and lineage-specific modules that finely regulate PGC-1α to promote or dampen the metastatic process. We propose a unifying model based on the systematic analysis of its controversial implication in cancer from cell proliferation to EMT and metastasis. This short review will provide a good understanding of current challenges associated with the study of PGC-1α.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".