Folliculin impairs breast tumor growth by repressing TFE3-dependent induction of the Warburg effect and angiogenesis
Bibliographic record
Abstract
Growing tumors exist in metabolically compromised environments that require activation of multiple pathways to scavenge nutrients to support accelerated rates of growth. The folliculin (FLCN) tumor suppressor complex (FLCN, FNIP1, FNIP2) is implicated in the regulation of energy homeostasis via 2 metabolic master kinases: AMPK and mTORC1. Loss-of-function mutations of the FLCN tumor suppressor complex have only been reported in renal tumors in patients with the rare Birt-Hogg-Dube syndrome. Here, we revealed that FLCN, FNIP1, and FNIP2 are downregulated in many human cancers, including poor-prognosis invasive basal-like breast carcinomas where AMPK and TFE3 targets are activated compared with the luminal, less aggressive subtypes. FLCN loss in luminal breast cancer promoted tumor growth through TFE3 activation and subsequent induction of several pathways, including autophagy, lysosomal biogenesis, aerobic glycolysis, and angiogenesis. Strikingly, induction of aerobic glycolysis and angiogenesis in FLCN-deficient cells was dictated by the activation of the PGC-1α/HIF-1α pathway, which we showed to be TFE3 dependent, directly linking TFE3 to Warburg metabolic reprogramming and angiogenesis. Conversely, FLCN overexpression in invasive basal-like breast cancer models attenuated TFE3 nuclear localization, TFE3-dependent transcriptional activity, and tumor growth. These findings support a general role of a deregulated FLCN/TFE3 tumor suppressor pathway in human cancers.
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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".