The MiTF/TFE Family of Transcription Factors: Master Regulators of Organelle Signaling, Metabolism, and Stress Adaptation
Bibliographic record
Abstract
Abstract The microphthalmia family (MITF, TFEB, TFE3, and TFEC) of transcription factors is emerging as global regulators of cancer cell survival and energy metabolism, both through the promotion of lysosomal genes as well as newly characterized targets, such as oxidative metabolism and the oxidative stress response. In addition, MiT/TFE factors can regulate lysosomal signaling, which includes the mTORC1 and Wnt/β-catenin pathways, which are both substantial contributors to oncogenic signaling. This review describes recent discoveries in MiT/TFE research and how they impact multiple cancer subtypes. Furthermore, the literature relating to TFE-fusion proteins in cancers and the potential mechanisms through which these genomic rearrangements promote tumorigenesis is reviewed. Likewise, the emerging function of the Folliculin (FLCN) tumor suppressor in negatively regulating the MiT/TFE family and how loss of this pathway promotes cancer is examined. Recent reports are also presented that relate to the role of MiT/TFE–driven lysosomal biogenesis in sustaining cancer cell metabolism and signaling in nutrient-limiting conditions. Finally, a discussion is provided on the future directions and unanswered questions in the field. In summary, the research surrounding the MiT/TFE family indicates that these transcription factors are promising therapeutic targets and biomarkers for cancers that thrive in stressful niches. Mol Cancer Res; 15(12); 1637–43. ©2017 AACR.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".