Modulation of LIN28B/Let-7 Signaling by Propranolol Contributes to Infantile Hemangioma Involution
Bibliographic record
Abstract
OBJECTIVE: Infantile hemangiomas (IHs) are the most common benign vascular neoplasms of infancy, characterized by a rapid growth phase followed by a spontaneous involution, or triggered by propranolol treatment by poorly understood mechanisms. LIN28/let-7 axis plays a central role in the regulation of stem cell self-renewal and tumorigenesis. However, the role of LIN28B/let-7 signaling in IH pathogenesis has not yet been elucidated. APPROACH AND RESULTS: LIN28B is highly expressed in proliferative IH and is less expressed in involuted and in propranolol-treated IH samples as measured by immunofluorescence staining and quantitative RT-PCR. Small RNA sequencing analysis of IH samples revealed a decrease in microRNAs that target LIN28B, including let-7, and an increase in microRNAs in the mir-498(46) cistron. Overexpression of LIN28B in HEK293 cells induced the expression of miR-516b in the mir-498(46) cistron. Propranolol treatment of induced pluripotent stem cells, which express mir-498(46) endogenously, reduced the expression of both LIN28B and mir-498(46) and increased the expression of let-7. Furthermore, propranolol treatment reduced the proliferation of induced pluripotent stem cells and induced epithelial-mesenchymal transition. CONCLUSIONS: This work uncovers the role of the LIN28B/let-7 switch in IH pathogenesis and provides a novel mechanism by which propranolol induces IH involution. Furthermore, it provides therapeutic implications for cancers in which the LIN28/let-7 pathway is imbalanced.
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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 | 0.001 |
| 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".