Dysregulation of the MiR-449b target TGFBI alters the TGFβ pathway to induce cisplatin resistance in nasopharyngeal carcinoma
Bibliographic record
Abstract
Despite the improvement in locoregional control of nasopharyngeal carcinoma (NPC), distant metastasis (DM), and chemoresistance persist as major causes of mortality. This study identified a novel role for miR-449b, an overexpressed gene in a validated four-miRNA signature for NPC DM, leading to chemoresistance via the direct targeting of transforming growth factor beta-induced (TGFBI). In vitro shRNA-mediated downregulation of TGFBI induced phosphorylation of PTEN and AKT, increasing cisplatin resistance. Conversely, the overexpression of TGFBI sensitized the NPC cells to cisplatin. In NPC patients treated with concurrent chemoradiotherapy (CRT), the overall survival (OS) was significantly inversely correlated with miR-449b, and directly correlated with both TGFBI mRNA and protein expression, as assessed by RNA sequencing and immunohistochemistry (IHC). Mechanistically, co-immunoprecipitation demonstrated that TGFBI competes with pro-TGFβ1 for integrin receptor binding. Decreased TGFBI led to increased pro-TGFβ1 activation and TGFβ1 canonical/noncanonical pathway-induced cisplatin resistance. Thus, overexpression of miR-449b decreases TGFBI, thereby altering the balance between TGFBI and pro-TGFβ1, revealing a novel mechanism of chemoresistance in NPC.
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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".