RTN4 Knockdown Dysregulates the AKT Pathway, Destabilizes the Cytoskeleton, and Enhances Paclitaxel-Induced Cytotoxicity in Cancers
Bibliographic record
Abstract
Reticulon-4 (RTN4), commonly known as a neurite outgrowth inhibitor (Nogo), is emerging as an important player in human cancers. Clinically, we found lower RTN4 expression in patient-derived tumors was associated with significantly better survival in lung, breast, cervical, and renal cancer patients. To identify the role of RTN4 in cancer biology, we performed mass spectrometry-based quantitative proteomic analysis on cancer cells following RTN4 knockdown and found its link with pro-survival as well as cytoskeleton-related processes. Subsequent mechanistic investigations revealed that RTN4 regulates lipid homeostasis, AKT signaling, and cytoskeleton modulation. In particular, downregulation of RTN4 reduced sphingomyelin synthesis and impaired plasma membrane localization of AKT, wherein AKT phosphorylation, involved in many cancers, was significantly reduced without any comparable effect on AKT-related upstream kinases, in a sphingolipid-dependent manner. Furthermore, knockdown of RTN4 retarded proliferation of cancer cells in vitro as well as tumor xenografts in mice. Finally, RTN4 knockdown affected tubulin stability and promoted higher cytotoxic effects with chemotherapeutic paclitaxel in cancer cells both in vitro and in vivo. In summary, RTN4 is involved in carcinogenesis and represents a molecular candidate that may be targeted to achieve desired antitumor effects in clinics.
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) | 0.000 | 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) | 0.003 | 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".