LC3-mediated fibronectin mRNA translation induces fibrosarcoma growth by increasing connective tissue growth factor
Bibliographic record
Abstract
Previously, we related fibronectin (Fn1) mRNA translation to an interaction between an AU-rich element in the Fn1 3' UTR and light chain 3 (LC3) of microtubule-associated proteins 1A and 1B. Since human fibrosarcoma (HT1080) cells produce little fibronectin and LC3, we used these cells to investigate how LC3-mediated Fn1 mRNA translation might alter tumor growth. Transfection of HT1080 cells with LC3 enhanced fibronectin mRNA translation. Using polysome analysis and RNA-binding assays, we show that elevated levels of translation depend on an interaction between a triple arginine motif in LC3 and the AU-rich element in Fn1 mRNA. Wild-type but not mutant LC3 accelerated HT1080 cell growth in culture and when implanted in SCID mice. Comparison of WT LC3 with vector-transfected HT1080 cells revealed increased fibronectin-dependent proliferation, adhesion and invasion. Microarray analysis of genes differentially expressed in WT and vector-transfected control cells indicated enhanced expression of connective tissue growth factor (CTGF). Using siRNA, we show that enhanced expression of CTGF is fibronectin dependent and that LC3-mediated adhesion, invasion and proliferation are CTGF dependent. Expression profiling of soft tissue tumors revealed increased expression of both LC3 and CTGF in some locally invasive tumor types.
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) | 0.001 | 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".