THUMPD3 regulates alternative splicing of ECM transcripts in human lung cancer cells and promotes proliferation and migration
Bibliographic record
Abstract
RNA-modifying enzymes have recently garnered considerable attention due to their relevance in cancer biology, identifying them as potential targets for novel therapeutic intervention. THUMPD3 was recently identified as an RNA methyltransferase catalysing N2-methylguanosine (m2G) within certain tRNAs. In this study, we unveil a novel role for THUMPD3 in lung cancer cells. Depletion of the enzyme from lung cancer cells significantly impairs their fitness, negatively impacting key cellular processes such as proliferation and migration. Notably, exogenous expression of THUMPD3 in normal lung fibroblasts stimulates their proliferation rate. Additionally, transcriptome-wide analyses reveal that depletion of THUMPD3 from lung cancer cells induces substantial changes in the expression of cell surface proteins, including those comprising the extracellular matrix (ECM). We further demonstrate that THUMPD3 maintains expression of an extra-domain B (EDB) containing pro-tumour isoform of Fibronectin-1 mRNA, encoding FN1, an important ECM protein. Crucially, depletion of THUMPD3 promotes an alternative splicing event that removes the EDB-encoding exon from Fibronectin-1. This is consistent with THUMPD3 depletion reducing cellular proliferation and migration. Moreover, depletion of THUMPD3 selectively and preferentially affects the alternative splicing of ECM and cell adhesion molecule encoding transcripts, as well as those encoding neurodevelopmental proteins. Overall, these findings highlight THUMPD3 as an important player in regulating cancer-relevant alternative splicing and they provide a rationale for further investigations into THUMPD3 as a candidate target in anti-cancer therapy.
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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) | 0.002 | 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".