SRRM1 coordinates an alternative splicing program that promotes expression of oncogenic protein isoforms
Bibliographic record
Abstract
Abstract The alternative splicing of the adapter protein NUMB is dysregulated in multiple cancer types, regulating its functional divergence towards either tumor suppression or oncogenesis in an isoform-dependant manner. Here we utilized a NUMB exon 9 (E9) splicing reporter in a genome-wide CRISPR screen to identify splicing regulators SRRM1 and SRSF11 that promote NUMB oncogenic splicing in colorectal, lung and breast cancer cell lines. We show that SRRM1 and SRSF11 share common protein interactors, RNA targets and effects on an oncogenic splicing program which favors the expression of pro-tumorigenic isoforms. In addition to NUMB E9, SRRM1 regulates cancer-associated splicing events in genes encoding signaling proteins, transcription factors and actin cytoskeleton regulators, many of which are also developmentally regulated, including CD44, MKNK2, ECT2, DIAPH1, KAT5, TCF7L2, FOXM1 and TBX3. Loss of SRRM1 in colorectal and lung cancer cells reduces cell proliferation, colony formation and invasion capabilities, as well as expression of tumour promoters Cyclin D1, Notum, and PRDX2. Our data indicate that SRRM1 regulation of alternative splicing represents a node to target multiple properties of malignant cells, with broad effects on cellular signaling, proliferation, EMT, apoptosis resistance and stemness.
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) | 0.002 | 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".