RBM10 promotes transformation-associated processes in small cell lung cancer and is directly regulated by RBM5
Bibliographic record
Abstract
Lung cancers are the leading cause of cancer-related deaths worldwide, with small cell lung cancer (SCLC) being the most aggressive type. At the time of diagnosis, SCLC has usually already metastasized, and an astonishing 95% of patients eventually succumb to the disease. This highlights the need for more effective SCLC screening and treatment options. Interestingly, the earliest and most frequent genetic alteration associated with lung cancers involves a lesion in the region to which the RNA binding protein RBM5 maps. We have recently shown that a decrease in RBM5 expression may be a key step in SCLC development, as RBM5 regulated many transformation-associated processes in SCLC cells. RBM5 is structurally and functionally similar to another RNA binding protein, RBM10. Both proteins have tumor-suppressor properties in a variety of cancer cell lines, and it has been suggested that RBM5 expression can influence RBM10. Due to their similarities, and the recent evidence that RBM10 is mutated in up to 21% of lung cancers, we hypothesized that RBM10 would share RBM5's tumor-suppressor properties in SCLC. Using transcriptome analysis and functional assays, we show, however, that RBM10's function was opposite to what we hypothesized; in the endogenously RBM5-null GLC20 SCLC cell line, RBM10 actually promoted cell proliferation and other transformation-associated processes. Using RNA immunoprecipitation followed by next generation sequencing (RIP-Seq) and Western blotting, we demonstrate that RBM5 post-transcriptionally regulated RBM10 expression via direct interaction with specific RBM10 splice variants. We propose a working model describing the impact of this interaction on cellular processes. Our results provide evidence that RBM10 expression, in RBM5-null tumors, may contribute to tumor growth and metastasis. Measurement of both RBM10 and RBM5 expression in clinical samples may therefore hold prognostic and/or potentially predictive value.
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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 | 0.001 |
| 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".