Alternative Splicing Changes Promoted by NOVA2 Upregulation in Endothelial Cells and Relevance for Gastric Cancer
Bibliographic record
Abstract
Angiogenesis is crucial for cancer progression. While several anti-angiogenic drugs are in use for cancer treatment, their clinical benefits are unsatisfactory. Thus, a deeper understanding of the mechanisms sustaining cancer vessel growth is fundamental to identify novel biomarkers and therapeutic targets. Alternative splicing (AS) is an essential modifier of human proteome diversity. Nevertheless, AS contribution to tumor vasculature development is poorly known. The Neuro-Oncological Ventral Antigen 2 (NOVA2) is a critical AS regulator of angiogenesis and vascular development. NOVA2 is upregulated in tumor endothelial cells (ECs) of different cancers, thus representing a potential driver of tumor blood vessel aberrancies. Here, we identified novel AS transcripts generated upon NOVA2 upregulation in ECs, suggesting a pervasive role of NOVA2 in vascular biology. In addition, we report that NOVA2 is also upregulated in ECs of gastric cancer (GC), and its expression correlates with poor overall survival of GC patients. Finally, we found that the AS of the Rap Guanine Nucleotide Exchange Factor 6 (RapGEF6), a newly identified NOVA2 target, is altered in GC patients and associated with NOVA2 expression, tumor angiogenesis, and poor patient outcome. Our findings provide a better understanding of GC biology and suggest that AS might be exploited to identify novel biomarkers and therapeutics for anti-angiogenic GC treatments.
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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) | 0.001 | 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".