Global profiling of alternative RNA splicing events provides insights into molecular differences between various types of hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Dysregulations in alternative splicing (AS) patterns have been associated with many human diseases including cancer. In the present study, alterations to the global RNA splicing landscape of cellular genes were investigated in a large-scale screen from 377 liver tissue samples using high-throughput RNA sequencing data. RESULTS: Our study identifies modifications in the AS patterns of transcripts encoded by more than 2500 genes such as tumor suppressor genes, transcription factors, and kinases. These findings provide insights into the molecular differences between various types of hepatocellular carcinoma (HCC). Our analysis allowed the identification of 761 unique transcripts for which AS is misregulated in HBV-associated HCC, while 68 are unique to HCV-associated HCC, 54 to HBV&HCV-associated HCC, and 299 to virus-free HCC. Moreover, we demonstrate that the expression pattern of the RNA splicing factor hnRNPC in HCC tissues significantly correlates with patient survival. We also show that the expression of the HBx protein from HBV leads to modifications in the AS profiles of cellular genes. Finally, using RNA interference and a reverse transcription-PCR screening platform, we examined the implications of cellular proteins involved in the splicing of transcripts involved in apoptosis and demonstrate the potential contribution of these proteins in AS control. CONCLUSIONS: This study provides the first comprehensive portrait of global changes in the RNA splicing signatures that occur in hepatocellular carcinoma. Moreover, these data allowed us to identify unique signatures of genes for which AS is misregulated in the different types of HCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".