Combining transcriptional and post-transcriptional regulation to predict somatic mutations altering the gene regulatory program in cancer cells
Bibliographic record
Abstract
While most cancer studies focused on patient variations lying in protein-coding regions, the noncoding ~98% of the genome, containing cis-regulatory regions that control when and where genes are expressed, is largely unexplored. Transcription factors are key proteins binding to cis-regulatory regions to modulate the rate of gene transcription. Delineating the specific positions at which a TF binds DNA is of high importance in deciphering gene regulation. As cancer is a disease of disrupted cellular regulation, it is critical to analyze these regions to highlight patient somatic mutations altering the gene regulatory program of the cells. I will present our recent works on the evaluation of the combined effects of transcriptional and post-transcriptional dysregulation of gene expression. Our analyses culminated with the identification of mutations at TFBSs affecting the expression of key protein-coding and miRNA genes with a cascading dysregulating effect of the cells’ regulatory program. Our predictions were enriched for protein-coding and miRNA genes previously annotated as potential cancer drivers. Functional enrichment analyses highlighted the dysregulation of key pathways associated with carcinogenesis. These results confirm that our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients. This new strategy represents an original methodology to decipher how the gene regulatory program is disrupted in cancer cells by combining transcriptional and post-transcriptional regulation of gene expression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".