MyoD1 promotes the transcription of BIK and plays an apoptosis-promoting role in the development of gastric cancer
Bibliographic record
Abstract
Myogenic differentiation (MyoD) 1, which is known as a pivotal transcription factor during myogenesis, has been proven dysregulated in several cancers. However, litter is known about the precise role and downstream genes of MyoD1 in gastric cancer (GC) cells. Here, we report that MyoD1 is lowly expressed in primary GC tissues and cells. In our experiments, overexpression of MyoD1 inhibited cell proliferation. Downstream genes of MyoD1 regulation were investigated using RNA-Seq. As a result, 138 up-regulated genes and 20 down-regulated genes and 27 up-regulated lncRNAs and 20 down-regulated lncRNAs were identified in MyoD1 overexpressed MKN-45 cells, which participated in epithelial cell signaling in Helicobacter pylori infection, glycosaminoglycan biosynthesis (keratan sulfate), notch signaling pathway, and others. Among these genes, BIK was directly regulated by MyoD1 in GC cells and inhibited cancer cell proliferation. The BIK knockdown rescued the effects of MyoD1 overexpression on GC cells. In conclusion, MyoD1 inhibited cell proliferation via 158 genes and 47 lncRNAs downstream directly or indirectly that participated in multiple signaling pathways in GC, and among these, MyoD1 promotes BIK transcription by binding to its promoter, then promotes BIK-Bcl2-caspase 3 axis and regulates GC cell apoptosis.
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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) | 0.002 | 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".