Synergistic inhibitory effects of 5-aza-2′-deoxycytidine and cisplatin on urothelial carcinoma growth via suppression of TGFBI-MAPK signaling pathways
Bibliographic record
Abstract
This study aimed to reveal the gene transcriptional alterations, possible molecular mechanisms, and pathways involved in the synergy of 5-aza-2′-deoxycytidine (DAC) and Cisplatin (CDDP) in urothelial carcinoma (UC). Two UC cell lines, 5637 and T24, were used in the present study. A cDNA microarray was performed to identify critical genes involved in the synergistic mechanism of both agents against UC cells. The results showed that several key regulatory genes, such as interleukin 24 (IL24), fibroblast growth factor 1 (FGF1), and transforming growth factor beta induced (TGFBI), may play critical roles in the synergy of DAC and CDDP in UC. Pathway enrichment suggested that many carcinogenesis-related pathways, such as the ECM-receptor interaction and MAPK signaling pathways, may participate in the synergy of both agents. Our results suggest that TGF-β1 stimulates the phosphorylation of ERK1/2 and p38 by increasing TGFBI expression, and that the TGFBI-MAPK signaling pathway plays an important role in the synergy of DAC and CDDP against UC. Therefore, we revealed the synergistic mechanism of DAC and CDDP in UC. Several key regulatory genes play critical roles in the synergy of combined treatment, and the TGFBI-MAPK signaling pathway may be an important potential target of these two agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".